ML Resources
Learning Paths
Beginner Path
Perfect for newcomers to ML
- Step 1 Machine Learning Specialization
- Step 2 Python Fundamentals
- Step 3 Data Analysis with Pandas
Intermediate Path
For those with ML basics
- Step 1 Deep Learning Specialization
- Step 2 PyTorch Tutorials
- Step 3 Kaggle Competitions
Advanced Path
For experienced practitioners
- Step 1 ML Ethics & Deployment
- Step 2 MLOps Specialization
- Step 3 Research Papers & Implementation
Essential Books
Deep Learning
By Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Advanced
800 pages
A comprehensive guide to deep learning principles and mathematics.
Read free online
Hands-On Machine Learning
By Aurélien Géron
Beginner-Friendly
600 pages
Practical guide to implementing machine learning with scikit-learn and TensorFlow.
View materialsTools & Libraries
TensorFlow
An open-source framework for building and deploying machine learning models, particularly neural networks.
Learn MoreScikit-learn
A robust library for data analysis and traditional machine learning techniques.
Learn MoreOnline Communities
Kaggle Forums
A vibrant community for discussing ML projects, competitions, and best practices.
Visit ForumsReddit - r/MachineLearning
Stay updated with the latest research, trends, and discussions in ML.
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