#Unsupervised Learning Market

[ict-123]

Unsupervised learning is a branch of artificial intelligence that involves the training of an algorithm on unstructured data. Unstructured data is defined as data that does not have any predefined categorizations or labels. 

[sunny374940]

I will teach you all I know

So here's a little story about Emmrich learning that his daughter is a mage.

Here it is on ao3

“FIRE!” Manfred was yelling excitedly from the living room. While this would not be an unusual occurrence in itself, the sound that followed made Emmrich's blood freeze. Ellie was screaming in fear and Emmrich was out of his study and down the stairs in mere seconds, skidding to a stop in front of his daughter. She was standing there, crying, and he fell down on his knees to examine her for any signs of injury, but blessedly there was nothing to be seen. So what was-

Ah. There was a burnt patch on the rug, Manfred must have stamped the fire out.

“Manfred?” he half asked, half admonished. He had directly forbidden any unsupervised magic inside the house and yet here they were.

“NOT MANFRED,” came the answer. Well, that mystery could wait, Emmrich decided, directing his attention back to Ellie. She was shaking, tears were still streaming down her face and she looked so terrified that Emmrich's heart almost broke at the sight. He drew her into an embrace, holding her tightly and stroking her hair until the tremors finally stopped. She squirmed in his arms and he let her go.

“Elanora, what happened? Are you alright?”

“It was an accident!” she wailed, on the verge of more tears. “I don't know what happened, it just started burning!” And then she was sobbing again and Emmrich picked her up to comfort her. She rested her head on his shoulder and his mind went back to the time when she was a baby, when he and Rook carried her through countless nights just like this. And now she was six years old and such a happy and clever child. Though not so happy at the moment, it seemed.

Emmrich looked at Manfred with a raised eyebrow.

“Manfred, could you please tell me what happened here?”

“SHE'S LIKE MANFRED! LIKE YOU!”

What? Like him? Surely not… “What do you mean?”

“MAGIC!” Manfred was clapping his hands in glee.

Emmrich gaped at him. She was a mage? She was a mage! Oh, but how he looked forward to teaching her! But that was for later, now he had a scared little girl in his arms who deserved an explanation.

“Ellie, would you come down for a bit?”

She sniffled but nodded against his shoulder and he settled her on the sofa and sat down next to her. He handed her a handkerchief and she blew her nose with a honk, giggling at the sound she produced. He rolled his eyes fondly. All was right in the world when his daughter was amused by bodily functions.

“Do you understand what happened?” he asked softly.

“Noo? But I didn't want to do it, I was playing dragons with Freddy and I was burning down a village and it was just pretend, but then the rug caught fire!” She was getting agitated again and Emmrich ran a hand down her back to help her calm down.

“I am not angry with you, my dear. You are showing the first signs of being able to use magic, though you are not yet able to control it.”

Her eyes lit up. “I'm gonna be a mage? Like you?”

“Yes, you are. But there is much studying ahead of you, if you wish to master it.”

She nodded seriously. “I can do it, daddy, don't worry.”

“Of that I have no doubt.”

Right then the front door opened, heralding Rook's return from the market. Ellie took off after him immediately.

“Dad, dad!” she yelled, running up to him and he caught her in a hug.

“Hey, little bug, how was your day?“

“I burned a hole in the rug and I'm gonna be a mage like daddy!” she said proudly, pointing at the rug in question. They walked back to Emmrich and Ellie plopped herself on the sofa, examining her hands hopefully for signs of more fire coming out of them.

“Oh. Okay?” Rook looked at Emmrich, more than a little confused.

“Rook, it is marvelous! She managed to produce flame all by herself. And at such a young age!”

“Wow, that's great! Well, not the rug, I mean. And she's gonna have the best teacher right at home.”

Rook said the last part with such conviction that Emmrich blushed a little at the words. Then Rook turned back to Ellie.

“Are you gonna be a necromancer like your daddy?”

“Nope. I wanna be cool like Neve and make ice knives!” She was animated now, making woosh-woosh noises, pretending to cast magic at an invisible opponent.

Emmrich sputtered at her comment and Rook's shoulders were shaking with quiet laughter.

“Excuse me, young lady, is commanding the dead not ‘cool’ enough for you?” he asked, pretending to be offended.She patted his hand consolingly.

“Neve can teach you to make ice too, if you want to be cool, you know.”

Rook was still snickering, but Emmrich pretended not to see.

“I shall remember that, then, should I ever wish to become cool like Neve and yourself,” he said haughtily, but there was laughter in his voice he couldn't quite hide.

He raised his gaze back to Rook, who was now watching him with a warm smile.

“So there are gonna be three mages in the house now? I'm beginning to feel a little outnumbered,” Rook chuckled, taking a hold of Emmrich's hand. “But I guess I'll manage, they're family, after all.”

Emmrich smiled back at him, still as hopelessly in love with his husband as he was when they first got together. Rook tugged at his hand to bring him closer and then he kissed him in belated greeting. Emmrich reciprocated happily, until-

“Daaaad!”

Rook drew away and looked at their daughter with a grin.

“Yeah, El?”

“Ew. Take yourselves elsewhere please,” she said in such a good imitation of Emmrich's voice that they both burst out laughing.

[chartreusejelly]

More random Sean HC's I have (pt. 2) ...

He doesn't eat much. While in reform school he didn't like the food and often refused to eat. Once he was on his own, having little money and not knowing how to hunt or fish, he lived on scraps he found in trash bins or pocketed from stores or markets. Being hungry is sort of the norm for him so he often forgets to eat or sometimes goes about the day snacking on provisions but not having a real meal.

He has the dirtiest ears ever. You could probably grow potatoes in them.

He snores and talks loudly in his sleep, often waking other camp members up, but he fiercely denies this when anyone brings it up.

Arthur tried to teach Sean to hunt (and shoot) when he first came along and joined the gang (in his mid-teens). But he was far too stubborn and argumentative to learn from him, and Arthur just got frustrated and gave up.

He and John butt heads a lot, especially when they were younger. Being close in age, and with John feeling superior being older and a part of the gang longer. However, they also often got into trouble together when left unsupervised.

Like a lot of the members who joined before adulthood, Sean took to Hosea, seeing him as more of a father figure than he did Dutch. Although Sean tested his patience more than a few times, Hosea was one of the few people willing to mentor him and help straighten him out, as stubborn and defiant as he could be. Hosea is the one person who never gave up on him, although he couldn't manage to teach him to read.

As a boy in Ireland, Sean was always playing with stray animals. He tried to bring them home a few times but his father would explain that they couldn't keep a pet due to being on the run a lot. He once found a stray kitten while with the VDL gang and brought it to camp, but since other members made fun of him for being "soft" he left it in nearby barn where he thought it would have a good life.

Between the ages of 2-7 he was an extremely precious small child with an unruly red mop, freckles and big blue eyes, who garnered a lot of attention. He'd often keep his father and his comrades entertained with dirty jokes and songs (which his father taught him). This is why he loves to be the center of attention as an adult.

TW: Some angst and violence below...

He suffered symptoms of PTSD (which wouldn't have been known to him at the time) after being tortured in captivity of the bounty hunters. He remained strong throughout it all while it was happening not showing any signs weakness to the perpetrators, but he did start to lose hope of being rescued by the gang after being there for several days, which caused him great sadness at the time.

He did not witness his father's murder. Darragh, knowing he could only run from the law for so long, devised a plan and told Sean he should hide in the cellar, should anything happen. When he heard the gunshot in the night that killed Darragh, he did what his father had said, despite wanting to fight. After the body was removed from the home, Sean was found. He saw bloodstains on the bed and floor while being taken out of the home.

Because of the unsavory way he talked to the lawmen who murdered his father as well as the sheriff, he was sent to reform school rather than any other option out of their spite.

His green hat belonged to his father, along with a pocket watch which he doesn't carry with him out of fear that it will be robbed.

[augustablog]

AI, Machine Learning, Artificial Neural Networks.

This week we learnt about the above topic and my take home from it is that Artificial Intelligence (AI) enables machines to mimic human intelligence, driving innovations like speech recognition and recommendation systems. Machine Learning (ML), a subset of AI, allows computers to learn from data and improve over time.

Supervised vs. Unsupervised Learning are types of AI

Supervised Learning: Uses labeled data to train models for tasks like fraud detection and image recognition.

Unsupervised Learning: Finds patterns in unlabeled data, used for clustering and market analysis.

Artificial Neural Networks (ANNs)

ANNs mimic the human brain, processing data through interconnected layers

Input Layer: Receives raw data.

Hidden Layers: Extract features and process information.

Output Layer: Produces predictions.

Deep Learning, a subset of ML, uses deep ANNs for tasks like NLP and self-driving technology. As AI evolves, understanding these core concepts is key to leveraging its potential.

It was really quite enlightening.

[lottiesnotebook]

need me some rook!cara & anders content pls.

"your OC’s doctor/healer talking about their injuries"

So the background everyone else will need for this AU is that in my heart, the Lighthouse's Caretaker is in fact Justice keeping an eye on his scapegrace daughter.

Anders & Cara 'Rook' Hawke-Laidir, parenting, fluff

@adainesjacket | @dadrunkwriting

"I cannot believe," Cara grumbles, from the nest of blankets she's been buried in since Treviso- since Minrathous- since she had to make a choice without knowing how much the world would end because of it, "that Varric snitched to my dad that I wasn't getting back to work."

"Varric?" Anders' brow furrows. "No, Justice fetched me as soon as I heard you were injured." He smooths a hand over her forehead like she's a little girl playing sick to get out of school, the cool brush of a dreamer's touch rather than the warm, callused reality of his hands.

He usually let her get away with playing sick, then. She doesn't think Elgar'nan and Ghilan'nain will be quite as indulgent. One doesn't get to call in sick from the apocalypse, even with a cracked skull, three broken ribs and frostbite across half her body.

"Justice shouldn't snitch," she pouts, and he gives a forced laugh that does not disguise his worry:

"You sound just like your mother when you say that." His brow furrows as he unwraps her blanket pile to reveal her battered, bruised body that Lace and Bellara between them had manipulated into a night shirt. "Cracked skull, three broken ribs, frostbite… What have you been doing, to end up in this much trouble?"

