Fundamentals of Machine Learning

Learning Outcome

Understanding machine learning basic algorithms, focusing on the concepts and experimenting them yourself using interactive Jupyter Notebooks. The following topics are covered:

content

Regression:

Linear and Regularized

Regression (Lasso, Ridge, ElasticNet)

Bias-Variance Trade-Off

Gradient Descent

Decision Trees, Random Forests and Gradient Boosted Trees

Classification:

Logistic Regression

Evaluation Metrics (ROC and PR Curves, Confusion Matrix)

Support Vector Machines

and also;

Linear Algebra (Matrix transformations, Eigenvectors and Eigenvalues)

Principal Component Analysis (PCA)

K-Nearest Neighbors (KNN)

Clustering with K-Means

Intro to Neural Networks: Activation Functions