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Useful resources for Machine Learning and Statistics.

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Specialization Certificates

See Certifications for AI, ML, DL, NLP, GANs and LLMs certificates.

References

General

Interpretable Machine Learning: Guide for Making Black Box Models Explainable, Molnar, 2023
The Little Book of Deep Learning, Fleuret, 2023
Rules of Machine Learning: Best Practices for ML Engineering, Zinkevich, 2016
Awesome Data Science

Books

Foundations

Foundations of Machine Learning, Mohri, Rostamizadeh & Talwalkar. The mathematical backbone of ML: algorithms, theory, and how models actually learn.
Understanding Deep Learning, Prince, 2023. Neural networks explained visually and intuitively, from basics to modern architectures.
Deep Learning, Goodfellow, Bengio & Courville, 2016. The definitive deep learning reference, written by the researchers who shaped the field.
Introduction to Machine Learning Systems, Reddi, 2024. How to design and build ML systems that work in production, not just in notebooks.
Algorithms for Optimization, Kochenderfer & Wheeler. The math behind how models improve: gradient methods, search, and decision-making.

Reinforcement Learning

Reinforcement Learning: An Introduction, Sutton & Barto, 2018. The classic RL textbook: how agents learn to make decisions through trial and reward.
Distributional Reinforcement Learning, Bellemare, Dabney & Rowland, 2023. Goes beyond average rewards to model the full distribution of outcomes.
Multi-Agent Reinforcement Learning, Albrecht et al., 2024. How multiple AI agents learn, compete, and cooperate in shared environments.

Probabilistic ML

Probabilistic Machine Learning: An Introduction, Murphy, 2022. ML through the lens of probability: uncertainty, inference, and Bayesian thinking.
Probabilistic Machine Learning: Advanced Topics, Murphy, 2023. Deep dives into probabilistic models, approximate inference, and generative methods.
Machine Learning: A Probabilistic Perspective, Murphy, 2012

Responsible & Agentic AI

Agents in the Long Game of AI, McShane, Nirenburg & English, 2024. How to build AI agents that are trustworthy, hybrid, and designed for long-term reliability.
Fairness and Machine Learning, Barocas, Hardt & Narayanan. Where ML meets society: bias, discrimination, and how to build more equitable systems.

Statistics & Mathematics

All of Statistics: A Concise Course in Statistical Inference, Wasserman, 2004
Pattern Recognition and Machine Learning, Bishop, 2006
The Elements of Statistical Learning, Hastie et al., 2009
An Introduction to Statistical Learning, James, Witten, Hastie & Tibshirani, 2013
Mathematics for Machine Learning, Deisenroth, Faisal & Ong, 2020

Other

Introduction to Machine Learning Interviews Book, Huyen, 2021
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Huyen, 2022
AI Engineering: Building Applications with Foundation Models, Huyen, 2024

Projects

A few projects I worked on:

Classification and recommender systems: Built and compared KNN, decision trees, and SVM classifiers, and implemented collaborative filtering for recommendations.
Anomaly detection: Detected anomalies using multivariate Gaussian models.
Music generation: Trained an LSTM network on a jazz corpus to generate music sequences.
Trigger-word detection: Built a speech recognition model from spectrograms with an attention mechanism.
Object detection and face recognition: Implemented CNN-based vehicle detection with YOLO and face recognition with triplet loss.
Sentiment analysis and duplicate detection: Used Siamese networks for review sentiment and duplicate question detection.
Text summarization and question answering: Fine-tuned Transformer-based models (T5, GPT, BERT) for summarization and chatbot QA.
Neural style transfer and CycleGAN: Generated artistic images with neural style transfer and image-to-image translation with CycleGAN.

Useful Courses

deeplearning.ai * Machine Learning Specialization by Andrew Ng
- Logistic Regression, Regularization
- Neural Networks
- Advice for Applying Machine Learning, Machine Learning System Design
- Support Vector Machines
- Unsupervised Learning, Dimensionality Reduction
- Anomaly Detection, Recommender Systems
- Large Scale Machine Learning
deeplearning.ai * Deep Learning Specialization by Andrew Ng
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models
Udacity * Deep Learning by Google
- From Machine Learning to Deep Learning
- Deep Neural Networks
- Convolutional Neural Networks
- Deep Models for Text and Sequences
IBM * AI Engineering Professional Certificate by IBM
- Machine Learning with Python
- Scalable Machine Learning on Big Data using Apache Spark
- Introduction to Deep Learning & Neural Networks with Keras
- Deep Neural Networks with PyTorch
- Building Deep Learning Models with TensorFlow
- AI Capstone Project with Deep Learning
deeplearning.ai * Natural Language Processing Specialization by Younes Bensouda Mourri and Łukasz Kaiser
- Natural Language Processing with Classification and Vector Spaces
- Natural Language Processing with Probabilistic Models
- Natural Language Processing with Sequence Models
- Natural Language Processing with Attention Models
deeplearning.ai * Generative Adversarial Networks Specialization by Sharon Zhou
- Build Basic Generative Adversarial Networks (GANs)
- Build Better Generative Adversarial Networks (GANs)
- Apply Generative Adversarial Networks (GANs)
deeplearning.ai * Machine Learning Engineering for Production (MLOps) Specialization by Andrew Ng, Robert Crowe, Laurence Moroney
- Introduction to Machine Learning in Production
- Machine Learning Data Lifecycle in Production
- Machine Learning Modeling Pipelines in Production
- Deploying Machine Learning Models in Production
deeplearning.ai * Generative AI with Large Language Models Specialization by Andrew Ng, Robert Crowe, Laurence Moroney
- Generative AI use cases, project lifecycle, and model pre-training
- Fine-tuning and evaluating large language models
- Reinforcement learning and LLM-powered applications
Stanford Online AI curriculum
- CS221 – Artificial Intelligence, Foundations of AI: search, reasoning, planning, decision-making
- CS229 – Machine Learning Full Course, Supervised/unsupervised learning + deep dive into algorithms
- CS230 – Deep Learning, Neural nets, CNNs, RNNs, and best practices for deep models
- CS229M - Machine Learning Theory, The math and proofs behind ML
- CS234 – Reinforcement Learning, MDPs, Q-learning, policy gradients
- CS224N – NLP with Deep Learning, Transformers, embeddings, and modern NLP architectures