About This Course

This course is jam-packed with everything you need to get started with machine learning. We will look at various algorithm types, such as linear regression, logistic regression, decision trees, random forest, ensembled models, and beyond. Each model type will have lectures devoted to conceptualizing its mechanics, with some easy-to-follow math as well. 

In addition to conceptual lectures, there will be accompanying videos on key data science theory to improve accuracy and avoid common model pitfalls. Finally, each model type will have a coding video in Python to demonstrate the algorithm in action. We will also discuss common tips and tricks for cleaning data in preparation for machine learning, as well as an overview of machine learning in R.

Topics that will be covered

  • Simple Linear Regression
  • Multiple Linear Regression
  • Logistic Regression
  • Decision Tree
  • Random Tree
  • Model Ensembling And Unsupervised Learning
  • Data Cleaning
  • Additional Machine Learning in R

Who is this course suitable for?

Anyone who is looking to understand more about Machine Learning with a view to break into a career in Data Science. Equally, you may be a professional working in tech or finance and want to learn new analytical techniques that can enhance your overall skill set.

Our Promise to You

By the end of this course, you will have learned about machine learning.

30 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked. 

Get started today and learn more about machine learning.

Course Curriculum

Section 1 - Simple Linear Regression
Simple Linear Regression – Theory 00:00:00
Simple Linear Regression – Code 00:00:00
Section 2 - Multiple Linear Regression
Multiple Linear Regression – Theory 00:00:00
Multiple Linear Regression – Code 00:00:00
Assumptions of Linear Regression – Theory 00:00:00
Curse of Dimensionality – TheoryV 00:00:00
Model Accuracy and Train Test Split – Code 00:00:00
Section 3 - Logistic Regression
Logistic Regression – Theory 00:00:00
Logistic Regression – Code 00:00:00
Section 4 - Decision Tree
Decision Tree – Theory 00:00:00
Cross-Validation And Hyperparameter Tuning – Theory 00:00:00
Decision Tree Classifier – Code 00:00:00
Decision Tree Classifier with Hyperparameter Tuning – Code 00:00:00
Decision Tree Regressor – Code 00:00:00
Decision Tree Regressor with Hyperparameter Tuning – Code 00:00:00
Section 5 - Random Forest
Random Forest – Theory 00:00:00
Random Forest Classifier – Code 00:00:00
Random Forest Classifier with Hyperparameter Tuning – Code 00:00:00
Random Forest Regressor – CodeV 00:00:00
Random Forest Regressor with Hyperparameter Tuning – Code 00:00:00
Section 6 - Model Ensembling And Unsupervised Learning
Ensembling Models – Theory 00:00:00
Model Ensembling – Code 00:00:00
Unsupervised vs. Supervised Learning + Summary of NLP and Deep Learning – TheoryV 00:00:00
Section 7 - Data Cleaning
Stepwise Selection – Theory 00:00:00
Stepwise Selection – Code 00:00:00
Label Encoding – Theory 00:00:00
Label Encoding – Code 00:00:00
5. Dummification – Theory 00:00:00
Dummification – Code 00:00:00
KNN – Theory 00:00:00
KNN – Code 00:00:00
Data Cleaning Walkthrough One (Housing Prices) – Code 00:00:00
Data Cleaning Walkthrough Two (Titanic) – Code 00:00:00
Section 8 - Additional Machine Learning in R
Simple Linear Regression in R – Code 00:00:00
Simple Linear Regression in R – Code 00:00:00
Logistic Regression in R – Code 00:00:00
Decision Tree in R – Code 00:00:00
Random Forest in R – Code 00:00:00
6. R Data Cleaning Example Part One – Code 00:00:00
7. R Data Cleaning Example Part Two – Code 00:00:00
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