Duration: 5 Days

Machine Learning (ML) Overview

• Machine Learning landscape

• Machine Learning applications

• Understanding ML algorithms and models (supervised and unsupervised)

Feature Engineering (FE)

• Preparing data for ML

• Extracting features and enhancing data

• Data cleanup

• Visualizing Data

• Exercise: data cleanup

• Exercise: visualizing data

• Linear regression

• Simple Linear Regression

• Multiple Linear Regression

• Running LR

• Evaluating LR model performance

Logistic Regression

• Understanding Logistic Regression

• Calculating Logistic Regression

• Evaluating model performance

Classification: SVM (Supervised Vector Machines)

• SVM concepts and theory

• SVM with kernel

Classification: Decision Trees and Random Forests

• Theory behind trees

• Classification and Regression Trees (CART)

• Random Forest concepts

Classification: Naive Bayes

• Theory behind Naive Bayes

• Running NB algorithm

• Evaluating NB model

Clustering (K-Means)

• Theory behind K-Means

• Running K-Means algorithm

• Estimating the performance

Principal Component Analysis (PCA)

• Understanding PCA concepts

• PCA applications

• Running a PCA algorithm

• Evaluating results

Recommendation (collaborative filtering)

• Recommender systems overview

• Collaborative Filtering concept