This repository contains free resources and a roadmap to learn Machine Learning and Artificial Intelligence in 2025.
- 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
- 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
- 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
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Build a Strong Math Foundation
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Learn Python and Core Libraries
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Learn Machine Learning Fundamentals
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Building Practical Experience
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Deepen Knowledge in Specialized Areas
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Learn about MLOps
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AI Research and Continuous Learning
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Prepare for AI/ML Job Interviews
- 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
- AWS Certified Machine Learning Engineer – Associate
- Microsoft Certified: Azure AI Engineer Associate
- Stanford AI and Machine Learning Certificate
- 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
- OpenAI Blog
- Google DeepMind
- Google Research
- Apple ML Research
- Amazon Science
- Microsoft AI
- Meta AI Blog
- AWS Machine Learning Blog
- NVIDIA - Deep Learning Blog
- AirBnB Engineering, AI & ML
- Spotify Engineering
- Uber Engineering
- Netflix Blog
- Google AI
- Matrix times Vector
- Titanic: Machine Learning from Disaster
- Predicting House Prices Using Linear Regression
- Decision Tree Learning
- Implement a Simple RNN with Backpropagation
- Generative Adversarial Networks (GANs) for Image Synthesis
- Attention Is All You Need (Google)
- DeepSeek R1: Incentivizing Reasoning Capability in LLMs
- 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
Your open-source contributions to improve this resource are welcome and appreciated!