"Fought a dragon," she says, attempting to sound casual, and failing. "A horrible, Blighted dragon. Probably way worse than the one in the Bone Pit you made up."

"That dragon was very real," Anders says, as he always does - this is one of their oldest games. She will pretend he's made up one of his adventures with her mother, and he'll raise source after source to refute it. "Ask your Aunt Isabela if you still don't believe me."

She blows a raspberry, then winces at the pressure it puts on her aching ribs. "Aunt Isabela would cover for you because it makes her look cooler."

Anders hisses in sympathy, presses down lightly on her ribs. "The same three you broke at Skyhold. I'm having words with that Harding girl-"

"Daddy," she whines, miserably reduced from twenty-one to twelve in a single blow. "You can't blame Lace for this one. She's the one who had to patch me up."

She'd been frantic, too, from what Cara's blurred memories showed her - so pale her freckles had stood out like stars as she tried to form a makeshift brace for her neck from the rubble of Treviso's market.

"And yet," he grumbles, "these things always happen around her. Varric too. Isabela should never have let you run off with both of them, it was asking for trouble." Cool magic flows from his fingers and she sighs in blessed relief as her ribs crack back into place.

"You said it was asking for trouble when I wanted to join the Lords in the first place," Cara pointed out.

"And you caused a diplomatic incident two years in!"

She shrugs, winces, and stops trying to shrug till he's checked her over more thoroughly. "And where do you think I learned that from?"

He sighs. "A point fairly made, which is why I can't ask why I find you in the middle of an apocalypse as soon as you're left unsupervised for five minutes."

"I'm not unsupervised!" she argues, though she realises the slur in her words is not helping. Her father's hands wrap around her skull, and there's a sickening crunch as the plates begin to reform. "I have Varric, and Lace…"

"Cara-hase…" He smooths back her hair from her face. "Look at you. If I could come here in person, I'd bring you home now. Maker knows Justice would if he could, and as for your mother…"

"I know," she sighs, letting her eyes flicker closed despite her desire to bask in the warmth of her father's closeness, even through the flimsy magic of a dream, "but I helped cause this mess, Daddy. I have to be the one to clean it up, don't I?"

Another hissing sigh through his teeth. "You didn't get that from me, did you?"

Her mind drifts back to a day long ago in Skyhold, when she'd learned the truth about their last day in Kirkwall - her mother's flight, her father's disappearance. She could have forgiven him for the Chantry, Varric had said, maybe even the lying, but he asked her to kill him, after the years she spent cleaning up after other people-

"No, Daddy," she murmurs, as sleep pulls her under, and wakefulness pulls him away from her, "You taught me that."

Healer's notes

Attending healer: Anders Laidir

Patient: Cara Hawke-Laidir

Diagnosis: Cracked skull, three broken ribs, multiple contusions, minor frostbite over 70% of the body, major frostbite in lower left leg.

Treatment: Magical healing applied to broken bones and worst contusions. I have left a tincture of embrium and Andraste's tears to be applied twice daily until the frostbite and the rest of the bruising fade. Please see the reverse of this note for the recipe.

Scout Harding, take care of my daughter. She still thinks she's immortal. I never want to find out she isn't.

[mariacallous]

Whether due to consumer backlash or an aging EV lineup, or both, Tesla sales have again seen a global plunge, this time 13 percent last quarter compared to the previous year—proof that the electric automaker hasn’t yet turned around a dismal year that saw public opinion of controversial CEO Elon Musk plummet. It could mean Tesla faces a second straight year of falling sales.

And yet: Tesla is still the world’s most valuable automaker by market capitalization, worth some $990 billion. At least some of that market confidence is likely traced to the happenings of June 22, when Tesla finally began allowing paying passengers to ride its autonomous vehicle service in Austin, Texas.

The service rollout has been fairly smooth. If the metric for success is “no crashes,” mission accomplished: There have been no public reports of crashes or fender-benders involving the robotaxis. The select few riders who have been allowed inside them have praised the service online, which for now costs just $4.20 a ride. (The price seems to be a weed joke.)

But there are plenty of caveats. For one thing, the program’s “early riders” appear to be Tesla influencers, online content creators who have financial stakes in the company or who run media businesses that tend to cheerlead for Tesla and/or electric vehicles. Tesla has not said when it will open the service to members of the public. (The company, which disbanded its PR team in October 2020, did not respond to any of WIRED’s questions.) For another, Tesla’s area of operations is notably smaller than Alphabet subsidiary Waymo’s, which began offering robotaxi service in the city through the Uber app in March.

For one more, there are plenty of humans involved in this driverless service. Tesla has a safety monitor in the front passenger seat of its robotaxis, who, according to online videos, seems poised to intervene if the technology makes a mistake. And Tesla has been less than transparent about its use of human teleoperators, who can either remotely drive or remotely assist its driverless technology. (The former is likely much safer than the latter, experts say, but Tesla hasn’t said which approach it uses.)

Missed Milestone

“Tesla has what I call the trifecta of babysitting going on right now,” says Missy Cummings, who researches autonomous vehicles at George Mason University, and has herself been the subject of Musk’s displeasure. The human contributions likely make Tesla’s service much safer, she says—something for which the automaker should be praised. In fact, keeping babysitting humans in the drivers’ seat is exactly what rivals Waymo and Zoox did in the early phases of their testing. (Waymo now offers robotaxi service in five cities; Zoox has said it will start service in Las Vegas this year.) “I want to encourage them to keep doing that,” she says.

But, for Cummings, the choice is likely evidence that Tesla is behind its competitors. “If learning to deploy a self-driving car system was grades K through 12, Tesla is in first grade,” she says. “Everything we're seeing in Texas suggests significant immaturity in self-driving operations.”

This means, too, that Tesla hasn’t hit the milestone Musk promised back in January, when he told investors that the company would launch “unsupervised full self-driving as a paid service in Austin in June … no one in the car.”

“This is a demo or test using safety drivers—it’s not an [autonomous vehicle] deployment,” says Bryant Walker Smith, a law professor at the University of South Carolina who studies autonomous vehicles. “Tesla is splashing around in the kiddie pool and everyone is asking where it’s going to place in the Olympic swim competition.”

Bloopers and Sensors

Tesla has kept quiet about many of the particulars of its technology. And it’s hard to reach definite conclusions about its tech from social media posts uploaded by riders. But some of those posts appear to show less-than-smooth rides. In one video, a robotaxi attempting to make a left turn appears to cross a double yellow line into oncoming traffic. In another, a robotaxi apparently fails to detect a UPS truck stopping and reversing to park, and the front seat safety monitor has to intervene to stop the car.

One YouTuber uploaded a video showing a robotaxi “phantom braking”—suddenly coming to a stop for no apparent reason—a phenomenon that’s also been flagged by hundreds of users of Tesla’s less-advanced Full Self-Driving (Supervised) feature and investigated by the federal government. Unlike actual self-driving technology, Full Self-Driving (Supervised) requires users to keep their eyes on the road.

The service pauses for bad weather, according to Tesla’s website. One YouTuber had their ride halted for a rainstorm; the robotaxi dropped the rider in an Austin park as the wind began to whip around them. Minutes later, according to a video, the same Robotaxi picked the creator up to continue their ride. However, contradicting the above, one poster has reported the cars perform “FLAWLESSLY” in heavy rain.

The early bloopers aren’t surprising, experts say. Full Self-Driving (Supervised) requires a human driver to intervene when needed, and it appears robotaxi is the same right now, says Philip Koopman, a professor at Carnegie Mellon University who studies autonomous vehicle safety. The slip-ups the robotaxis have made are not unlike what human drivers do on the road, he says. But autonomy’s value add is supposed to be safety, so it makes sense that the videos—and the tech’s “rough edges”—are making people nervous.

Camera Quandary

The launch has reopened public debates about a core tenet of Tesla’s technology: its use of cameras alone to perceive and “make decisions” as it drives. Musk and his company have long argued that artificial intelligence, supplemented by the data collected by cameras, is sufficient to operate a safe, driverless car. The CEO has promised that all of its cars can become autonomous without any modifications, with a simple push of updated software (though Tesla also quietly reneged on this claim). Other companies see more expensive sensors, including radar and lidar, as important validators and support. (Lidar has dramatically dropped in price; many Chinese automakers are now including the sensor on every car that they sell.)

Advances in large language models have convinced some in the auto industry that Musk’s approach is the right one. In a podcast interview published this week, Kyle Vogt, the former CEO of General Motors AV unit Cruise, argued that images from multiple vehicle-mounted cameras plus advanced models can be “really accurate.” (Vogt stepped down from Cruise after one of its driverless vehicles hit and dragged a pedestrian. The company was not transparent with regulators about the incident, a report later found.)

For Cummings, the reports out of Austin have confirmed her beliefs that cameras alone aren’t enough to operate a car autonomously. “There is no robotic system that exists that is safety critical—meaning people can die [using it]—that has ever been successful on a single sensor strain,” she says. “It's unclear why Tesla thinks that they can do what has never before been done.”

One metric that might reveal Tesla’s internal success: how quickly it expands. Musk boldly said in May that Tesla will have hundreds of thousands—and perhaps up to a million—autonomous vehicles on the road next year. The company seems motivated. According to a job posting, Tesla is hiring for additional vehicle operators, who are paid to drive cars around Austin to collect data. But, of course, Musk is no stranger to deadlines unmet.

[dearestones]

Muscle Memory (Azul Ashengrotto and Reader)

Warnings: Exploration of merfolk culture, elaboration on certain aspects of canon lore, etc. 

@lottieinlimbo Request: Hi Devin!!! Congratulations on the 750 followers :D!! Your writing is incredible, you have more than earned your success!!!! I’m here to steal the final request slot! There were a lot of cool prompts that would have been fascinating with multiple characters, it was hard to choose! But I’m thinking “why did you help me?” With Azul could be really fun, maybe something platonic? I’m really excited to see what you do with this, congrats again on 750 followers!!

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Once upon a time, you desperately wanted to be a child of the sea. 

