Machine Learning roadmap

What you will learn

1. 1. ML Foundations

   Supervised and unsupervised learning, training and validation, and the metrics used to judge models.

   • Supervised & Unsupervised Learning — Machine Learning · Supervised & Unsupervised Learning
   • Training, Validation & Cross Validation — Machine Learning · Training, Validation & Cross Validation
   • Precision, Recall, F1 & ROC — Machine Learning · Precision, Recall, F1 & ROC

2. 2. Supervised Models

   Regression and classification, the bias-variance trade-off and regularization, and tree ensembles.

   • Regression & Classification — Machine Learning · Regression & Classification
   • Bias, Variance & Regularization — Machine Learning · Bias, Variance & Regularization
   • Decision Trees, Random Forest & Boosting — Machine Learning · Decision Trees, Random Forest & Boosting

3. 3. Features, Clustering & Neural Networks

   Engineer better features, find structure with clustering and PCA, and train neural networks.

   • Feature Engineering — Machine Learning · Feature Engineering
   • Clustering & PCA — Machine Learning · Clustering & PCA
   • Neural Networks — Machine Learning · Neural Networks