Awesome AI/ML Resources

This repository contains curated free resources and a roadmap to learn Machine Learning and Artifical Intelligence in 2025.

📌 AI/ML Key Concepts

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Generative adversarial networks (GANs)
• Dimensionality Reduction
• Clustering Algorithms
• Bayesian Inference
• Ensemble Learning
• Explainable AI (XAI)
• Time Series Analysis
• Self-Supervised Learning

🛠️ AI/ML Building Blocks

• Linear Algebra for Machine Learning
• Probability & Statistics
• Calculus for Optimization
• Python for Machine Learning
• Optimization Techniques
• Data Preprocessing & Feature Engineering
• Model Evaluation & Metrics
• Regularization Techniques
• Loss Functions
• Activation Functions
• Hyperparameter Tuning

👨🏽‍💻 AI/ML Roles

• Machine Learning Engineer
• Data Scientist
• Software Engineer (AI)
• ML/AI Platform Engineer
• ML/AI Infrastructure Engineer
• Framework Engineer
• Solution Architect
• Developer Advocate
• Solutions Engineer
• Applied Research Scientist
• Research Engineer
• Research Scientist

🚗 AI/ML Roadmap

1. Build a Strong Math Foundation

   • Linear Algebra
   • Probability & Statistics
   • Calculus

2. Learn Python and Core Libraries

   • Intro Python
   • Advanced Python
   • NumPy: Numerical computing and arrays
   • Pandas: Data manipulation and analysis
   • Matplotlib & Seaborn: Data visualization
   • scikit-learn: Implement ML algorithms

3. Learn Machine Learning Fundamentals

   • Google Machine Learning Crash Course
   • Machine Learning by Andrew Ng
   • Read Hundred-Page ML Book

4. Building Practical Experience

   • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
   • Practical Deep Learning for Coders
   • Structured Machine Learning Projects
   • Build GPT

5. Deepen Knowledge in Specialized Areas

   • Natural Language Processing
   • Reinforcement Learning
   • Computer Vision
   • Deep Learning
   • Transformers

6. Learn about MLOps

   • Intro to MLOps
   • Three levels of ML
   • Fullstackdeeplearning

7. AI Research and Continuous Learning

   • ArXiv for Research Papers

8. Prepare for AI/ML Job Interviews

   • Introduction to Machine Learning Interviews
   • Designing Machine Learning Systems

📚 Courses

• Machine Learning by Andrew Ng (Coursera)
• AI For Everyone by Andrew Ng (Coursera)
• Deep Learning Specialization (Coursera)
• Machine Learning with Python (edX - IBM)
• Reinforcement Learning Specialization (Coursera)
• CS231n: Convolutional Neural Networks for Visual Recognition (Stanford)
• RL Course by David Silver
• Natural Language Processing with Deep Learning (Stanford - CS224n)
• Fast.ai’s Practical Deep Learning for Coders

🎓 Certifications

• AWS Certified Machine Learning Engineer – Associate
• Microsoft Certified: Azure AI Engineer Associate
• Stanford AI and Machine Learning Certificate

📕 Books

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• AI Engineering: Building Applications with Foundational Models
• Introduction to Machine Learning Interviews
• Designing Data Intensive Applications
• Designing Machine Learning Systems
• Deep Learning

🛠️ Tools & Frameworks

• PyTorch
• TensorFlow
• Scikit-Learn
• XGBoost
• Keras
• Perplexity
• CursorAI
• Whisper

AI/ML Research Blogs

• OpenAI Blog
• Google DeepMind
• Google Research
• Apple ML Research
• Amazon Science
• Microsoft AI
• Meta AI Blog

AI/ML Applied Blogs

• AWS Machine Learning Blog
• NVIDIA - Deep Learning Blog
• AirBnB Engineering, AI & ML
• Spotify Engineering
• Uber Engineering
• Netflix Blog
• Google AI

AI/ML Problems

Easy

• Matrix times Vector
• Titanic: Machine Learning from Disaster
• Predicting House Prices Using Linear Regression

Medium

• Single Neuron
• K-Means Clustering
• Predicting Loan Default Risk
• Sentiment Analysis on Movie Reviews

Hard

• Decision Tree Learning
• Implement a Simple RNN with Backpropagation
• Generative Adversarial Networks (GANs) for Image Synthesis

⚡️ AI/ML Communities

• r/LearnMachineLearning
• Chip Huyen MLOps Discord
• Hugging Face Discord

📺 Youtube Channels

• Stanford Online
• Andrej Karpathy
• FreeCodeCamp
• 3Blue1Brown
• Sentdex

📩 Newsletters

• Rundown AI
• AI with Arman

📃 Must Read Papers

• Attention Is All You Need (Google)
• Monolith: Real Time Recommendation System (TikTok/ByteDance)
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Understanding Deep Learning Requires Rethinking Generalization
• Playing Atari with Deep Reinforcement Learning
• Distilling the Knowledge in a Neural Network
• Open AI Key Papers in Deep RL