It was a stupid thing to want, you know that now that you’re older, but as a child of a fisherman and a marine biologist, you grew up immersed in the sea. Swimming came as naturally as breathing and there were days when you refused to get out of the water. Your mother would spend hours either cataloging and tending to the wildlife off the shore or lecturing students at your local university. Your father, on the other hand, would board a boat with a few other fishermen and cast his net along the side of their boats, eager to catch fresh fish for dinner and to sell at the local market. 

If it were not for the legs that burned under the unforgiving sun, you would have been one of the merfolk. You told your mother so, and she laughed as she hastily scribbled annotations on a particularly bright student’s thesis. It wasn’t a demeaning laugh, it was more like a sound that sought to appease a precocious child like yourself. 

“If you were from the sea,” your mother had crooned in your ear, “then I wouldn’t be able to do this, would I?” 

She would tuck you deep into her chest before blowing raspberries into your cheek and tousling your hair.

“If you were a mer,” your father had told you, “I would cast nets out everyday so I can take you back home with me.” 

He would then throw you into the middle of your bed before gathering the covers and pulling the corners together to make it seem as if you were caught in a net. 

You couldn’t be a child of the sea, and even your young, naive mind knew that, so you latched onto the next best thing. 

Talking about them. 

Both your mother and father, having spent most of their childhood, adolescence, and adulthood living in and around the sea, had tales to tell of meeting merfolk. People who lived on the mainland, you found, had very few encounters with the children of the sea. Sevens, they even said that it was actually more likely for them to meet one of the fair folk visiting from Briar Valley. Your parents and quite a number of the locals had often talked to the children of the sea who inhabited the waters a few miles from the coast, but some of the small fry would frequent the shallow waters, often risking getting beached on shore. 

One of your first few encounters with merfolk happened when you were only six years old. You had long since learned how to swim (you learned when you were only nine months and since then, you begged your father to take you out to the beach whenever possible), but you were never left unsupervised. On this particular day, both your parents were free from their duties and had set up a little picnic spot quite away from the tide.

You had recently eaten a snack and because your parents were superstitious, said that you couldn't go swimming until at least thirty minutes had passed. You were six and you had no true grasp of consequences outside of your parents sternly talking to you or taking away privileges as punishment. Because of that, you were toeing the line between the water and hopping in between the craggy rocks that littered the area. You had been forced to wear special swim shoes to keep your feet safe, but even with your short sighted nature, cuts on your feet and punishment from your parents didn't make the idea of disobedience all that appealing.

You skipped down the share, sometimes slipping and tripping into the water, but you always laughed to yourself and waved back at your parents every once in a while. They kept a wary eye on you, but continued to converse in that way only adults could understand and children didn't feel like grasping.

Eventually, you wandered far enough that you couldn't see or even hear your parents anymore. That may have frightened any other child, but you grew up with the sea practically in your backyard and you were taught how to self-rescue since before you could learn how to walk.

You were safe and to a certain extent, your parents trusted you.

As you waded in and out of the water that only reached your ankles, you caught sight of a boulder jutting out of the water. It was several feet away from you, away from the remnants from the stolid presence of the beach and trespassing into the heart of the sea. Normally, with how high the tide can get, the boulder would be submerged, but today, you could see how craggy and sharp it really looked.

So, it startled you when you saw a ruddy face jut out from the other side of the boulder, lock eyes with you for a second, before shrieking and backing away.

Friend?

Was it a friend?

At that moment, you weren't thinking about your parents, if enough time had passed, or if you would look stupid with your shoes still on. When you locked eyes with a child your own age, you could only hope that they would be your friend.

You ran into the water, quickly started swimming once you were deep enough, and swam towards the other side of the boulder.

Would the child still be there?

You swam onto the other side of the boulder until you saw the child, except there was—

"You have a tail!" You could barely hold onto your excitement. It was only due to societal convention that you didn't start tugging and prodding at the brilliant appendage that dangled and swished in front of you.

"And you have legs."

You looked up to find a face both like and unlike your own.

"And it's not just a tail! I've got eight of them!" The child splashed you with one powerful swish of her tails. “And they’re called arms!”

And that's how you met your first friend from the sea.

Shelley was a native from the sea bordering the Land of the Red Dragon. Her family used to live in those warm waters until they decided to migrate towards your stretch of ocean. You were young, so you didn't quite understand what she was talking about when she mentioned migratory patterns, politics, or whatever, but you did latch onto the fact that she was lonely and had a hard time making friends.

So you offered to be her friend.

"With a human?" Shelley scoffed.

Well, sixteen years later and she was still splashing you. Both of you grew up together, often sharing aspirations and dreams with each other. Like the mermaid princess, she dreamed of going up on land and walking on her own pair of legs. It took a while, but she managed to get into an exclusive land boot camp that occurred on another island (you pouted when she told you) and was later given a regular supply of transformation potions once she was old enough to enroll into university. 

And that’s when you realized that the both of you could still work together.

You see, over the years, you began to realize that your love of merfolk wasn’t just because you wanted to befriend them, but you also wanted to get to know their culture better. Like the fae, children of the sea were notorious for keeping close to themselves, especially those who spent their formative years in the bathypelagic zones. What most land dwellers knew about their aquatic neighbors were either from the outdated accounts from the famed mermaid princess or the very few merfolk who decided to breach the surface. Most governments wanted to bridge the gap between the vastly different cultures, which meant that they often recruited both merfolk and land dwellers to facilitate and improve upon the land boot camps that were initially started by the very first royal who deemed it worthy to live on land. 

You broached the topic cautiously to Shelley when you had been busy helping her walk across the beach after a session at her land boot camp. 

It took some convincing, but the both of you decided that you would work together: one day, you would be a physical therapist to help merfolk walk upon land and Shelley would teach classes to acclimate them to land culture. 

This year, you were supposed to help merfolk native to the Coral Sea stand and walk for the first time. 

This land boot camp you had relocated to was situated on one of the small islands under the Land of Dawning’s jurisdiction. The facility was small and private, owing to the fact that it was still a government project and no one wanted to be ogled at by civilians, especially when they were learning how to be human for the first time. 

This batch of merfolk were from the mesopelagic or twilight zone of the ocean, second to the sunny euphotic zone. You had your fair share of merfolk from across all three main zones, but most participants originated from the upper layer, owing to the fact that there was an increase in land dwellers to merfolk interactions. So, it wasn’t a surprise to find less than a dozen merfolk swimming playfully in the water or waiting patiently upon rocks. 

After taking the transformation potion (each of them tailored to the specific type of sea dweller because it would be a disaster and a half giving one of the classical looking mers the dosage that would better benefit a mer modeled after an angler fish or a cecaelia), all of them would be given the chance to stand up and walk within the water. 

Years ago, when the mermaid princess made her way ashore, it was only through willpower alone that she could heave herself off the sandy beach and trudge her way into her beloved’s castle. Today’s research declared that it was better to treat merfolk with hydrotherapy first by giving them exercises that would acclimate them to using the right muscles and granting them muscle memory that would later aid them in land. The water helped with the sudden pull of gravity while also maintaining a sense of familiarity so as to not cause them undue stress. 

(It was also for the same reason it was strictly forbidden that government workers such as your supervisor and by extension, yourself, were not allowed to teach the merfolk how to walk in a pool. The chlorine contact would not cause them undue harm since they now had human skin, but mers were notoriously sensitive to dryness and that wouldn’t exactly be the best impression to make when fostering better relations across races). 

The same was said for clothing. 

Get the merfolk accustomed to walking first and then clothes. 

It was best to get them hurtling one obstacle after another instead of tackling everything human-related at once. After at least one week of hydrotherapy and lessons on human etiquette, the second week would introduce clothing and walking on land. The third week would be full time human-ing, which meant a stronger dose of transformation potion that would last three days before a refill was needed. Finally, during the fourth week, they would be given the full transformation potion and a trip to the mainland where they would tour and interact with the locals as opposed to government agents. 

A month shouldn’t be enough time to get merfolk acquainted on land, but the newcomers from the twilight zone were hardy and could take a stronger beating than their neighbors in the higher zones. They already had strong muscles and coordination; they needed only guidance and direction on how to move those muscles properly in a new body. 

And so, after you reviewed your roster of attendees, you wore a wetsuit, slathered on sunscreen in copious amounts to stave off the harmful rays of the sun, and then ventured away from the facility on land. Upon first glance, you realized that most of them were young, but you saw that there were at least two middle aged adults and one elderly mer who congregated closer to the front of their younger comrades. Carefully, you studied them; how they moved, how their tails would ripple in the sunlight, and how some of them had teeth made for slicing. 

A mixed bag of both predator and prey. 

You would have to keep an eye out for any bullying. 

Once your supervisor was in position and began detailing the history of the government facility, you carefully tuned her out, content to dig out the tray full of transformation potions from the cooler that one of the other interns had brought out of the lab. Inside, vials of cerulean and silver gleamed within the confines of pale lavender vials. To each of the stoppers, a nametag and species of merfolk was affixed. Indeed, if you remembered correctly, the concentrations of cerulean and silver allowed a brief glimpse of what the potion could do: silver to bind appendages together, turn gills into scarred skin, transform beautiful scales and hides into varying shades of brown and tan; cerulean, to split tails in two, strengthen the bones, and allow for greater lung capacity. 

Depending on the type of mer, maybe they needed more of the components that made up cerulean to cut their legs into two. Or maybe they were a species of mer that took after squids or octopi; they needed more silver. 

All of the vials were encased in a thin layer of frost: optimal temperature for quick results. 

In another cooler, there were another set of vials. For these, all of the dosages were equal. 

Pain medication in case some of the merfolk would not be able to take the pain of sloughing away their old bodies for a new one. 

(It was rarer for the merfolk native to the deeper zones to ask for the pain medications, but you had seen it happen once or twice. Knowing them, however, and knowing that there were at least a ratio of three predators to one prey, it would be best for everyone involved that no one took the medication). 

And underneath the set of pain medication, there was also another tray filled with potions meant for emergencies, but you chose not to dwell on unpleasant possibilities. 

