Platforms for Machine Learning, AI, & Data Science Best Practices

Adaptation to Machine Learning, AI, & Data Science

Sharing some of the best online platforms to master AI and Machine Learning for free. I have been following 'Corey Schafer' for learning Python for quite a few years, and it got me a huge boost in learning with efficiency, and I literally became a Machine Learning enthusiast following the playlists from 'Krish Naik' - which is unparalleled for ML or AI, in my opinion. Finally, Kamran Ahmed's developer roadmap helps make my efforts a lot easier.

These actionable steps along with links of resources are expected to be helpful in many ways to most of us who are AI and Machine Learning practitioners. Kindly, feel free to add more to this, cheers!

# Roadmap for Full Stack Data Science and AI : 12 Sections, 4.5 Months ; 1 hour/day

1. Python Programming and Logic Building

2. Data Structure & Algorithms

3. Pandas Numpy Matplotlib

4. Statistics

5. Machine Learning (Data Import > EDA-Preprocess/Cleaning > Feature Engineering > Feature Selection > Model Building > Train/Validation/Test > Deploy > Retrain)

6. Natural Language Processing

7. Deep Learning / Computer Vision

8. Data Visualization with Tableau

9. Databases (MySQL, SQL, MongoDB)

10. Big Data and PySpark

11. Development Operations with AWS, IBMC, GCP and Azure

12. End-end Major Projects and Git

- Details Link

- Actionable steps by Himanshu R : Link

# Roadmap for AI SDE for Global Giants :

1. Basic in ONE Programming Language (30d)

2. Problem-Solving in Data Structure & Algorithms (150d)

3. Computer Science Fundamentals (30d)

4. System Design (30d)

5. Projects Building (30d)

- Details Link

# Roadmap & Resource Site (for best roadmaps: roadmap. sh)

1. Data Professionals Roadmap by Ravena : Link

2. Data Science Roadmap 11 Months : Link

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1.??Python

• Corey Schafer : Link **
• Krish Naik : Link** , Hindi Link, and GitHub
• Sumit Mittal : Link
• Sentdex : Link
• Efficient Python Code : Link
• 60 Python Projects : Link
• 20 Python Projects by Bro Code : Link
• Python Fun Projects : Link
• Python Roadmap & Resources : Link
• Python Roadmap by Himanshu R. : Link
• 170 Python Projects by Himanshu R : Link
• Python Projects & GitHub Repo : Link
• Python by Google, IBM, Harvard : Link
• 68 Built-in Python Functions : Link
• Ways to Master Coding : Link
• Full Resource on Coding : Link
• Python Best Resources : Link **
• Python FreeCodeCamp : Link ** ?
• 5 Easy Steps to Learn for Job : Link
• Python Roadmap : Link
• Python Cheatsheet : Link
• Clean Code in Python : Link
• Python Full Course by DataFlair Hindi : Link
• Python in More Detail by Telusko: Link **
• Python Playlist by Priya Bhatia : Link **
• Python Full Course : Link

_____________________________

2. Data Structure and Algorithms

• DSA FreeCodeCamp : Link & Link
• DSA CodDevX : Link
• DSA GreatLearning : Link
• DSA in Python by CampusX : Link
• GeeksForGeeks Tutorial : Link
• Algorithms by Abdul Bari : Link **
• DSA by codebasics : Link **
• DSA for Free by GregHogg : Link
• DSA by Krish Naik : Link ** ?
• DSA by Priya Bhatia : Link **
• DSA C++ by Apna College : Link
• DSA Leetcode Pattern : Link

_____________________________

3. Statistics and Probability for ML

• Krish Naik Statistics Live : Link**
• Krish Naik Statistics in ML : Link**
• Krish Naik Statistics Roadmap : Link
• Josh Starmer Statistics : Link
• Great Learning : Link and Link
• WsCube Tech : Link
• Recommended Basic Stats : Link

_____________________________

4. EDA & Feature Engineering

• EDA Playlist Krish Naik : Link**
• Feature Engineering Playlist Krish Naik : Link**
• Krish Naik EDA & FE Live : Link**
• Database : Link
• Github : Link 1
• Github : Link 2
• CampusX 100 days YT Playlist : Link

