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Machine Learning Essentials with R is an essentials-level, three-day hands-on course that teaches you core skills and concepts in modern ML practices.

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Unlimited Duration

Last Updated

March 3, 2021

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This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. In this course you will learn about:

• Machine Learning (ML) Overview

• Machine Learning Environment

• Machine Learning Concepts

• Feature Engineering (FE)

• Linear Regression

• Logistic Regression

• Classification : SVM (Supervised Vector Machines)

• Classification : Decision Trees & Random Forests

• Classification : Naive Bayes

• Clustering (K-Means)

• Principal Component Analysis (PCA)

• Recommendation (Collaborative filtering)

• Time Permitting: Capstone Project


Course Curriculum

    • Machine Learning landscape 00:00:00
    • Machine Learning applications 00:00:00
    • Understanding ML algorithms & models (supervised and unsupervised) 00:00:00
    • Introduction to Jupyter notebooks / R-Studio 00:00:00
    • Exercise: Getting familiar with ML environment 00:00:00
    • Statistics Primer 00:00:00
    • Covariance, Correlation, Covariance Matrix 00:00:00
    • Errors, Residuals 00:00:00
    • Overfitting / Underfitting 00:00:00
    • Cross validation, bootstrapping 00:00:00
    • Confusion Matrix 00:00:00
    • ROC curve, Area Under Curve (AUC) 00:00:00
    • Exercise: Working with Basic Statistics 00:00:00
    • Preparing data for ML 00:00:00
    • Extracting features, enhancing data 00:00:00
    • Data cleanup 00:00:00
    • Visualizing Data 00:00:00
    • Exercise: data cleanup 00:00:00
    • Exercise: visualizing data 00:00:00
    • Simple Linear Regression 00:00:00
    • Multiple Linear Regression 00:00:00
    • Running LR 00:00:00
    • Evaluating LR model performance 00:00:00
    • Exercise / Use case: House price estimates 00:00:00
    • Understanding Logistic Regression 00:00:00
    • Calculating Logistic Regression 00:00:00
    • Evaluating model performance 00:00:00
    • Use case: credit card application, college admissions 00:00:00
    • SVM concepts and theory 00:00:00
    • SVM with kernel 00:00:00
    • Use case: Customer churn data 00:00:00
    • Theory behind trees 00:00:00
    • Classification and Regression Trees (CART) 00:00:00
    • Random Forest concepts 00:00:00
    • Exercise / Use case: predicting loan defaults, estimating election contributions 00:00:00
    • Theory behind Naive Bayes 00:00:00
    • Running NB algorithm 00:00:00
    • Evaluating NB model 00:00:00
    • Exercise / Use case: spam filtering 00:00:00
    • Theory behind K-Means 00:00:00
    • Running K-Means algorithm 00:00:00
    • Estimating the performance 00:00:00
    • Exercise / Use case: grouping cars data, grouping shopping data 00:00:00
    • Understanding PCA concepts 00:00:00
    • PCA applications 00:00:00
    • Running a PCA algorithm 00:00:00
    • Evaluating results 00:00:00
    • Exercise / Use case: analyzing retail shopping data 00:00:00
    • Recommender systems overview 00:00:00
    • Collaborative Filtering concepts 00:00:00
    • Use case: movie recommendations, music recommendations 00:00:00
    • Hands-on guided workshop utilizing skills learned throughout the course 00:00:00

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