Machine Learning Cheatsheet

Prerequisite Knowledge

• Linear Algebra
• Probability theory
• Calculus
• Optimisation Methods

Machine Learning Algorithms

Common ML algorithms (examples coming soon)!

• Deep learning (MLP, CNN, RNN, LSTM)
• Linear regression,Multiple regression,logistic regression]
• Random Forests
• Support vector machine (SVM) with kernels (Linear, Poly, RBF)
• K-Means
• Gaussian Mixture Model
• K-nearest neighbors
• Naive bayes
• Principal component analysis (PCA)
• Factorization machines
• Restricted Boltzmann machine (RBM)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Gradient Boosting trees (also known as GBDT, GBRT, GBM, XGBoost)
• Reinforcement learning (Deep Q learning)

Beginner Books

• Real World Machine Learning [Free Chapters]
• An Introduction To Statistical Learning - Book + R Code
• Elements of Statistical Learning - Book
• Computer Age Statistical Inference (CASI) (Permalink as of October 2017) - Book
• Probabilistic Programming & Bayesian Methods for Hackers - Book + IPython Notebooks
• Think Bayes - Book + Python Code
• Information Theory, Inference, and Learning Algorithms
• Gaussian Processes for Machine Learning
• Data Intensive Text Processing w/ MapReduce
• Reinforcement Learning: - An Introduction (Permalink to Nov 2017 Draft)
• Mining Massive Datasets
• A First Encounter with Machine Learning
• Pattern Recognition and Machine Learning
• Machine Learning & Bayesian Reasoning
• Introduction to Machine Learning - Alex Smola and S.V.N. Vishwanathan
• A Probabilistic Theory of Pattern Recognition
• Introduction to Information Retrieval
• Forecasting: principles and practice
• Practical Artificial Intelligence Programming in Java
• Introduction to Machine Learning - Amnon Shashua
• Reinforcement Learning
• Machine Learning
• A Quest for AI
• Introduction to Applied Bayesian Statistics and Estimation for Social Scientists - Scott M. Lynch
• Bayesian Modeling, Inference and Prediction
• A Course in Machine Learning
• Machine Learning, Neural and Statistical Classification
• Bayesian Reasoning and Machine Learning Book+MatlabToolBox
• R Programming for Data Science
• Data Mining - Practical Machine Learning Tools and Techniques Book
• Machine Learning with TensorFlow Early access book
• Reactive Machine Learning Systems Early access book
• Hands‑On Machine Learning with Scikit‑Learn and TensorFlow - Aurélien Géron
• R for Data Science: Import, Tidy, Transform, Visualize, and Model Data - Wickham and Grolemund. Great as introduction on how to use R.
• Advanced R - Hadley Wickham. More advanced usage of R for programming.
• The Little Book of Deep Learning - François Fleuret ## Resources

• 41 Essential Machine Learning Interview Questions (with answers)

• How can a computer science graduate student prepare himself for data scientist interviews?

• How do I learn Machine Learning?

• FAQs about Data Science Interviews

• Does Balancing Classes Improve Classifier Performance?

• What is Deviance?

• When to choose which machine learning classifier?

• What are the advantages of different classification algorithms?

• ROC and AUC Explained (related video)

• An introduction to ROC analysis

• fast.ai - Practical Deep Learning For Coders

• fast.ai - Cutting Edge Deep Learning For Coders

• A curated list of awesome Deep Learning tutorials, projects and communities

• Udacity - Machine Learning (Georgia Tech)

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