Finally, you heard your supervisor call for you and gesture for you to walk across the boardwalk and call out the names of the attendees. Like any roster, it was labeled in alphabetical order. A’s to B’s to C’s and so on. No surprise there. 

However, what caught your attention was that the first A on your roster (the only A, actually), belonged to a young male mer. He kept most of his body underwater with only the top of his head and eyes peeking out, but at your behest, he reluctantly began to climb out. It wasn’t until you caught sight of his multiple appendages attached to the bottom of his torso that you realized that you may know his type of species.

A cecaelia.

Out of habit, you counted all eight of his arms—exactly like Shelley!—but you must have been so entranced at the idea of working with another cecaelia because the boy made an irritated grunting noise as he narrowed his eyes at you. 

You apologized, slightly bemused by his impatience before giving him the vial that corresponded to his name. You murmured the instructions that he should wait until everyone else received their dose before ingesting it. At that, the cecaelia nodded, resolute and determined in his very reaction, before he ducked below the surface of the water and swam far away from the boardwalk. 

After that brief encounter, you continued down the list of names and made sure not to spend too much time mentally cataloging the types of merfolk who were supposed to be your students. There were at least two “classical” merfolk (very human at the torso with a nondescript, but shimmering tail at the bottom), but the rest were clearly descended from various species. You could see an angler fish, a squid, and even twin eels!

Twins!

If you remembered correctly, twins were very rare within the ocean, especially the deeper you went. The fact that there were two adolescent mers who were born at the same time and still lived despite the cruel nature of the ocean, could only spell good luck. Goodness, it didn’t even matter if they were predatory merfolk! You were sure they would be a delight to have under your tutelage. 

Finally, after each of the merfolk had received their government mandated potions, your supervisor called you and the other intern to stand in five feet intervals along the boardwalk. You were at the very end of the boardwalk and you were assigned to the first four merfolk along your roster. It was, of course, the cecaelia, but there were also two classical merfolk and an angler fish. The classical merfolk huddled together, giggling as they asked you questions about how long it normally took humans to learn how to walk. Meanwhile, the two other deep sea merfolk were content to keep to themselves, but you noted that the cecaelia was hanging onto your every word. 

Now that everyone was in position, your supervisor announced that in each group, only one mer would go at a time. This was to prevent them from drowning all at the same time. 

“Okay then!” You clapped your hands to gain all of your students’ attention. “Does anyone want to volunteer? Or would you rather go by alphabetical order?”

During your time as an intern, you were more accustomed to the merfolk who hailed from the euphotic zone. More often than not, the attendees were classical mers who were already aware of land dweller cultures and it showed—they were open and friendly, often clamoring and climbing over each other to be the first. For this particular group, you were disappointed, but not at all surprised to see that your chattier students had instantly quieted and looked to each other in apprehension. Meanwhile, the cecaelia and the angler fish mers looked like they were planning on completely submerging themselves. 

You took a deep breath before coming to a decision. 

“Mr. Azul… Ashengrotto?” You nodded at the cecaelia, watching as his face seemed to grow from a light lavender gray to a light flushed blue. Nodding at him, you sat upon the boardwalk and beckoned him forward with a kind smile and a wave. “Come here. For safety purposes, I’ll ask that after you drink the transformation potion, you hold onto me or the boardwalk. It will be painful, but the shock will pass once you start kicking your legs. Do you understand?”

The cecaelia, still having yet to speak in your presence, nodded his head. 

“Do you remember the demonstration on how to tread water?”

The cecaelia furrowed his brows, perhaps remembering how both your supervisor and fellow intern had jumped into the water earlier that day. They had hung onto the wood of the boardwalk while also slightly kicking underwater to keep themselves upright. Then, they gestured for you to sit to also demonstrate for some of the more nervous mers how to hold onto your arms or legs if they needed someone to further stabilize their movement and offer comfort.

With a decisive nod, he said, "Yes, I do. May I start?"

You said a few more things that were included in protocol just so the entire group under your watch understood the consequences for not taking the exercises seriously, to comply with legal regulations, and to allow each of the mers time to relax and understand that you knew what you were doing. Finally, after about five more minutes, you nodded to the cecaelia.

In your experience, taking any sort of potion was best done by downing the entire thing. It was recommended that the merfolk don't take pauses or take small sips. Transformation was already a tricky process, it would become all the more complicated if you delivered only a small dose of the potion to your body only to take a break for too long and have certain body parts rearranging themselves without the proper dosage. Thankfully, the young mer looked to be the studious type, and above all else, cautious, because once he unstoppered the vial, he returned the seal to you and knocked back the potion as if he were thirsty.

Within seconds, his face contorted in pain and his body began to thrash as his skin began receding from the shimmering lilac and gray undertones to an almost sickly pale coloring. You had assigned spotters earlier in case the mer undertaking the potion were too wracked with pain to come to their senses, so that meant that the angler fish began to restrain the cecaelia while your other students brought both of his hands up to the boardwalk. Under your keen eye, you could see that the skin above his knuckles were bone white and that face was flushing dark blue. You took note of that: scientists were always trying to improve upon the efficacy of their potions, cosmetics being one of the criteria that was often underrepresented.

"Breathe." You kept your voice steady. "Like the breathing exercises," you reminded him. "In through your nose and out through your mouth. Slow and steady."

His thrashing continued, albeit at a more subdued pace. The angler fish and the classical mers watched patiently, but you could see that all of them were transfixed by the sight of their comrade overcoming the pain. Usually, at this point, you would often pinpoint the exact moment a mer would decide to give up and discontinue the program. It was unfortunate, but not everyone was suited to the surface.

If your suspicions were correct, one of the classic mers looked like she was about to be sick while the other two merfolk were eyeing their vials with a scrutinizing gaze.

After giving them a warning glance, to which all of them merely nodded, you focused once more on the cecaelia. His breathing had regulated and the grip he had on the boardwalk was less desperate and more out of desire to seek stability.

You bent down low, murmuring to him so as to not startle him out of how much of a good job he was doing. "Most cecaelia have a hard time adjusting to losing six of their limbs. Can you raise your right arm—" You gestured to your own arm to inform him you meant the human appendage and not what most humans would mistakenly call a tentacle. "—and spread your fingers wide?"

The cecaelia's eyes widened, his lips wobbling. Out of breath and clearly out of his element, he experimentally tried to move... something, but his mind must have been too frazzled other than to cast his gaze downward, clearly at a loss. That was okay. Shelley had felt the same. Both the pain and the shock of losing so much oneself within seconds was hard, but it was even harder trying to control and relearn how to move and be at peace with what you had left.

Another note that you would have to make in the margin of your roster. The cecaelia would need time to adjust. Within a month, that was more than fine. All of the merfolk were permitted time out of their busy schedules to practice and train their muscles.

Reaching behind you, you picked up one of the floating devices that were assigned to you. There were a series of life jackets, buoyancy paddles, and pool noodles. Some mers wanted to get acclimated to wearing human clothing (so they chose the life jackets), others wanted something that was not restraining and had the familiarity of floating driftwood (hence the paddles and noodles).

"Would you like a life jacket?"

Bright blue eyes the color of the calm ocean glared dangerously at you. Had you not had your fair share of predatory mer, you would have found yourself almost scared. However, you could understand the feeling of getting one's pride crushed, even if it was the first time gaining legs. Still, though, such anger seemed simultaneously out of place, yet right at home, on his pale face.

"One of those, please." He concentrated on his right arm, but after realizing that he was still somewhat stuck in his current mindset and still working out the connections, he glanced at the noodles that you kept at your side.

It took some maneuvering and some help from the angler fish (you saw that the cecaelia had a hard time being so close to a predator when he was now human), but you managed to get the pool noodle under his arms so that he could float without any more aid.

Satisfied that he wouldn't recklessly flip over and risk drowning himself, you called out the second name on your list.

It was going to be one long session.

.

.

.

[PART ONE HERE] [PART TWO] [PART THREE] [PART FOUR]

If you want to donate a Ko-Fi, feel free https://ko-fi.com/devintrinidad.

TWISTED WONDERLAND MASTERLIST

[artistic-pussy-power]

An Ode to the Dust and the Potholes of India 

Inside India’s most beautiful state, Kerala, is its most beautiful town: Kodanchery, my hometown of dusty streets and polluted corners. (I think if you told some Indians burning plastic is bad, they would throw their personalities into the fire just to be spiteful). 

Kodanchery is loud and, as with every town in India, filled with rickety old shops. There’s the fish shop where my brother would live if he could. I’m sure the owner would quite like him to because there’s nothing that lights up his face as much as hearing my brother’s foreign English inside his smelly shop. 

Then there’s the open market stalls my achachan limps along, browsing for fruits and vegetables. They’re gathering with fruit files, but no one seems to care. It’s nothing that can’t be fixed with a wash anyway.  

Today, my cousins are home for the weekend and working away in their father’s “teashop” I guess you could call it. The shop is small but about six tables are shoved in there anyway. Like all "teashops” in my little state, there are no windows or doors, just the open, inviting front and spluttering fans whirling away. As we sit, my aunt comes from the small kitchen with a smile. She has my brother’s egg puffs, my neyyappam and all our chai in hand already. The snacks are held in a heated shelf just steps away from us, but she plates them anyway.  

In another life, we would make the 30-minute trek home on foot and my achachan would spend the entire time talking about the importance of daily exercise. In this life, his grotesquely swollen leg will bid us to call our other uncle. My ‘papapa’ picks us up in the autorickshaw he’s had as long as I’ve known him and achachan gives the lecture as we bump along.  

A later day, I will drive along our neighbourhood with my dad, learning the frustrations of manual. We skitter along the asphalt road, avoiding the edges that lead to unsupervised pits (there are many of them) and turn left at the small “Cross N’ Church” (it sounds better in Malayalam). The forest of rubber trees that give our little neighbourhood its livelihood rise above us and drip with white. We go over roads that might make my friends in New Zealand faint at the sheer sight and laugh about the time we crossed the border between Kerala and Tamil Nadu––how the potholes and eroded asphalt disappeared in the blink of an eye. 

