#supervisedlearning

[edutech-brijesh]

Machine learning algorithms use data to make predictions and decisions without explicit programming, enabling automation and insights for various applications like healthcare and finance.

[fortunatelycoldengineer]

[thedevmaster-tdm]

Unlocking the Secrets of LLM Fine Tuning! 🚀✨

[learnandgrowcommunity]

AI & Machine Learning Fundamentals in 2 Hours for Beginners

Welcome to "AI & Machine Learning Fundamentals in 2 Hours for Beginners"! This session is designed to provide a comprehensive introduction to Artificial Intelligence (AI) and Machine Learning (ML), covering essential topics and concepts in a concise, easy-to-understand format. Whether you're a novice or looking to refresh your knowledge, this session is perfect for you.

Video Link - https://youtu.be/AYCul4JiryQ

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[probabs]

Deep Reinforcement Learning

Get to grips with deep reinforcement learning in Probabs' advanced course. Learn how to tackle complex decision-making problems in AI and robotics like a pro.

[globaltechnologysolution]

An AI dataset is a collection of data used for training, testing, and validating machine learning models, and its quality and diversity are crucial for accurate and reliable model performance.

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[rithangowda29]

An Unbiased View Of The 6 Types of Supervised Learning

Supervised Machine Learning is an important area in today's world of big data. From your search history to your Facebook likes, many various types of data can be utilized with supervised learning. The field is constantly advancing, especially with the advent of deep neural networks, which can process image and speech data more accurately than before.

In this article, we will go over the detailed introduction of supervised learning along with its applications.

Supervised learning

Supervised learning is the process of training a model on training data, then validating its predictions against new data. The critical process allows machine learning algorithms to be used for various tasks, including fraud detection, image analysis, and recommendation engines.

Difference between Supervised and Unsupervised learning:

Supervised learning is when you have some data about the relationship between two or more variables and want to use that data to predict new values for unknown variables. This can be used for classification or regression problems.

Unsupervised learning is when you have no training data but want to make predictions based on relationships between variables. This type of prediction can be used for clustering or dimensionality reduction problems. A detailed explanation of ML types can be found in a Machine learning course in Delhi, co-developed with IBM.

Classification: 

The goal of classification is to determine the correct class labels for new instances (data) based on their relationship with examples stored in the training set. This type of learning is used when the labels represent categorical values, which can be either discrete or continuous.

For example, using classification, you can decide whether or not a creditor will default on a loan if you are planning to give them credit.

Regression: 

This type of learning aims to predict continuous variables given a set of known values for those variables (that have been previously determined).

By using training data, regression generates a single output value. This value is a probabilistic interpretation determined by assessing the correlation strength among the input parameters. For example, a regression can effectively forecast the price of a property based on its location, size, and other factors.

By using logistic regression, the output contains discrete values depending on a collection of independent factors. When dealing with non-linear and various choice limits, this strategy might fail. 

Linear Regression

Linear regression is one example of a supervised learning algorithm that uses a linear fit between the input and output variables. The output variable is called the prediction variable, and it's used to predict an outcome (for example, whether or not a patient will die). The model's coefficients are the x-intercepts of the line, and its slope represents how much change there is from y-intercept to y-intercept.

Logistic regression

When the dependent variable has a binary or categorical output, such as "yes" or "no," logistic regression is utilized. Additionally, logistic regression forecasts discrete values for variables since it is employed to address binary classification problems.

Naive Bayesian Model:

The Bayesian classification model is used for big finite datasets. It is a technique of assigning class labels that use a direct acyclic graph. The graph has one parent node and many child nodes. Moreover, each child node is expected to be independent and different from the parent. Since the supervised learning model in ML supports the development of classifiers in a basic and easy manner, it works well with smaller data sets.

This model is based on common data assumptions, such as the hypothesis that each attribute is independent. Despite its simplicity, this approach can be easily applied to complex situations.

