This resources are compiled of materials that I consider good and are mostly freely accessible to everyone. I hope anyone taking thier journey into learning ML/DL or other to have a smooth ride with top-class study material without burning thier pockets. None of them are affilated or anything. I'll keep updating as newer resources are build/found.
- Learing and Practicing Programming Languages: Python, C++, JavaScript, Practicing.
- Machine Learning: General ML, Deep Learning, Computer Vision, NLP, Pytorch, Tensorflow, Applied ML, ML Competitions, ML Interview prep, ML from scratch.
- Web Development: Complete Courses, Specifics.
- Extras: Other Resources, Projects, Newsletters, Youtube Channels.
- Byte of Python beginner for python.
- Think Python also a very good introductory.
- Programmer's guide to Python by me takes you to a fast python ride.
- Automate the Boring Stuff By Al Sweigart takes you from basic to someone good.
- WTF Python exploring and understanding Python through surprising snippets.
- Leetcode, HackkerRank.
- Interview prep by Freecodecamp
- Data Structures
- Blind-75 Coding Problems and Leetcode Patterns
- Interviewcake
- Elements of AI kind of AI for everyone course, easy and covers overview of AI concepts.
- Machine Learning by Andrew NG legendary course to get started.
- ML Course by ODS has very well designed curriculum to boost/build your ML intuition.
- Stat 451: Intro to Machine Learning (Fall 2020) by Sebastian Raschka
- The Hundred-Page Machine Learning Book by Andriy Burkov is easily the best go-to for beginners, it is free, short, yet comprehensive. Folks with experience can also benefit from this awesome book. Author is very generous to keep the book "READ FIRST, BUY LATER" kind and has new book Machine Learning Engineering which is simply the next best thing, tons of real-world ML examples which can take you far.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 2/Amazon.com by Aurelien Geron is an excellent primer, yet provides deeper understanding of concepts in General ML, Deep Learning techniques used around Computer Vision, NLP. It supplies sufficient code for using them with sklearn, Tensorflow.
- FastAi: Deep Learning by Jeremy Howard certainly best place to kick-start deep learning journey.
- Deep Learing with Pytorch best deep learing practices using pytorch.
- MIT 6.S191: Introduction to Deep Learning
- Deep Learning Course by DeepMind
- Deep Learning spcialization by Andrew NG Youtube version ->1,2,3,4,5
- Dive into Deep Learning is well thought and written overall. This book has a wide array of techniques starting from basic mathematics to GANs, providing code for almost everything(has pytorch and tensorflow 2 both). Contents are continually updated by the authors. Overall this book is a gold-mine for a ML-Engineer and is free.
- Deep Learning With python(2nd Edition) by the creator of Keras covers building blocks of Deep Learning with easy language and code supplied throughtout is a very nice package overall. Also this is upcoming new edition, you can read for free on Manning.
- Computer Vision by M.Shah covers Classic/Traditional Computer Vision techniques and builds a strong foundation. I have created notebooks accompanying some material in this course here in Image Processing.
- First Principles of Computer Vision new take on Traditional Computer Vision techniques.
- Deep Learning for Computer Vision covers the modern techniques of using Deep Learning to tackle Computer Vision challenges. Also provides fruitful Deep Learning knowledge.
- Image Processing with Opencv learn images processing tutorials from opencv.
- PyimageSearch has curated list of great tutorials which are totally worth checking out.
- Natural Language Processing by Amazon A soft introduction into NLP.
- Natural Language Processing Specialization Comprehensive course put up by deeplearning.ai, which builds a solid ground to start building your NLP applications.
- HuggingFace Course great intro to use transformers library.
- CS224N: Natural Language Processing with Deep Learning 2020
- CMU Neural Nets for NLP 2021
- A Code-First Introduction to NLP by fast.ai
- NLP for Developers
- NLP course for you fair introduction to NLP techniques using clear diagrams.
- NLP by jacob eisenstein
- Neural Network Programming - Deep Learning with PyTorch easy to understand course by DeepLizard.
- Deep Neural Networks with PyTorch good course by coursera.
- Eat Pytorch in 20 days (translated to English) builds a strong hands-on of syntax and general usage of Pytorch.
- Deep Learning with PyTorch takes the approach of Deep Learing with Python but using Pytorch. Also it is free to read on Manning.
- Deep Learning with PyTorch: A 60 Minute Blitz basic workaround pytorch.
- Keras - Deep Learning beginner's course very easy language, rewarding for beginner.
- Eat TF 2.0 in 30 days builds a strong hands-on of syntax and general usage of Tensorflow.
- ML Foundations + Applied ML neat ML basics and Applied ML techniques.
- Full Stack Deep Learning(new 2021) deep learning to production.
- MLOPS basic
- Kaggle best place to learn/apply deep learning skills.
- mlcontests list of all ongoing competitions.
- Omdena platform to create projects, get paid, get hired.
- Zindi participate in competitions.
- Awesome Data Science Interview Resources
- Question Answers, More Question Answer
- Practice ML Questionaries
- Web-Dev-For-Beginners simple begineer for web-dev.
- Fullstackopen learn modern web development practices.
- FullStackPython quick walkthrough into python web-development.
- Learn Git
- Introduction to SQL
- Introduction to Pandas
- Matplotlib, Pandas, Numpy, Seaborn crash course
- AI,ML,DL overview
- Linear_Algebra_With_Python
- Crash Course Numpy, Learn Numpy, More Numpy Tutorials
- Learn Pandas
- Deep learning weekly
- The Batch by deeplearning.ai
- HuggingFace Newsletter
- Andriy Burkov - True Positive Weekly
- NLP News by Sabastian Ruder