On the way back, I wave to the house on the hill where my Malayalam teacher lives. Her guard dogs bark at us, and in the evening, when I go over to learn my mother tongue, the aggression will startle me. In a couple of weeks, I will start to smile at their roar. Today, I think they were cute—very cute.

[jonathanmatthew]

The Complete Tech Stack for Generative AI Development in 2025

Introduction

Generative AI is redefining industries by creating content that mirrors human creativity. As we move into 2025, the development of generative AI systems requires a powerful and versatile tech stack to enable fast, efficient, and scalable solutions. This blog outlines the key technologies and tools needed for building robust generative AI models, from hardware configurations to deployment frameworks.

What is Generative AI Development?

Generative AI refers to systems capable of producing new content—whether text, images, audio, or other forms of media—based on patterns learned from data. It stands apart from traditional AI, which focuses on analyzing and classifying data. In generative AI development, the focus is on using deep learning models to generate realistic outputs. Developers build these models with the help of powerful computing resources, data, and algorithms to train the models.

What Technology is Used in the Development of Generative AI?

To build an efficient generative AI system, a variety of technologies come into play:

Neural Networks: Central to the functioning of generative AI, they mimic the way the human brain processes information.

Deep Learning Models: These models, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), enable pattern recognition and content generation.

Natural Language Processing (NLP): For text generation, NLP techniques help understand language semantics, allowing AI to create human-like text.

Machine Learning Training: The backbone of any AI system, machine learning ensures models improve as they process more data.

Why is Data Collection Essential for Generative AI Development?

Data serves as the foundation for generative AI models. Without accurate, diverse, and high-quality data, AI systems cannot generate meaningful or useful outputs. Data collection is crucial for several reasons:

Model Accuracy: The more diverse the data, the more accurate the model’s predictions will be.

Fairness: Proper data collection helps avoid biases, ensuring that the AI’s outputs are unbiased and representative.

Training Efficiency: High-quality data enables faster training and better generalization, resulting in more reliable models.

What is Generative AI and How Does it Work?

Generative AI works by learning from data to create new, similar data. For example, a generative AI model trained on thousands of images can generate new, realistic images that look like the ones in the dataset. These models use techniques like unsupervised learning or reinforcement learning to identify patterns, and then apply those patterns to generate new outputs. Key to this process is the model’s ability to learn from the data’s statistical properties without human intervention.

Why Generative AI Development is Important

The importance of generative AI development cannot be overstated. It holds the potential to significantly impact various industries, from healthcare and marketing to entertainment and education. By automating content creation and generating data-driven insights, businesses can enhance operational efficiency, improve customer experiences, and create entirely new forms of content. Moreover, it opens new doors for personalized services, allowing for custom-tailored experiences at scale.

Core Layers of a Generative AI Tech Stack

The tech stack used to build generative AI models consists of several critical components that come together to enable the system’s operation. These include compute power, frameworks, and data management tools. Let’s break down the core layers:

Compute Requirements and Hardware Configurations

Generative AI development requires significant computational power, especially for large models like GPT-4 or Stable Diffusion. Developers need to use high-performance GPUs, multi-core CPUs, and even specialized hardware like TPUs (Tensor Processing Units) to train these models efficiently. Having the right hardware ensures that the models can handle large datasets and complex algorithms.

Selecting the Right Framework: TensorFlow, PyTorch, JAX

Choosing the right framework is essential for smooth model development. Among the most popular are:

TensorFlow: Known for its flexibility and scalability, it supports both research and production workloads.

PyTorch: Valued for its user-friendly interface and dynamic computation graphs, making it ideal for rapid prototyping.

JAX: Emerging as a powerful tool for high-performance machine learning, it excels in scientific computing and automatic differentiation.

Building and Scaling Generative AI Models

Building generative AI models goes beyond creating a neural network; it requires designing scalable, efficient, and adaptable systems.

Model Architectures Supporting 2025-Scale Workloads

By 2025, AI models need to support more complex tasks. Transformers, Diffusion Models, and other advanced architectures are optimized for large-scale workloads. Developers must consider scalability and optimize the architecture to handle an increasing amount of data and compute power.

Choosing Datasets for Accuracy and Fairness

When choosing datasets, it’s essential to ensure diversity and avoid bias. Malgo excels in helping businesses select datasets that strike a balance between accuracy and fairness, ensuring that generative models provide useful and equitable results.

LLM (Large Language Models) Development Essentials

Large Language Models (LLMs) like GPT-4 have revolutionized AI, enabling highly sophisticated text generation. Developing LLMs requires careful consideration of model fine-tuning and optimization.

Fine-Tuning vs Instruction Tuning in Production

Fine-Tuning: Adjusting a pre-trained model to improve performance on specific tasks.

Instruction Tuning: Involves guiding the model with specific instructions to better align with a task, making it ideal for business applications.

Model Compression and Quantization for Faster Response

To make LLMs more efficient, model compression and quantization techniques help reduce the size of models without sacrificing their performance. This results in faster response times and lower computational costs.

AI Text Generation: Tools That Speed Up Deployment

The deployment of AI models requires tools that help scale text generation applications.

Prompt Libraries, Tokenizers, and Text Post-Processing

Using prompt libraries helps standardize input for text generation, ensuring more consistent outputs. Tokenizers break down text into manageable units, enabling more efficient processing. Finally, post-processing ensures the generated text is readable and coherent.

API-Ready Pipelines for News, Marketing, and Code

Generative AI’s ability to automate content generation is invaluable for industries like news, marketing, and software development. API-ready pipelines allow for easy integration with platforms, automating content creation at scale.

Using Stable Diffusion for Image-Based Applications

For visual AI applications, Stable Diffusion is a leading technology.

Workflows for Text-to-Image Generation at Scale

Generative AI models can now turn text prompts into high-quality images. Efficient workflows for text-to-image generation allow businesses to produce visuals at scale, without the need for manual image creation.

Stable Diffusion Models vs Custom Diffusion Variants

Stable Diffusion is a strong out-of-the-box solution. However, businesses may want to explore custom diffusion models for more specific needs, such as generating highly specialized visuals.

GPT API Integration in SaaS and Internal Platforms

Integrating GPT APIs into software platforms allows businesses to harness AI for various tasks, from customer support to content creation.

Streamlining GPT Calls with Caching and Validation Layers

Using caching and validation layers ensures faster and more efficient GPT API calls, improving response times and reducing costs.

Managing Rate Limits and Token Costs Efficiently

Efficient management of rate limits and token costs is essential for maintaining the performance of GPT applications, especially in large-scale environments.

Open Source vs Proprietary: Which Stack Delivers More Control?

Choosing between open-source and proprietary solutions depends on the level of control a business needs over its AI models.

Governance, Contributions, and Forking Options

Open-source models offer flexibility, as businesses can contribute to the code or fork it for their needs. Proprietary systems, on the other hand, offer more controlled environments but may come with restrictions.

Support Systems for Long-Term Maintenance

Long-term support is crucial for AI models. Open-source projects often rely on community support, while proprietary solutions offer dedicated customer service.

Monitoring, Testing, and Continuous Deployment

Maintaining a generative AI system requires ongoing monitoring and testing to ensure reliability.

Real-Time Error Detection in Generated Outputs

Real-time error detection ensures that AI-generated content meets quality standards, reducing the risk of flawed outputs.

CI/CD Setup for Multi-Model AI Workflows

Setting up Continuous Integration/Continuous Deployment (CI/CD) pipelines allows for smooth updates and testing of AI models, ensuring they remain functional and efficient over time.

Final Thoughts

Generative AI development in 2025 requires a robust tech stack, with the right mix of frameworks, tools, and hardware. The ability to scale models, handle large datasets, and efficiently deploy AI applications will be essential for businesses to stay competitive. Kickstart Your Generative AI Development Today. Malgo leads the field in generative AI development, offering cutting-edge solutions that are reliable and scalable for diverse industries. Their ability to integrate AI seamlessly into business operations ensures that companies can benefit from the latest advancements in AI while optimizing performance and efficiency.

FAQs

What are the must-have components in a generative AI tech stack? Key components include hardware, frameworks like TensorFlow or PyTorch, data management tools, and APIs for deployment.

Which frameworks are most compatible with large-scale LLMs? PyTorch, TensorFlow, and JAX are ideal frameworks for large-scale LLMs.

Is Stable Diffusion better suited for commercial or research projects? Stable Diffusion is effective for both, but customized versions may suit specific commercial needs.

How can I make GPT API usage more efficient in large apps? Use caching, manage rate limits, and optimize token usage to improve efficiency.

Do open-source models outperform paid solutions in 2025? It depends on specific needs, but open-source models offer more flexibility, while proprietary models provide support and control.

[uthra-krish]

The Skills I Acquired on My Path to Becoming a Data Scientist

Data science has emerged as one of the most sought-after fields in recent years, and my journey into this exciting discipline has been nothing short of transformative. As someone with a deep curiosity for extracting insights from data, I was naturally drawn to the world of data science. In this blog post, I will share the skills I acquired on my path to becoming a data scientist, highlighting the importance of a diverse skill set in this field.

The Foundation — Mathematics and Statistics

At the core of data science lies a strong foundation in mathematics and statistics. Concepts such as probability, linear algebra, and statistical inference form the building blocks of data analysis and modeling. Understanding these principles is crucial for making informed decisions and drawing meaningful conclusions from data. Throughout my learning journey, I immersed myself in these mathematical concepts, applying them to real-world problems and honing my analytical skills.

Programming Proficiency

Proficiency in programming languages like Python or R is indispensable for a data scientist. These languages provide the tools and frameworks necessary for data manipulation, analysis, and modeling. I embarked on a journey to learn these languages, starting with the basics and gradually advancing to more complex concepts. Writing efficient and elegant code became second nature to me, enabling me to tackle large datasets and build sophisticated models.

Data Handling and Preprocessing

Working with real-world data is often messy and requires careful handling and preprocessing. This involves techniques such as data cleaning, transformation, and feature engineering. I gained valuable experience in navigating the intricacies of data preprocessing, learning how to deal with missing values, outliers, and inconsistent data formats. These skills allowed me to extract valuable insights from raw data and lay the groundwork for subsequent analysis.

