#analyticsvidhya

[lastfry]

Analytics Vidhya Reviews – Career Tracks, Courses, Learning Mode, Fee, Reviews, Ratings and Feedback

Analytics Vidhya stands as a vibrant community committed to nurturing the next generation of AI experts by providing comprehensive resources in data science, artificial intelligence, and analytics. This article serves as a review, offering insights into the platform's evolution, courses, certification programs, mentorship, and overall user experience.

Introduction and Evolutionof Analytics Vidhya Review, founded in April 2013 by Kunal Jain, initially began as a part-time blog focusing on science education. Recognizing the growing demand for data science and analytics knowledge, Jain envisioned a platform that would empower learners, professionals, and enthusiasts alike. Over the years, Analytics Vidhya has evolved into a global portal, offering targeted courses, B2B training, and specialized programs like 1729 for AI and ML enthusiasts and Datahour for data science professionals.

Courses and Certification Programs: Analytics Vidhya offers a diverse range of courses and certification programs catering to individuals at various stages of their data science journey. The Certified AI & ML BlackBelt Plus Program and the Generate AI Pinnacle Program are comprehensive courses covering a wide array of topics in AI and machine learning. Participants benefit from well-organized assignments, hands-on projects, and mentorship sessions, gaining practical skills and industry-relevant knowledge.

Mentorship and Support: Analytics Vidhya emphasizes personalized guidance and support through mentorship sessions and career-oriented services. Participants have access to experienced mentors who provide valuable insights and assistance in navigating the complexities of data science. Mentorship sessions facilitate learning and career development, enhancing the overall learning experience for participants.

User Experience and Accessibility: Analytics Vidhya's website and mobile app are designed to provide a seamless and user-friendly experience. The website features a clean layout, intuitive navigation, and responsive design, ensuring accessibility across various devices. Course pages are well-structured, offering detailed information about curriculum, enrollment process, and progress tracking. Additionally, the platform offers ebooks for enhanced accessibility and learning.

Pros and Cons: While Analytics Vidhya offers specialization courses and features for accessibility, some users have raised concerns about course syllabus clarity and mentorship quality. The platform's certification is not recognized by some organizations, and there is limited information available about mentors. However, positive reviews highlight the platform's valuable content, mentorship sessions, and overall learning experience.

Conclusion: In conclusion, Analytics Vidhya emerges as a premier destination for individuals seeking to advance their careers in data science, artificial intelligence, and machine learning. Despite some drawbacks, the platform's comprehensive courses, mentorship opportunities, and commitment to excellence make it a valuable resource for data science enthusiasts worldwide. Through Analytics Vidhya, individuals can acquire the knowledge, skills, and confidence needed to succeed in today's competitive job market and thrive in the rapidly evolving field of data science and AI.

[a-useless-unseen]

𝑨𝒎𝒂𝒛𝒊𝒏𝒈 𝑩𝒍𝒐𝒈 𝒐𝒏 Data Visualization and Unlock the full potential of your data with Python's top 3 must-try visualization libraries! 📈📊🔥👇

✅ Key Features

🔰 Take your data storytelling to the next level. 🔰 Easy-to-follow examples and code snippets. 🔰 Suitable for beginners and experienced data scientists. 🔰 Dive into advanced visualization techniques like heatmaps, 3D scatter plots & animations. 🔰 Unlock the secrets of your data with Python.

Please have a look and share your thoughts 👍 Share this Blog with someone who asks for your help to start their #datascience and #machinelearning Journey. 🤝

[thegotogirl]

Blogging Rule 17. Know Your Audience

Do you know your audience?

Blogging Rule 17. Know Your Audience Do you know who reads your blog?  Male or female? Old or young? What country they are residing in?  No! neither did I until recently.  As I discussed in my post the other day on Blogging Rule 8 analytics can help you with more things than the amount of traffic your website receives.  I particularly love the audience insights which gives you a breakdown of the age of your audience, their gender, location and even which devices they are looking at your website on!  How amazing is that?  I love checking these figures out.  I was excited enough when I saw the figures for the number of impressions my website was receiving without the split of all the categories I found when I started digging.  Audience Insights are important.

