Links to posts about machine learning and data science:

• Introduction: What is machine learning?

Supervised Learning

• Linear Regression
• Linear Regression FULL VIDEO COURSE
• Multiple Linear Regression
• Linear Programming for Linear Regression
• Bayes Classifier and Naive Bayes Tutorial
• Tutorial: K-Nearest Neighbor classifier for MNIST
• Deep Learning Tutorial part 1/3: Logistic Regression
• Logistic Regression FULL VIDEO COURSE
• Deep Learning Tutorial part 2/3: Artificial Neural Networks
• Deep Neural Networks FULL VIDEO COURSE
• Deep Learning Tutorial part 3/3: Deep Belief Networks
• Practical Deep Learning in Theano and TensorFlow FULL VIDEO COURSE
• Convolutional Neural Networks and Computer Vision in Theano and TensorFlow FULL VIDEO COURSE
• A Tutorial on Autoencoders
• Decision Tree Tutorial
• Ensembles:
  • Random Forest Tutorial (and Bagging)
  • AdaBoost Tutorial
  • XGBoost (frequent Kaggle winner)

Unsupervised Learning

• K-Means Clustering, Hierarchical Clustering, and Gaussian Mixture Models FULL VIDEO COURSE
• K-Means Clustering
• Bayesian Bandit Tutorial
• A Tutorial on Autoencoders
• Principal Components Analysis (PCA)
• Principal Components Analysis in Theano

Recommendations / Recommender Systems

• Collaborative Filtering and Matrix Factorization
• Non-negative Matrix Factorization

Practical Issues:

• Probability Smoothing for Natural Language Processing (NLP)
• Cross-validation
• How to run distributed machine learning jobs using Apache Spark and EC2 (and Python)
• Covariance Matrix: Should you divide by N or N-1?