Data Visualization and Communication

Data visualization plays a pivotal role in conveying insights to stakeholders and decision-makers. I realized the power of effective visualizations in telling compelling stories and making complex information accessible. I explored various tools and libraries, such as Matplotlib and Tableau, to create visually appealing and informative visualizations. Sharing these visualizations with others enhanced my ability to communicate data-driven insights effectively.

Machine Learning and Predictive Modeling

Machine learning is a cornerstone of data science, enabling us to build predictive models and make data-driven predictions. I delved into the realm of supervised and unsupervised learning, exploring algorithms such as linear regression, decision trees, and clustering techniques. Through hands-on projects, I gained practical experience in building models, fine-tuning their parameters, and evaluating their performance.

Database Management and SQL

Data science often involves working with large datasets stored in databases. Understanding database management and SQL (Structured Query Language) is essential for extracting valuable information from these repositories. I embarked on a journey to learn SQL, mastering the art of querying databases, joining tables, and aggregating data. These skills allowed me to harness the power of databases and efficiently retrieve the data required for analysis.

Domain Knowledge and Specialization

While technical skills are crucial, domain knowledge adds a unique dimension to data science projects. By specializing in specific industries or domains, data scientists can better understand the context and nuances of the problems they are solving. I explored various domains and acquired specialized knowledge, whether it be healthcare, finance, or marketing. This expertise complemented my technical skills, enabling me to provide insights that were not only data-driven but also tailored to the specific industry.

Soft Skills — Communication and Problem-Solving

In addition to technical skills, soft skills play a vital role in the success of a data scientist. Effective communication allows us to articulate complex ideas and findings to non-technical stakeholders, bridging the gap between data science and business. Problem-solving skills help us navigate challenges and find innovative solutions in a rapidly evolving field. Throughout my journey, I honed these skills, collaborating with teams, presenting findings, and adapting my approach to different audiences.

Continuous Learning and Adaptation

Data science is a field that is constantly evolving, with new tools, technologies, and trends emerging regularly. To stay at the forefront of this ever-changing landscape, continuous learning is essential. I dedicated myself to staying updated by following industry blogs, attending conferences, and participating in courses. This commitment to lifelong learning allowed me to adapt to new challenges, acquire new skills, and remain competitive in the field.

In conclusion, the journey to becoming a data scientist is an exciting and dynamic one, requiring a diverse set of skills. From mathematics and programming to data handling and communication, each skill plays a crucial role in unlocking the potential of data. Aspiring data scientists should embrace this multidimensional nature of the field and embark on their own learning journey. If you want to learn more about Data science, I highly recommend that you contact ACTE Technologies because they offer Data Science courses and job placement opportunities. Experienced teachers can help you learn better. You can find these services both online and offline. Take things step by step and consider enrolling in a course if you’re interested. By acquiring these skills and continuously adapting to new developments, they can make a meaningful impact in the world of data science.

[accelebrate]

Machine Learning Training for Non-Tech Professionals: How to Get Started

For non-tech professionals, venturing into machine learning can seem intimidating. Yet, this field is becoming more accessible, presenting exciting opportunities for those ready to tackle new challenges. This guide will help you navigate the transition into machine learning roles, providing you with the essential knowledge and resources to start your journey with confidence. By following these steps, you'll be well on your way to harnessing the power of machine learning to enhance your career.

Understanding the Basics

Before you jump into the deep end, it's essential to grasp the fundamentals of machine learning. Start with understanding what machine learning is: it's a branch of artificial intelligence (AI) that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed. The core concepts include supervised learning, unsupervised learning, and reinforcement learning. Familiarizing yourself with these basics will provide a solid foundation for further exploration.

Identify Your Goals

Next, define why you want to learn machine learning. Are you looking to enhance your current role, switch careers, or start a new project? Knowing your goals will help you choose the right learning path. For instance, if you're in marketing, you might focus on predictive analytics. If you're in finance, you might be more interested in risk modeling.

Leverage Online Resources

There is a wealth of online resources designed to make machine learning accessible to non-tech professionals. Platforms like Coursera, edX, and Udacity offer introductory courses that cover the basics of machine learning without requiring a deep technical background. Look for courses that offer practical exercises and real-world applications, which can help bridge the gap between theory and practice.

Start with Data Analysis

One of the most crucial aspects of machine learning is data analysis. Learning how to handle and analyze data will make the transition smoother. Tools like Excel, Google Sheets, and basic statistical software are great starting points. Once you're comfortable with data handling, you can move on to more advanced tools like Python or R, which are commonly used in machine learning.

Choose the Right Training Program

For non-tech professionals, choosing a comprehensive and supportive training program is vital. Look for programs that offer structured learning paths, hands-on projects, and personalized support. Accelebrate is a renowned provider in this space, offering a wide range of courses designed to cater to different levels of expertise and industry needs. Their courses are known for their practical approach and expert instructors, making them an excellent choice for those new to the field.

Stay Updated and Keep Learning

Machine learning is a rapidly evolving field, and continuous learning is essential. Stay updated with the latest trends, tools, and technologies by following industry blogs, subscribing to relevant journals, and experimenting with new techniques and algorithms.

In Conclusion

Transitioning into a machine learning role as a non-tech professional is entirely achievable with the right approach and resources. To further accelerate your learning journey, consider enrolling in a course with Accelebrate. Their extensive range of machine learning courses is tailored to help professionals from all backgrounds gain the skills they need to succeed in the world of data and AI.

Embrace the challenge, and you'll find that the world of machine learning is not just for techies—it's for anyone willing to learn and innovate. Happy learning!

For more details, visit: https://www.accelebrate.com/machine-learning-training

[almea]

I've got 2.1 thoughts.

This was really the Aventurine patch. His perspective was definitely the longest one, especially considering he's the first one you explore both maps with. It's an interesting way to tell the story because with the kind of person Aventurine is, there's no way he would have confided even half of what we learned about him in the story to another character. I didn't dislike him after 2.0 (Although I did find it funny when he was trying to make us suspicious of Acheron because she's an Emanator and I was just like. Aventurine, you just told me that not only is Acheron hot but she's also scary strong and you expect me to not lay my life on the line for her?) but I wasn't that invested in him either. I'm still not Invested invested in him after 2.1, but I did really feel for him, especially towards the end. I'm just truly the weakest bitch in the universe when it comes to characters talking to their younger selves. I didn't expect them to get as into Aventurine being a slave or former slave as they did, but damn.

And by the end I was like. Is this man dead? Did they kill this man right before his banner? After everyone was like "Oh no, they drip marketed Robin so now everyone knows she's not dead because dead characters wouldn't be playable"? After his talk with Acheron my understanding is that he's not actually dead, he was just able to break in to a deeper part of dream because of Acheron and he may not be able to return so I guess he's effectively kind of dead for now???

I think it's such a weird choice that Aventurine's hat is clearly supposed to be important to him but he just. Doesn't wear it 95% of the time???? I feel like that final cutscene would have hit harder if he actually wore his hat so it would be more strongly associated with him. I don't understand why they designed him with that hat and then just like never make him wear it.

It's pretty clever that his perspective being so long also acts as an extra long trial for his character. I ended up using my Jingliu team for his portions of the story because she's the only DPS that I've even remotely built besides Qingque, and she was truly doing more damage to my team than the enemies were. Thinking about it now, I prooooobably would have been fine running him with my Qingque team but I would have missed the reliability of the mono Quantum. The extra long trial did not work on me though because I've got Stellaron Hunters to collect and I'm still waffling about whether or not to get Acheron's light cone. I also still haven't made up my mind about whether to pull for Robin yet.

His boss fight was kind of a pain in the ass though. My poor Silver Wolf and Lynx were getting bullied nonstop during the second phase while Qingque was overcapping the dice like crazy because she kept getting her follow up and Fu Xuan's ultimate was her saving grace. Using Qingque against Aventurine feels so appropriate because so many of her combat lines are related to gambling.

I liked Sparkle's conversation with Aventurine very plainly confirming that she has a very clear picture of what's happening but, true to her little animation, she prefers to work from the background and let the main characters do all the heavy lifting. Going off that animation, I'm looking forward to seeing what role she plays in 2.2.

March being so indignant that Stelle was getting bullied left and right by people with Agendas ever since they split up was really sweet. I love how people's general feeling after 2.0 was like "Everyone we met was fucking suspicious and hiding things from us, I don't fully trust anyone except the rest of the Express crew" and the characters were also like "Yeah, we can only trust each other."

The other side of that being that they kept running into people Stelle fought in 2.0 and March and Himeko being like "What have you been doing while you were unsupervised? Were you committing crimes?" was really funny.

I'm curious about whether Dan Heng is going to join in on the action in 2.2. He's already asked if they they need him in 2.1, so the possibility of him getting called in if things get really dicey is already there. And there's the fact that he does appear in one shot of the White Night trailer, and I watched a stream where his voice actor was saying Penacony's really good which makes it sound like he recorded more lines than just Dan Heng saying he'd stay on the train. Or idk maybe the other voice actors told him about stuff that happened. Is that allowed lmao? Sam also said Elio's instruction was "Get all of the Astral Express to track down the grand legacy," so does that "all" include Dan Heng?

It was very cruel of them to have one of the first things in the story be Acheron saying she knows who's in Sam's armour and then just. Not touching on that again until the last five to ten minutes. I figured they were going to do the Sam/Firefly reveal at some point in 2.1 so they'd be able to drip market Sam/Firefly for 2.3 (I've deluded myself into believing they're coming out in 2.3 because it makes sense to me to release them during Penacony's epilogue while they're main story relevant) since they're probably both in the splash art, but man did they take their sweet time getting there and slowly killed me the entire time.

I'm sooooo fascinated by the whole Sam/Firefly thing. I was tragically spoiled by a leak forever ago, but the leak I saw was just someone saying "I guess Firefly is the exploration model and Sam is the battle model" so I still don't know whether Firefly is an actual person who exists in the real world or if she's just a form Sam is able to take on in the dream world. If Firefly is a real person, I am so obsessed with the juxtaposition of Sam being the most ruthless Stellaron Hunter, to the point where Kafka said people are better off running into her, and Firefly looking like the sweetest, most gentle girl ever.