Blogging Rule 17. Know Your Audience The analytics I use even tell me the interests recorded by the individual reading my blog.  This is amazing to see as you just can't imagine ever knowing this amount of information about your readers and followers.  I love knowing all about them.  What analytics do you use?  Do you know all about the people who read your posts?  You should as this can help you write more relevant posts and get more traffic which in turn can help you make your brand and website more successful.  I love it when I learn things as I can then spread the word on my blog and help all of my readers to use the tips and tricks I learn much quicker.  Blogging Rule 18 tomorrow.  Thanks for reading.  Love The Go To Girls Blog xxx  Read the full article

[tensorflow4u]

RT @KirkDBorne: Want to learn more #DataScience #MachineLearning #AI #DeepLearning? See these lists of choices: 1) https://t.co/oyvXbufHiU 2) https://t.co/A50woso8KG 3) https://t.co/4csQ6ffAMj 4) https://t.co/URSpE4gIQ6 @AnalyticsVidhya #BigData #DataLiteracy #DataScientists #Coding #abdsc https://t.co/5y7d2vnBiM

[rickmartingt]

Learn AI and ML on the go! https://t.co/SI5IQmjpN5 by @analyticsvidhya #artificialintelligence

[pythoncodequs-blog]

Introduction to Text Summarization using the TextRank Algorithm via @AnalyticsVidhya https://t.co/KO3UbvaPK9 #python https://t.co/R49puuYLA0 (via Twitter http://twitter.com/PythonWeekly/status/1062721945744089088) #Python

[thiruhr]

Tweeted

Top 28 Cheat Sheets for #MachineLearning, #DataScience, Probability, SQL and #BigData https://t.co/XuveE0KdoN via @AnalyticsVidhya #ArtificialIntelligence

— Thirukumaran R (@ThiruHR) September 8, 2018

[akhilmenonz1]

Using the Power of #DeepLearning for #CyberSecurity Current State - Case Study & Data Experiments https://t.co/SNAfDQeXoW [by satnam74s & Balamurali A R v/ analyticsvidhya] #AI #NeuralNetworks #InfoSec https://t.co/Jvb6TOHZwy

Using the Power of #DeepLearning for #CyberSecurity Current State - Case Study & Data Experimentshttps://t.co/SNAfDQeXoW [by satnam74s & Balamurali A R v/ analyticsvidhya]#AI #NeuralNetworks #InfoSec pic.twitter.com/Jvb6TOHZwy

— Akhil Menon (@akhilmenonz1) July 20, 2018

via Twitter https://twitter.com/akhilmenonz1 July 20, 2018 at 05:31PM

[analyticsvidhya]

Basic Concepts of Object-Oriented Programming in Python

While learning Object-Oriented Programming (oops concepts), I decided to dive into its history to fully know what is oops concept and it turned out to be fascinating. The term “Object-Oriented Programming” (OOP), also known as oops concepts in python, was coined by Alan Kay around 1966 while he was at grad school. The language called Simula was the first programming language with the features of Object-oriented programming. It was developed in 1967 for making simulation programs, in which the most important information was called objects.

Though OOPs were in the market since the early 1960s it was in the 1990s that OOPs began to grow because of C++. After that, this technique of programming has been adapted by various programming languages including Python Today its application is in almost every field such as Real-time systems, Artificial intelligence, and expert systems, Client-server systems, Object-oriented databases, and many more.

So, in this article, I will explain the basic concepts of Object-Oriented Programming in Python, oop fundamentals, and features of oops. It is important that you know Python before you continue.

[raposthumus]

via Twitter https://twitter.com/RAPOSTHUMUS

[elgatotweet]

via Twitter https://twitter.com/torbenaarhus

[lomonpla]

RT @Ronald_vanLoon: The Most Comprehensive Guide To K-Means #Clustering You’ll Ever Need by @ PULKIT SHARMA @AnalyticsVidhya Read more: https://t.co/uzVKlumLJp #AI #BigData #MachineLearning #ArtificialIntelligence #ML #MI #DataScience #Analytics cc: @kirkdborne @ywanvanloon @mhiesboeck https://t.co/0TZpXDj06z

RT @Ronald_vanLoon: The Most Comprehensive Guide To K-Means #Clustering You’ll Ever Need by @ PULKIT SHARMA @AnalyticsVidhya Read more: ht…

— Lola Montalban 🇻🇪🐝 (@Lomonpla) Jun 22, 2021

via Twitter https://twitter.com/Lomonpla June 23, 2021 at 01:24AM

[rickmartingt]

Reinforcement learning with A3C https://t.co/0e3n9IEdQ1 by @analyticsvidhya #datascience

[pythoncodequs-blog]

Deep learning Tutorial for Video Classification using Python via @AnalyticsVidhya https://t.co/WEPDdBGgyV #python #video #deeplearning https://t.co/58lsLAg1cD (via Twitter http://twitter.com/PythonWeekly/status/1042035703658545152) #Python