Welcome

Introduction to ML

Statistics I

Statistics II

Installation of nec. packages

Preprocessing I

kNN

kNN Hands-on

Metrics and Evaluation I

Bayes

Hands-on

Feature selection

Hands-on

Metrics and Evaluation II

Decision Trees

Random Forest

Random Forest Hands-on

Regression

Regularization

Regression methods Hands-on

Dimensionality Reduction

Feature Engineering

Dimensionality Hands-on

ML project I: Data handling

ML project II: Supervised learning

ML project III: Supervised learning

Nonlinear Regression

Nonlinear Regression Hands-on

k-Means, DBSCAN

Clustering Hands-on

Clustering Hands-on

Ensembles

Pattern search

Pattern search Hands-on

ML project IV: Unsupervised learning

Kernel methods

Introduction to Neural Networks

Neural Networks Optimization Algorithms

CNN, RNN, LSTM

Fine tuning Neural Networks

Deep learning Hands-on

ML project V: Deep learning

ML project VI: Deep learning

Exam: full ML project

Technical Imnterview