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