A Comprehensive Introduction to Machine Learning Fundamentals

I’ve been writing a collection of tutorials on fundamental machine learning concepts. Below the relevant tutorials are linked and structured in the format of what constitutes an introductory course in machine learning. This curriculum should take you from having basically no familiarity with machine learning to a practitioner’s level of understanding. Enjoy!

Supervised Learning

• Linear Regression
• Logistic Regression
• Naive Bayes
• Support Vector Machines
• Decision Trees
• K-Nearest Neighbors

Machine Learning in Practice

• Bias-Variance Tradeoff
• How to Select a Model
• How to Select Features
• Regularizing Your Model
• Ensembling: How to Combine Your Models
• Evaluation Metrics

Unsupervised Learning

• Market Basket Analysis
• K-Means Clustering
• Principal Components Analysis

Deep Learning

• Feedforward Neural Networks
• Grab Bag of Neural Network Practices
• Convolutional Neural Networks
• Recurrent Neural Networks

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