- Standalone Data Analyst

• Data Analyst 60D Prep : Link

_____________________________

5. Database & AI Dataset

- Database

• MySQL Playlist by Krish Naik : Link**
• MySQL Full by ProgWithMosh : Link
• SQL Full Course by Simplilearn : Link
• MongoDB Playlist by Krish Naik : Link**
• MongoDB Full by FreeCodeCamp : Link
• SQL Learning Resources : Link
• SQL Resources by Mandar Patil : Link
• MySQL is the most popular one.
• MySQL YT : Link
• SQL Cheat Sheet : Link
• SQL Resources LinkedIn : Link
• SQL Simplified for All : Link
• PostgreSQL Tutorial for Beginners : Link
• SQL by Priya Bhatia : Link **
• Complete SQL by Apna College : Link **

- Dataset

• Datasource & MongoDB - Real World Projects : Link, Rapid API**
• Data Collection Using APIs / Rapid API : Link
• Kaggle Dataset : Link
• Dataset Collection by Himanshu R. : Link & Link
• 1000+ ML Datasets by Himanshu R. : Link

_____________________________

6. ML Algorithms

• ML Algorithms Live Krish Naik : Link**
• Extended to previous link (with PCA algo) : Link**
• Complete ML in 6 Hours by Krish Naik : Link ?
• Unfold DS : Link
• Simplilearn : Link
• Edureka : Link
• 10 Algorithms for Beginners : Link

_____________________________

7. Tutorials & Resources for Full-Stack ML

• Full-Stack ML Playlist by Krish Naik : Complete Roadmap, End-end Playlist, Basic Tutorial **
• 6-Months Full Stack Roadmap by Krish Naik YT : Link**
• Perfect Roadmap for Data Science in 2024 with Resources by Krish Naik : Link
• Grand Complete Resources by Krish Naik : Link
• Steve Nouri sharing high-quality resources : Link**
• Full-Stack Data Science Resources by Youssef Hosni GitHub : Link**
• Awesome AI Data by Youssef Hosni GitHub : Link
• Full-Stack Data Science by Simplilearn YT : Link
• AI & ML Full Course by Simplilearn : Link
• Full-Stack- 100 days of Python, ML DL by CampusX : Link
• Full-Stack Himanshu R. GitHub : Complete Roadmap
• Full-Stack Andrew NG applied AI YT : Link
• Full-Stack by Edureka : Link
• Full-Stack by Rune : Link
• Asish Patel LinkedIn : Link
• Sentdex YT : Link
• Statquest with Josh Starmer YT : Link
• Resources for Serious ML : Link

_____________________________

8. Computer Vision/ Deep Learning Algorithms

• Andrew Ng : Link
• Complete Deep Learning Krish Naik : Link**
• Deep Learning Live Krish Naik : Link**
• Deep Learning in 5 Hours Krish Naik : Link ?
• Object Detection Architectures : Link
• Understanding Deep Learning : Link

_____________________________

9. NLP (Natural Language Processing)

• Complete NLP Krish Naik : Link**
• NLP Live Krish Naik : Link**
• Sentdex : Link

________________________________

10. Hands-on end-to-end ML/ DL/ AI/ Data Science Projects

• Data Science Projects by Krish Naik : Link**
• 7-days End-End Project by Krish Naik : Link**
• Nicholas Renotte : Link**
• End-End on TowardsDataScience : Link
• 50 ML Projects List : Link

_________________________________

11. MLOps Resources

• MLOps by Krish Naik : Link
• Best MLOps Architecture by Krish Naik : Link
• MLOps Roadmap by Asish Patel : Link
• Aurimas Griciunas : Link
• Larysa Visengeriyeva's GitHub 'Awesoem_MLOps' : Link
• 3 Best Resources : Link

___________supporting resources below___________

12. Free Blogs & Practice Schools for Learning

• Towards Data Science : Link**
• Medium : Link
• Machine Learning Mastery : Link**
• W3Schools : Link**
• GeeksForGeeks : Link**
• Kaggle : Link
• Clever Programmer : Link
• Google Developer : Link
• Learning Path : Link
• JavaTPoint : Link
• Daily Dose of Data Science by Avi Chawla : Link
• Books Soft Copy Sources: pdfdrive Link, debugzilla Link

