Machine Learning Videos

Machine Learning Fundamentals

A friendly introduction to machine learning

A friendly survey through some of the main algorithms in machine learning with easy examples. Regression, classification, and clustering are discussed.

Machine Learning: Testing and error metrics

A survey of the main concepts used in training ml models such as testing, precision/recall, under/overfitting, model complexity graphs, and grid search, using simple examples. Godzilla makes an appearance.

Linear Models

Linear Regression

A friendly introduction to the linear regression, using a trick that moves lines closer to points (no calculus). A housing price example is discussed.

Logistic Regression and the Perceptron Algorithm

A friendly introduction to the perceptron algorithm, using a trick that moves lines closer to points (no calculus). A spam detection example is used.

Support Vector Machines

A simple explanation of support vector machines (SVM), where we develop an algorithm to train them, without using calculus, only lines that move and separate.

Neural Networks

Neural Networks

A friendly and very graphical explanation of neural networks, using probabilities and gradient descent.

Convolutional Neural Networks

A simple explanation of convolutional neural networks using a simple example of image recognition.

Recurrent Neural Networks

A friendly introduction to recurrent neural networks using a cooking example.

Unsupervised Learning


A friendly explanation of K-means and Hierarchical clustering using a marketing example and an example of locating restaurants in a map. Elbow method and dendrograms discussed as well.

Principal Component Analysis (PCA)

A friendly explanation of mean, variance, covariance, eigenvectors, eigenvalues, and dimensionality reduction.


Matrix Factorization and Netflix Recommendations

Matrix factorization explained using a simple example of 4 users generating ratings for 5 movies.


Probabilistic Models

Bayes Theorem and the Naive Bayes Algorithm

Bayes theorem and the naive Bayes algorithm explained using a friendly spam detection example.


Hidden Markov Models

Bayes theorem, hidden Markov models and the Viterbi paths algorithm explained using a simple example of guessing the weather based on someone’s mood.

Code in Github

Shannon Entropy and Information Gain

A simple explanation of entropy and information gain by using an example of picking balls from boxes.

Accompanying blog post

Natural Language Processing

Latent Dirichlet Allocation (part 1 of 2)

A powerful NLP technique used to sort documents by topic.

In the first video we study Dirichlet distributions and the blueprint for LDA.

Latent Dirichlet Allocation – Gibbs Sampling (part 2 of 2)

A powerful NLP technique used to sort documents by topic.

In the second video we study a way to train LDA models using Gibbs sampling.

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