And if she's real, I'm so curious about whether the other Stellaron Hunters know about Firefly. They've only ever referred to Sam with male pronouns, but we've never had a scene with just them where they mentioned Sam so they could know about her and have Reasons for letting other people think Sam is male.

Looking back at 2.0, I assume Firefly might have transformed into Sam if Black Swan hadn't saved them because Stelle was in danger and their scripts from Elio aren't as detailed as the ones we've seen the other Stellaron Hunters reference, and her apology when she was killed could be read as "sorry I'm going to give you some trauma now so you'll have the motivation to find the Watchmaker's legacy." I'm not super clear on whether the memories of Firefly Stelle followed with Black Swan and Acheron was just an act she put on to lead Stelle to that place or there was something more going on there.

I just really want to know how much of Firefly in 2.0 was an act and how much of it was genuine. The main thing being when she tells Stelle she hopes they don't have to be enemies when everything is revealed. I love how Firefly's Sam persona was clearly leaking out a bit when she told Sampo/Sparkle "You talk too much."

2.2 really cannot come soon enough because I've got so many questions and I'm so excited to see the story pick up right from Stelle finding out Firefly is Sam. I can't wait to see what dialogue options Stelle gets for her reaction. Stelle's thoughts when you go back to Firefly's secret base are that she still cherishes the memories of the time she spent with Firefly, but she's confused about how to feel about her being Sam. I'm just like, Stelle, please hug her because I am a fool who would forgive her because she's cute and I love the Stellaron Hunters dearly, but I think if I'm not being self-indulgent I'm just expecting Stelle to be happy or relieved that Firefly's not dead but also feel very wary of her.

I think it's so funny that during the 2.1 livestream they were like. "Gallagher is so normal. He's the most normal person in the whole Penacony cast. He doesn't have a past, he's just a guy." And then by the end of 2.1 it's like. Actually Gallagher may have the biggest past out of everyone.

I'm not clear on what's up with that bird that's watching in some scenes. I thought it could be related to Sunday since he said he has servants that see everything, but its colour scheme looks like the Memory Zone monsters so it might also be related to Gallagher? idk if I'm even supposed to understand at this point.

I love that they made a special trial for Acheron so they could be like "Look how cool her technique is!!!"

It was very nice of them to let us collect the birds even when we weren't in Stelle's perspective, but Acheron and Aventurine can't actually see them so the dialogue options reflected that.

Black Swan talking to a memory? Of Constance wasn't something I expected. I'm still not clear on what was happening during that scene, but I'm interested to see where it goes in 2.2. Boothill getting an early cameo in that part was fun as well. It was so funny to me when he got drip marketed because I was just like. I have never seen this man in my life before lmao. I must have stopped looking at leaks before he was found.

Topaz's very brief appearances were very interesting. It seems like she feels at least a little conflicted about Aventurine's "death." I think Topaz herself is very kind, but she's still one of the Ten Stonehearts and that whole group seems a little shady. I don't think I trust Jade at all.

[vivekavicky12]

Navigating the Data Science Learning Landscape: A Guide to Different Types of Courses

Embarking on a journey into the realm of data science involves mastering a diverse set of skills. Whether you're a beginner or looking to specialize, understanding the types of data science courses available is crucial. Choosing the best Data Science Institute can further accelerate your journey into this thriving industry.

In this blog, we'll navigate through various types of data science courses, each catering to specific facets of this multidimensional field. 

1. Foundational Data Science Courses:

Foundational courses lay the groundwork for understanding key concepts in data science. They cover fundamental principles of data analysis, statistics, and basic programming skills necessary for any data scientist.

2. Programming for Data Science Courses:

Mastery of programming languages is at the core of data science. Courses in this category focus on teaching languages such as Python or R, ensuring proficiency in the tools essential for data manipulation and analysis.

3. Data Visualization Courses:

Data visualization is an art form in data science. These courses delve into techniques for creating compelling visualizations that effectively communicate insights drawn from data.

4. Machine Learning Courses:

Machine learning is a cornerstone of data science. Courses in this category explore various algorithms and models used in machine learning, covering both supervised and unsupervised learning techniques.

5. Deep Learning Courses:

For those diving into the intricacies of neural networks and deep learning, specialized courses explore frameworks, applications, and the theoretical underpinnings of this powerful subset of machine learning.

6. Big Data Courses:

Handling large volumes of data requires specialized skills. Big data courses address the challenges and tools associated with processing and analyzing massive datasets.

7. Natural Language Processing (NLP) Courses:

Understanding and processing human language is critical in data science. NLP courses focus on techniques for working with text and language-related data.

8. Data Engineering Courses:

Data engineering courses cover the technical aspects of collecting, storing, and managing data to ensure it's ready for analysis.

9. Time Series Analysis Courses:

For those working with time-dependent data, time series analysis courses provide insights into techniques for analyzing and forecasting temporal patterns.

10. Data Ethics and Privacy Courses:

As data science continues to evolve, ethical considerations become paramount. Courses in data ethics and privacy address the responsible handling of data and the associated ethical considerations.

11. Domain-Specific Data Science Courses:

Tailored to specific industries or applications, these courses delve into the unique challenges and opportunities within domains such as healthcare, finance, or marketing.

12. Capstone Projects or Case Studies:

Application-focused courses allow learners to bring together their skills by working on real-world projects or case studies. This hands-on experience is invaluable for showcasing practical expertise.

In the vast landscape of data science, the journey of learning involves a variety of courses catering to different skill sets and interests. Whether you're building a strong foundation, specializing in a specific area, or applying your skills to real-world projects, the diverse types of data science courses ensure there's a learning path for everyone. Choose courses based on your current level, career aspirations, and the specific aspects of data science that intrigue you the most. Remember, the key to mastering data science lies in the continuous pursuit of knowledge and hands-on experience. Choosing the best Data Science courses in Chennai is a crucial step in acquiring the necessary expertise for a successful career in the evolving landscape of data science.

[redbixbite-solutions]

Everything You Need to Know About Machine Learning

Ready to step into the world of possibilities with machine learning? Learn all about machine learning and its cutting-edge technology. From what do you need to learn before using it to where it is applicable and their types, join us as we reveal the secrets. Read along for everything you need to know about Machine Learning!

What is Machine Learning?

Machine Learning is a field of study within artificial intelligence (AI) that concentrates on creating algorithms and models which enable computers to learn from data and make predictions or decisions without being explicitly programmed. The process involves training a computer system using copious amounts of data to identify patterns, extract valuable information, and make precise predictions or decisions.

Fundamentally, machine Learning relies on statistical techniques and algorithms to analyze data and discover patterns or connections. These algorithms utilize mathematical models to process and interpret data. Revealing significant insights that can be utilized across various applications by different AI ML services.

What do you need to know for Machine Learning?

You can explore the exciting world of machine learning without being an expert mathematician or computer scientist. However, a  basic understanding of statistics, programming, and data manipulation will benefit you. Machine learning involves exploring patterns in data, making predictions, and automating tasks.

 It has the potential to revolutionize industries. Moreover, it can improve healthcare and enhance our daily lives. Whether you are a beginner or a seasoned professional embracing machine learning can unlock numerous opportunities and empower you to solve complex problems with intelligent algorithms.

Types of Machine Learning

Let’s learn all about machine learning and know about its types.

Supervised Learning

Supervise­d learning resemble­s having a wise mentor guiding you eve­ry step of the way. In this approach, a machine le­arning model is trained using labele­d data wherein the de­sired outcome is already known.

The­ model gains knowledge from the­se provided example­s and can accurately predict or classify new, unse­en data. It serves as a highly e­ffective tool for tasks such as dete­cting spam, analyzing sentiment, and recognizing image­s.

Unsupervised Learning

In the re­alm of unsupervised learning, machine­s are granted the autonomy to e­xplore and unveil patterns inde­pendently. This methodology mainly ope­rates with unlabeled data, whe­re models strive to une­arth concealed structures or re­lationships within the information.

It can be likene­d to solving a puzzle without prior knowledge of what the­ final image should depict. Unsupervise­d learning finds frequent application in dive­rse areas such as clustering, anomaly de­tection, and recommendation syste­ms.

Reinforcement Learning

Reinforce­ment learning draws inspiration from the way humans le­arn through trial and error. In this approach, a machine learning mode­l interacts with an environment and acquire­s knowledge to make de­cisions based on positive or negative­ feedback, refe­rred to as rewards.

It's akin to teaching a dog ne­w tricks by rewarding good behavior. Reinforce­ment learning finds exte­nsive applications in areas such as robotics, game playing, and autonomous ve­hicles.

Machine Learning Process

Now that the diffe­rent types of machine le­arning have been e­xplained, we can delve­ into understanding the encompassing proce­ss involved.

To begin with, one­ must gather and prepare the­ appropriate data. High-quality data is the foundation of any triumph in a machine le­arning project.

Afterward, one­ should proceed by sele­cting an appropriate algorithm or model that aligns with their spe­cific task and data type. It is worth noting that the market offe­rs a myriad of algorithms, each possessing unique stre­ngths and weaknesses.

Next, the machine goes through the training phase. The model learns from making adjustments to its internal parameters and labeled data. This helps in minimizing errors and improves its accuracy.

Evaluation of the machine’s performance is a significant step. It helps assess machines' ability to generalize new and unforeseen data. Different types of metrics are used for the assessment. It includes measuring accuracy, recall, precision, and other performance indicators.

The last step is to test the machine for real word scenario predictions and decision-making. This is where we get the result of our investment. It helps automate the process, make accurate forecasts, and offer valuable insights. Using the same way. RedBixbite offers solutions like DOCBrains, Orionzi, SmileeBrains, and E-Governance for industries like agriculture, manufacturing, banking and finance, healthcare, public sector and government, travel transportation and logistics, and retail and consumer goods.

Applications of Machine Learning

Do you want to know all about machine learning? Then you should know where it is applicable.