________________________________

13. Interview & Resume - Portfolio

• Story-telling During Interview by Krish Naik : Link**
• How to Explain Projects by Krish Naik : Link
• Interview Questions : Link**, Relevant Video
• 450+ Interview Q/A : Link & Questions
• Coding Before Interview : Link
• Common Interview Coding Topics : Link
• 45 Python Interview QA : Link
• 45 ML Interview QA : Link
• 170 Interview QA : Link
• Interview Resource : Link
• John Washam GitHub : Link
• How to create a Data Science portfolio : Link
• Describe A Data Science Project : Link
• Netflix Success Tips : Link
• Best Strategy for Coding Interview : Link *
• Deep Learning Interview Book: Link
• Data Science Interview Prep : Link
• Python Interview Prep : Link
• Only Interview Resources Ever Needed: Link
• 45 Common Coding Interview Questions : Link
• Python Interview Questions: Link
• Software Engineering Interview : Link
• Resume, Interview Prep, & More Callbacks (saved in professional folder in laptop) : Link
• Statistics for Interview : Link
• Resume Template for Giants with Remote Job Boards : Link
• InterviewMaster - DSA, Leetcode, & System Design Tips by Sahil; Subscription : Link
• Google Interview Overview : Link
• FAANG/MAANG Interview Preparation 1 : Link
• FAANG/MAANG Interview Preparation 2 : Link
• Pre-Interview Cheat Sheet : Link
• Machine Learning Interview Questions : Link

_________________________________

14. Some pioneering GitHub & LinkedIn profiles to follow

• Roadmaps for Tech Stacks : Link and Website
• Krish Naik : GitHub
• Asish Patel : GitHub & LinkedIn
• Ambarish Singh LinkedIn : Link & GitHub
• Assignments on GitHub : Link & Link
• Nicholas Renotte : GitHub
• Arpit Singh : LinkedIn
• Avi Chawla's Daily Dose of Data Science : GitHub
• Himanshu Ramchandani : LinkedIn
• 5 Best GitHub Repo for AI / ML : Link

_________________________________

15. Codeless Instant AI Implementation

• Obviously AI : Link
• Weka IO : Link

_________________________________

16. Virtual Environment & Competitive Programming

- Virtual Coding Environment

• Codesphere
• Google Colab
• iNeuron's NeroLab : Link **
• Codecademy
• SoloLearn Mobile App
• Jovian for Data Science : Link

- Coding Q/A

• GeeksForGeeks Practice : Link
• Leetcode Problems for Interviews : Link**
• Leetcode Blind 75 Solutions - Neetcode YT : Link
• Important Leetcode Problems : Link
• Hackerrank Problem Solving : Link
• PrepByte Beginner Python Problem Solving : Link
• W3Schools Exercises : Link**

- Competitive Programming

• Coding Game (enrolled) : Link
• Top 10 Prestigious : Link
• Top 15 Websites : Link

________________________________

17. Popular Books & Courses

- Recommended Books

• Deep Learning with Python, 2nd Ed. by Francois Chollet : Portfolio Link
• Computer Vision: Algorithms and Applications, 2nd Ed. by Richard Szeliski : Link
• The Best Book For Learning by Krish Naik (I have their PDFs) : Link
• 5 Recommended Books : Link
• Recommended Programming Books : Link
• Recommended Books : Link
• Books Referred by 365 DS : Link
• Books Facebook eShop : Link

- Research Papers

• Shared by Ilya from OpenAI : Link
• Recommendation Systems : Link

- Other Resources

• 100+ best free DS books : Link
• 100+ best ML books : Link
• 100+ cheat sheet for DS & ML : Link
• 5 Best Books : Link
• 100 Days Data Engineering : Link
• Data Engineering Videos : Link
• 9 Kaggle Certifications : Link**
• Books Recommended by Simplilearn : Link
• AI Basics - Free Course by Simplilearn : Link
• ML Basics - Free Course by Simplilearn : Link
• Google AI Courses : Link**
• IBM Data Science Professional Certificate by Coursera
• Tutort’s Academy AI and ML Course
• Data Science with R and Python by Udemy
• The Full Stack Data Scientist Bootcamp by Udemy : Removed
• Introduction to Data and Data Science by 365 Data Science
• Articles by Youssef Hosni : Link
• Frew Data Engineering Courses : Link
• Resources Shared by Youssef Hosni : Link
• Google's Free Courses : Link
• 2023 Google's 15 Free Courses : Link
• Multi-purpose AI Resources : Link
• 5 Best Courses that Helped Enter Google : Link
• Top Free Courses for 2024 : Link
• Python, SQL - Data Engineering Full : Link
• NVIDIA Courses on AI : Link
• Google AI Courses : Link
• IBM 21 Courses : Link
• CircleFTP Udemy Courses & Bootcamps : Link
• Data Engineering Courses : Link
• ML. School suggested by Ketul : Link **
• AI for Accelerated Learning by Razven, uDemy : Link