Decision Trees: 

Decision Trees classify depending on the values of the features. They apply the Information Gain approach to figure out which component of the dataset provides the critical information, identify that as the root node, and so on until they can classify each dataset sample. Every branch of the Decision denotes a dataset feature. They are one of the most extensively used classification methods.

Random Forest Model:

Using an ensemble approach, the random forest model. It works by building several decision trees and then classifying each tree as it is generated.

The random forest algorithm uses many supervised learning techniques to reach a conclusion and is frequently referred to as an ensemble method. It also employs several decision trees to output the classification of individual trees. It is, therefore, commonly used in a variety of sectors.

Support Vector Machine (SVM): 

SVM algorithms are based on Vap Nik's statistical learning theory. Kernel functions, which are a crucial notion in most learning tasks, are often used. These techniques generate a hyper-plane to differentiate between the two classes.

Application of supervised learning

Speech recognition:

This is the type of application where you train the algorithm about your voice, and it recognizes you. The most popular real-world applications are virtual assistants like Google Assistant and Siri. 

Spam Detection:

This technology is used to prevent unreal or computer-based texts and E-Mails. Gmail includes an algorithm that learns the many terms that may be fraudulent and block those messages immediately.

Conclusion

To sum up, supervised learning is widely used in machine learning. They are primarily used to derive the relation between inputs and the output, e.g., the connection between the pixel of a photo (input) and its label, i.e., whether it depicts a school bus or car, which determines its usefulness for an object recognition task. In applications such as pattern recognition/computer vision, it is often labeled as a classification problem and falls under the discriminative learning domain. This is super simple for humans because we are used to looking at the world around us and labeling it by what we see. The goal of supervised learning is to create an algorithm using a dataset that can be used as a black box, and other algorithms can be applied to achieve similar results. 

If you want to learn more about machine learning, check out Learnbay's Data science course in Delhi, which is intended for working professionals and provides 450+ hours of in-depth training with 12+ hands-on practical projects.

[data-science-lovers]

[theaifusion]

Hyperparameter tuning in machine learning

The performance of a machine learning model in the dynamic world of artificial intelligence is crucial, we have various algorithms for finding a solution to a business problem. Some algorithms like linear regression , logistic regression have parameters whose values are fixed so we have to use those models without any modifications for training a model but there are some algorithms out there where the values of parameters are not fixed.

Here's a complete guide to Hyperparameter tuning in machine learning in Python!

#datascience #dataanalytics #dataanalysis #statistics #machinelearning #python #deeplearning #supervisedlearning #unsupervisedlearning

[contadorpj]

#MachineLearningDescomplicado 💡

Hoje vamos desbravar o universo do #MachineLearning! 🌐

O que é ML? 🤔

ML é como ensinar computadores a aprenderem! 🧠💻

#Algoritmos inteligentes aprendem com dados. 📊📈

Tipos de ML 🔄

#SupervisedLearning: Ensino com exemplos rotulados! 🏫📚

#UnsupervisedLearning: Descobertas sem rótulos! 🕵♂🌌

#ReinforcementLearning: Aprendendo com recompensas! 🏆🕹

Neural Networks 🧠💡

Inspirado no cérebro! 🧠🔗

#DeepLearning: Múltiplas camadas de neurônios! 🌐🔍

Dados, Dados, Dados! 📊📉

#BigData: Grandes volumes de dados! 🌐

🔢

Qualidade dos dados é crucial! 📏👌

Overfitting vs. Underfitting 📉📈

#Overfitting: Aprender demais! 🚀📚

#Underfitting: Não aprende o suficiente! 🚫📚

Avaliação de Modelos 📏📐

#Accuracy: Precisão é chave! ✔📊

#CrossValidation: Testando robustez! 🔄

📂

Deploy do Modelo 🚀🌐

Colocando o modelo em ação! 🚀🎬

#CloudComputing facilita! ☁💻

Agora, você está pronto para explorar o incrível mundo do #MachineLearning! 🚀🌟

#TechTalk #DataScience #AprendizadoDeMáquina #TechExplained

[toboldlycode-blog]

