Machine Learning Essentials With R

Machine Learning (ML) Overview

• Machine Learning landscape
• Machine Learning applications
• Understanding ML algorithms & models (supervised and unsupervised)

Machine Learning Environment

• Introduction to Jupyter notebooks / R-Studio
• Exercise: Getting familiar with ML environment

Machine Learning Concepts

• Statistics Primer
• Covariance, Correlation, Covariance Matrix
• Errors, Residuals
• Overfitting / Underfitting
• Cross validation, bootstrapping
• Confusion Matrix
• ROC curve, Area Under Curve (AUC)
• Exercise: Working with Basic Statistics

Feature Engineering (FE)

• Preparing data for ML
• Extracting features, 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
• Exercise / Use case: House price estimates

Logistic Regression

• Understanding Logistic Regression
• Calculating Logistic Regression
• Evaluating model performance
• Use case: credit card application, college admissions

Classification : SVM (Supervised Vector Machines)

• SVM concepts and theory
• SVM with kernel
• Use case: Customer churn data

Classification : Decision Trees & Random Forests

• Theory behind trees
• Classification and Regression Trees (CART)
• Random Forest concepts
• Exercise / Use case: predicting loan defaults, estimating election contributions

Classification : Naive Bayes

• Theory behind Naive Bayes
• Running NB algorithm
• Evaluating NB model
• Exercise / Use case: spam filtering

Clustering (K-Means)

• Theory behind K-Means
• Running K-Means algorithm
• Estimating the performance
• Exercise / Use case: grouping cars data, grouping shopping data

Principal Component Analysis (PCA)

• Understanding PCA concepts
• PCA applications
• Running a PCA algorithm
• Evaluating results
• Exercise / Use case: analyzing retail shopping data

Recommendation (Collaborative filtering)

• Recommender systems overview
• Collaborative Filtering concepts
• Use case: movie recommendations, music recommendations

Time Permitting: Capstone Project

• Hands-on guided workshop utilizing skills learned throughout the course