Links to posts about machine learning and data science:
- Introduction: What is machine learning?
- “To all you that are trying to tell people they can become professionals in just a few weeks JUST to sell your product – shame on you!” (A meta-video by another software developer that says a lot of what I’ve wanted to say)
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
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?