Posted @withregram • @whats_ai Machine Learning (ML) divided into: :Supervised Learning: Classification + Regression and Clustering models and their corresponding algorithms and methods Follow @toboldlycode Credits @artificialintelligence.digest . . . . . #data #datanalytics #datascience #datascientist #ai #ml #artificialintelligence #machinelearning #deeplearning #neuralnetworks #supervisedlearning #unsupervisedlearning #clustering #classification #regression #toboldlycode (at Chicago, Illinois) https://www.instagram.com/p/B7izQiMA7S4/?igshid=f8p815rltchi

[fortunatelycoldengineer]

[thedevmaster-tdm]

Unlocking the Secrets of LLM Fine Tuning! 🚀✨

[learnandgrowcommunity]

Session 11 : What is Classification Task in Supervised machine Learning | Beginner-Friendly ML

Have you ever wondered how machines can be trained to make decisions, just like humans? Classification tasks play a crucial role in this process. Whether it's identifying spam emails, predicting diseases, or recognizing handwritten digits, classification allows machines to categorize data into distinct groups.

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[enoumen]

QNN Machine Learning and AI Breaking News - Data Today https://inRealTimeNow.com/machinelearning iOS: https://apps.apple.com/us/app/qnn-read-breaking-news-trivia/id1611189650 android: coming soon windows: https://www.microsoft.com/store/apps/9P20L5PMN5J8 #QNN #Djamgatech #MachineLearning #AI #ML #MachineLearningBreakingNews #AIBreakingNews #SafeAI #DeepLearnin #SupervisedLearning #NLP #ComputerVision Machine learning can help read the language of life; An intro to AI, made for students; This Googler wants to ‘add every voice’ to AI; Ask a Techspert: What does AI do when it doesn’t know?; Advancing genomics to better understand and treat disease; How Abigail Annkah is using AI to improve maps in Africa; 2021 Year in Review: Google Quantum AI; This archaeologist fights tomb raiders with Google Earth; Unlocking human rights information with machine learning; Machine learning to make sign language more accessible; https://www.instagram.com/p/Can6FP8pZDP/?utm_medium=tumblr

[faststreamtechnologies]

Machine Learning | Machine Learning Solutions | Deep Learning | Supervised Learning

Machine learning Solutions bring new insights every day across a broad range of industries and research worldwide. Be part of it and explore the best of what happens when human and machine intelligence is combined. The ever-increasing usage of electronic means of interaction and commerce, as well as IoT devices producing an incredible volume of data and statistics which is impossible for humans to analyze manually. Machine learning technology helps combine all the data gathered from myriad touchpoints for delivering useful insights to enterprises that contribute to the various strategic outcomes.

Machine learning seems to be rocket science for most businesses. But if you are new to data science, it is best to jump-start on machine learning without much investment which would be the right move to grab the low-hanging fruit.

It is integrated, end-to-end data science and advanced analytics solution. It enables data scientists to prepare data, develop experiments, and deploy models on a cloud-scale. Faststream technologies Machine Learning services fully support open source technologies.

Benefits of Our Machine Learning Solutions

Massive Data Consumption from Unlimited Sources

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Machine learning virtually consumes an unlimited amount of comprehensive data. The consumed data can be used to constantly review and modify your sales and marketing strategies based on customer behavioral patterns.

Rapid Analysis Prediction and Processing

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  The rate at which Machine Learning consumes data and identifies relevant data makes it possible for you to take appropriate actions at the right time. For instance, Machine learning will optimize the best subsequent offer for your customer.

Improves Precision of Financial Rules and Models

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It is a significant impact on the finance sector. Machine learning benefits in Finance include portfolio management, algorithmic trading, loan underwriting, and most importantly fraud detection.

Interpret Past Customer Behaviors:

Machine learning will let you analyze the data related to past behaviors or outcomes and interpret them. Therefore, based on the new and different data you will be able to make better predictions of customer behaviors.