ratings
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.
Unlimited Duration
March 3, 2021
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
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- Machine Learning landscape 00:00:00
- Machine Learning applications 00:00:00
- Understanding ML algorithms & models (supervised and unsupervised) 00:00:00
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- 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
- 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
- SVM concepts and theory 00:00:00
- SVM with kernel 00:00:00
- Use case: Customer churn data 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
- 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
- Hands-on guided workshop utilizing skills learned throughout the course 00:00:00
Course Reviews
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