- Krish Naik uDemy Courses

• Krish Naik Complete ML : Link **
• Krish Naik Complete Python & DSA : Link
• Krish Naik Complete GenAI : Link

- Online Books (Bangladesh)

• House of Words : Link **
• NBT : Link
• LBL : Link
• BookBar : Link

________________________________

18. Career & Jobs : Best Tech Organisations to Serve

• List of Companies with URL : Link
• Companies Hiring without DSA Skills : Link , DirectList , DirectList , JobBoard **
• Remote Friendly Companies : Link
• AI Virtual Internships : Link
• Companies with Relocation : Link
• Jobs with Relocation : Link
• Data Engineering Employment : Link
• Job Boards Remote : Link
• No#1 AI Jobboard : Link
• Remote Job Boards with Resume Template for Giants : Link
• Fresh Hiring & Remote Jobs : Link
• AI Tech & Company - Written by Meri Nova : Link
• Ycombinator Startup Directory - Leads : Link **
• GeekWire Recent Funded Companies : Link
• Companies Hiring Freshers : Link , Hiring Off-campus Link
• Referrals Template : Link

________________________________

19. Free Hosting Sites

• https://netlify.com
• https://firebase.google.com
• https://aws.amazon.com
• https://heroku.com
• https://pages.github.com
• https://vercel.com
• https://surge.sh
• https://render.com
• https://docs.gitlab.com/ee/user/project/pages

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20. Videos

- Best 7 Unique YT Channels for AI

• Events & talks, research release, robotics : OpenAI
• NLP for Developers, Developing Contextual AI assistants with Rasa tools, Algorithm Whiteboard, Live Coding : Rasa
• PyTorch, TensorFlow, ML Algorithms : Aladdin Persson
• Two Minute Papers, AlphaGo, Fluid, Cloth and Hair Simulations, AI and Deep Learning, Light Transport, Ray Tracing, and Global Illumination : Two-Minute Papers
• Introduction to Machine Learning playlists, Data analysis in Python with Pandas : Data School
• Deep Learning Paper Summaries, Reinforcement Learning, Generative Adversarial Networks, Neural Network Design : Connor Shorten
• Deep Learning from the Foundations, Introduction to Machine Learning for Coders, Practical Deep Learning for Coders : Jeremy Howard

- 5 Videos : Link

- Best 7 YT Channels for AI : Link

- 100+ YT Channels for AI

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21. Generative AI / LLM

- LangChain Playlist by Krish Naik : Link **

- Complete Transformers by Krish Naik : Link **

- Generative AI Resources by YH : Link

- Stanford Transformer YouTube : Link

- Complete LLM Resources : Link

- OpenAI API Tutorials : Link

________________________________

22. AIoT

• Benefits & Techniques of ML Embedded : Link
• IoT Book Resources : Link

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#AItips1

Well, being a data scientist is knowing everything, like an all rounder and understand business problems as well.

Go for it later always,

You should actually know everything over the years, do one thing at a time,

Path: Data Analyst -> ML -> MLOps -> ML System Design & Architecture -> Data Scientist -> AI specialist (Leadership role)

............

#AItips2

Data Science & ML comes under computer science.

So if you know software development it will be a great+ as ML or AI is part of the whole software solution.

Example - website will be a software with frontend and backend capabilities, but a chatbot is just a part of it, which is an AI.

Software development: coding, DSA, version control, front-end development, backend development, database, API; cloud, containerization & deployment; testing, etc.

While ML development: statistics, mathematics, ML algorithm, training & testing of model added with SWD.

Best way to learn is Software Development > Statistics > ML > Data Science

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Machine Intelligence is the new future! Happy Exploring, Cheers!!

Collated by: Navid Bin Ahmed, 22-Dec 22, GitHub