Natural Language Processing (NLP)- One are­a where machine le­arning significantly impacts is Natural Language Processing (NLP). It enables various applications like­ language translation, sentiment analysis, chatbots, and voice­ assistants. Using the prowess of machine le­arning, NLP systems can continuously learn and adapt to enhance­ their understanding of human language ove­r time.

Computer Vision- Computer Vision pre­sents an intriguing application of machine learning. It involve­s training computers to interpret and compre­hend visual information, encompassing images and vide­os. By utilizing machine learning algorithms, computers gain the­ capability to identify objects, faces, and ge­stures, resulting in the de­velopment of applications like facial re­cognition, object detection, and autonomous ve­hicles.

Recommendation Systems- Recomme­ndation systems have become­ an essential part of our eve­ryday lives, with machine learning playing a crucial role­ in their developme­nt. These systems care­fully analyze user prefe­rences, behaviors, and patte­rns to offer personalized re­commendations spanning various domains like movies, music, e­-commerce products, and news article­s.

Fraud Detection- Fraud dete­ction poses a critical concern for businesse­s. In this realm, machine learning has e­merged as a game-change­r. By meticulously analyzing vast amounts of data and swiftly detecting anomalie­s, machine learning models can ide­ntify fraudulent activities in real-time­.

Healthcare- Machine learning has also made great progress in the healthcare sector. It has helped doctors and healthcare professionals make precise and timely decisions by diagnosing diseases and predicting patient outcomes. Through the analysis of patient data, machine learning algorithms can detect patterns and anticipate possible health risks, ultimately resulting in early interventions and enhanced patient care.

In today's fast-paced te­chnological landscape, the field of artificial inte­lligence (AI) has eme­rged as a groundbreaking force, re­volutionizing various industries. As a specialized AI de­velopment company, our expe­rtise lies in machine le­arning—a subset of AI that entails creating syste­ms capable of learning and making predictions or de­cisions without explicit programming.

Machine learning's wide­spread applications across multiple domains have transforme­d businesses' operations and significantly e­nhanced overall efficie­ncy.

[threewaysdivided]

G.R.R. Martin once planned to have a time skip in between each chapter of the books, but discarded it because it would require large amounts of flashbacks and expositions to get the audience up to speed. Boy, it sure is great that YJ only had one season so we didn't have to worry about each sequential season having to allocate precious episode time to explain what happen in the gaps while also developing new characters and plot lines /s

Sadly I cannot speak to or for Mister George Raymond Richard Martin's storytelling as I managed to miss the boat on both ASOIAF as a book series and Game of Thrones as a TV phenomenon. However, it certainly seems like he made the right decision there. Smart man.

As for the Young Justice Animated series... I can only agree. While I would have loved to see a continuation of the mystery, and think there was a lot of potential in the original cast of heroes and interesting things they could have done with The Light, I'm glad that DC made the call to cancel the show and move on to new projects when it became clear that the tone and themes didn't fit with their brand-pivot to New 52 edginess, rather than trying to forcibly crush the series into an incompatible mold for use as a marketing vessel.

Plus, the more complaints I've heard from other franchises about what happens when you leave Greg Weisman creatively unsupervised, the more likely it is that we would have just been in for a bad-fanfic barrage of random new characters, arbitrary time-skips, jumbled perspective-hopping, attempted twitter-retcons and general throwing-ideas-at-a-wall that he seems to use in place of actually learning basic narrative techniques like developing a story arc, maintaining character consistency or creating proper causal links between concepts, all seasoned with a heaping helping of Joss-Whedon-inspired performative virtue-signalling, creepy rape-y garbage and fetishistic sexism. Bullet dodged, methinks. Our city now.

There are fine things that are more brilliant when they are unfinished than when finished too much. - François de La Rochefoucauld

Here's to a rare and pleasant display of creative honesty.

[Goof-posting because there's really not a lot about YJ's problems that I haven't already dissected at length. If you want a serious technical look at the scope and structure problems the timeskips cause then I've gone into it here. You can also try here for a deeper dive into how the timeskips are actually just a symptom of a bigger problem of narratively directionless story-contradictions being hastily papered over by a largely-untalented privilege-poisoned embodiment of George Lucas Syndrome and a largely-inexperienced adaptation producer who didn't have the original-storytelling chops to compensate for his co-runner's bullshit once the actually talented original production/ directorial team left the picture. C'est la capitalisme, I guess.]

[alivah2kinfosys]

Can AI Courses for Beginners Lead to Job Opportunities?

Introduction: Why AI Skills Matter More Than Ever

Artificial Intelligence (AI) is transforming the way the world works, automating tasks, powering smart systems, and shaping industries from healthcare to finance. You’ve probably heard the buzz around AI and wondered, “Can AI courses for beginners help me get a job?”

The answer is a clear yes. Whether you're a student, recent graduate, or career switcher, AI training opens doors to exciting, future-ready roles. The demand for skilled professionals in AI and machine learning is skyrocketing, and getting trained, even at a beginner level, can set you on the path to a high-paying and meaningful career.

The Booming Demand for AI Skills

Before diving into how beginner AI courses help, let’s look at the demand:

AI Job Growth: According to the World Economic Forum, AI and machine learning roles are among the top emerging jobs, expected to grow by 40% by 2027.

Hiring Trend: A LinkedIn survey highlighted that AI-related skills like machine learning, deep learning, and natural language processing are now in high demand across sectors like tech, banking, e-commerce, and healthcare.

Salary Potential: Entry-level AI specialists can earn an average of ₹8 to ₹12 LPA in India, with roles in AI engineering, data analysis, and automation testing.

What You’ll Learn in AI Courses for Beginners

An AI course for beginners typically starts with the fundamentals and slowly introduces more advanced tools and techniques. Here’s what’s usually covered:

AI Fundamentals

What is AI?

History and evolution of artificial intelligence

Types of AI: Narrow, General, and Super AI

Python for AI

Introduction to Python programming

Writing basic AI logic

Python libraries: NumPy, Pandas, Matplotlib

Machine Learning Basics

What is machine learning?

Supervised vs. unsupervised learning

Common ML algorithms: Linear Regression, Decision Trees, KNN

Real-World AI Applications

AI in chatbots and customer service

AI in recommendation systems (e.g., product suggestions)

AI in fraud detection and risk analysis

Hands-On Projects

Predicting house prices using linear regression

Creating a basic AI chatbot

Using classification algorithms for spam detection

How AI Courses Translate to Job Opportunities

Let’s get to the core: how do beginner AI courses actually lead to jobs?

1. Skill Alignment with Industry Needs

AI training programs are designed to meet current market needs. Most companies now value practical skills over degrees. If you can show you’ve built a working machine learning model, even a simple one, you’re already more attractive to employers.

2. Portfolio Development

Beginner AI courses include projects you can showcase in your resume. These hands-on tasks serve as proof of skill, which can be shared during job interviews or on professional platforms.

3. Certifications Add Value

An Artificial Intelligence certification online helps validate your expertise. Recruiters often filter resumes using certifications as a criterion. It’s a simple yet effective way to stand out.

4. Career Pathways

Even basic AI knowledge opens doors to multiple job roles:

AI Assistant or AI Support Analyst

Junior Machine Learning Engineer

Data Analyst

Python Developer with AI Focus

AI QA Tester

As you gain more experience, roles like AI Engineer, NLP Developer, or AI Product Manager become attainable.

What Makes a Good AI Course for Beginners?

When choosing a course, here’s what to look for:

Structured Curriculum

Ensure the course covers the basics of AI, Python, machine learning, and offers hands-on exercises.

Practical Application

Look for an AI training program that includes projects and real-time problem solving.

Certification

Make sure the course offers a recognized Artificial Intelligence certification online.

Mentor Support

Courses with expert trainers and doubt-clearing sessions make learning smoother.

Placement Support

Check if the course includes resume building, interview preparation, and job placement assistance.

Real-World Use Cases Covered in Beginner AI Courses

Understanding how AI is used today will boost your confidence and perspective.

Healthcare

AI in medical imaging

Predictive analytics for patient care

E-Commerce

Product recommendation engines

Inventory management automation

Finance

AI-driven fraud detection

Algorithmic trading

Automotive

Self-driving algorithms

Predictive maintenance for vehicles

AI and Machine Learning Courses: Where They Take You

Beginner AI and machine learning courses can lead you into various tech roles. You could become a Data Scientist or Machine Learning Engineer, working with data to build predictive models. If you're interested in language, NLP roles like Chatbot Developer are a great fit. For those who love visual tech, Computer Vision skills can help you work on facial recognition or autonomous vehicles. Want to dive deeper? Deep Learning leads to roles building neural networks and smart systems. You can even explore AI in Robotics, creating automation tools and intelligent machines. Each path offers strong career growth and industry demand.

Simple AI Program Idea for Practice

Here’s a quick AI task you can do with beginner knowledge:

python

# A basic spam detection classifier using Python and scikit-learn

from sklearn.feature_extraction.text import CountVectorizer

from sklearn.naive_bayes import MultinomialNB

texts = ["Buy now!", "Limited offer", "Hi John, how are you?", "Your OTP is 123456"]

labels = [1, 1, 0, 0]  # 1 = Spam, 0 = Not Spam

vectorizer = CountVectorizer()

features = vectorizer.fit_transform(texts)

model = MultinomialNB()

model.fit(features, labels)

# Test with a new message

test = vectorizer.transform(["Free entry in a contest"])

print("Spam" if model.predict(test)[0] else "Not Spam")

This shows how just a few lines of code can be used to apply AI in real-world tasks.

Key Takeaways

AI courses for beginners are designed to help anyone, regardless of background, get started with industry-relevant skills.

Completing an artificial intelligence course online with certification enhances your credibility and job-readiness.

Real-world projects, hands-on exercises, and job support make these courses highly effective for career growth.

There are various job roles, even at the beginner leve,l that AI learners can step into.

With the right mindset and guidance, AI can be your gateway to the tech industry.

Final Words: Your Career Starts Here

AI isn’t just the future, it’s the present. Equip yourself with real-world skills through beginner AI courses and become a part of the growing AI revolution.

Enroll now at H2K Infosys to learn AI hands-on and take your career to the next level. Your future in tech starts today!