Module 1Section 1: Welcome to the course!
Unit 1Welcome to the course!
Unit 2Installing R and R Studio (MAC & Windows)
Unit 3Installing Python and Anaconda (MAC & Windows)
Module 2Section 2: -------------------------- Part 1: Data Preprocessing --------------------------
Unit 1Welcome to Part 1 Preview
Unit 2Get the dataset
Unit 3Importing the Libraries
Unit 4Importing the Dataset
Unit 5Missing Data
Unit 6Categorical Data
Unit 7Splitting the Dataset into the Training set and Test set
Unit 8Feature Scaling
Unit 9And here is our Data Preprocessing Template !
Unit 10Quiz 1 - Data Preprocessing
Module 3Section 3: Part 2 - Regression
There are no units in this module.
Module 4Section 4: Simple Linear Regression
Unit 1Dataset + Business Problem Description
Unit 2Simple Linear Regression Intuition - Step 1
Unit 3Simple Linear Regression Intuition - Step 2
Unit 4Simple Linear Regression in Python - Step 1
Unit 5Simple Linear Regression in Python - Step 2
Unit 6Simple Linear Regression in Python - Step 3
Unit 7Simple Linear Regression in Python - Step 4
Unit 8Simple Linear Regression in R - Step 1
Unit 9Simple Linear Regression in R - Step 2
Unit 10Simple Linear Regression in R - Step 3
Unit 11Simple Linear Regression in R - Step 4
Unit 12Quiz 2 - Simple Linear Regression
Module 5Section 5: Multiple Linear Regression
Unit 1Dataset + Business Problem Description
Unit 2Multiple Linear Regression Intuition - Step 1
Unit 3Multiple Linear Regression Intuition - Step 2
Unit 4Multiple Linear Regression Intuition - Step 3
Unit 5Multiple Linear Regression Intuition - Step 4
Unit 6Multiple Linear Regression Intuition - Step 5
Unit 7Multiple Linear Regression in Python - Step 1
Unit 8Multiple Linear Regression in Python - Step 2
Unit 9Multiple Linear Regression in Python - Step 3
Unit 10Multiple Linear Regression in Python - Backward Elimination - Preparation
Unit 11Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !
Unit 12Multiple Linear Regression in Python - Backward Elimination - Homework Solution
Unit 13Multiple Linear Regression in R - Step 1
Unit 14Multiple Linear Regression in R - Step 2
Unit 15Multiple Linear Regression in R - Step 3
Unit 16Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
Unit 17Multiple Linear Regression in R - Backward Elimination - Homework Solution
Unit 18Quiz 3 - Multiple Linear Regression
Module 6Section 6: Polynomial Regression
Unit 1Polynomial Regression Intuition
Unit 2Polynomial Regression in Python - Step 1
Unit 3Polynomial Regression in Python - Step 2
Unit 4Polynomial Regression in Python - Step 3
Unit 5Polynomial Regression in Python - Step 4
Unit 6Python Regression Template
Unit 7Polynomial Regression in R - Step 1
Unit 8Polynomial Regression in R - Step 2
Unit 9Polynomial Regression in R - Step 3
Unit 10Polynomial Regression in R - Step 4
Unit 11R Regression Template
Module 7Section 7: Support Vector Regression (SVR)
Unit 1SVR in Python
Unit 2SVR in R
Module 8Section 8: Decision Tree Regression
Unit 1Decision Tree Regression Intuition
Unit 2Random Forest Regression in Python
Unit 3Decision Tree Regression in R
Module 9Section 9: Random Forest Regression
Unit 1Random Forest Regression Intuition
Unit 2Random Forest Regression in Python
Unit 3Random Forest Regression in R
Module 10Section 10: Evaluating Regression Models Performance
Unit 1R-Squared Intuition
Unit 2Adjusted R-Squared Intuition
Unit 3Evaluating Regression Models Performance - Homework's Final Part
Unit 4Interpreting Linear Regression Coefficients
Module 11Section 11: ---------------------------- Part 3: Classification ----------------------------
There are no units in this module.
Module 12Section 12: Logistic Regression
Unit 1Logistic Regression Intuition
Unit 2Logistic Regression in Python - Step 1
Unit 3Logistic Regression in Python - Step 2
Unit 4Logistic Regression in Python - Step 3
Unit 5Logistic Regression in Python - Step 4
Unit 6Logistic Regression in Python - Step 5
Unit 7Python Classification Template
Unit 8Logistic Regression in R - Step 1
Unit 9Logistic Regression in R - Step 2
Unit 10Logistic Regression in R - Step 3
Unit 11Logistic Regression in R - Step 4
Unit 12Logistic Regression in R - Step 5
Unit 13R Classification Template
Unit 14Quiz 4 – Logistic Regression
Module 13Section 13: K-Nearest Neighbors (K-NN)
Unit 1K-Nearest Neighbor Intuition
Unit 2K-NN in Python
Unit 3K-NN in R
Unit 4Quiz 5 - K-Nearest Neighbor
Module 14Section 14: Support Vector Machine (SVM)
Unit 1SVM Intuition
Unit 2SVM in Python
Unit 3SVM in R
Module 15Section 15: Kernel SVM
There are no units in this module.
Module 16Section 16: Naive Bayes
Unit 1Bayes Theorem
Unit 2Naive Bayes Intuition
Unit 3Naive Bayes Intuition (Challenge Reveal)
Unit 4Naive Bayes Intuition (Extras)
Unit 5Naive Bayes in Python
Unit 6Naive Bayes in R
Module 17Section 17: Decision Tree Classification
Unit 1Decision Tree Classification Intuition
Unit 2Decision Tree Classification in Python
Unit 3Decision Tree Classification in R
Module 18Section 18: Random Forest Classification
Unit 1Random Forest Classification Intuition
Unit 2Random Forest Classification in Python
Unit 3Random Forest Classification in R
Module 19Section 19: Evaluating Classification Models Performance
Unit 1False Positives & False Negatives
Unit 2Confusion Matrix
Unit 3Accuracy Paradox
Unit 4CAP Curve
Unit 5CAP Curve Analysis
Module 20Section 20: ---------------------------- Part 4: Clustering ----------------------------
There are no units in this module.
Module 21Section 21: K-Means Clustering
Unit 1K-Means Clustering Intuition
Unit 2K-Means Random Initialization Trap
Unit 3K-Means Selecting The Number Of Clusters
Unit 4K-Means Clustering in Python
Unit 5K-Means Clustering in R
Unit 6Quiz 6 - K-Means Clustering
Module 22Section 22: Hierarchical Clustering
Unit 1Hierarchical Clustering Intuition
Unit 2Hierarchical Clustering How Dendrograms Work
Unit 3Hierarchical Clustering Using Dendrograms
Unit 4HC in Python - Step 1
Unit 5HC in Python - Step 2
Unit 6HC in Python - Step 3
Unit 7HC in Python - Step 4
Unit 8HC in Python - Step 5
Unit 9HC in R - Step 1
Unit 10HC in R - Step 2
Unit 11HC in R - Step 3
Unit 12HC in R - Step 4
Unit 13HC in R - Step 5
Unit 14Quiz 7 - Hierarchical Clustering
Module 23Section 23: ---------------------- Part 5: Association Rule Learning ----------------------
There are no units in this module.
Module 24Section 24: Apriori
There are no units in this module.
Module 25Section 25: Eclat
There are no units in this module.
Module 26Section 26: ------------------------ Part 6: Reinforcement Learning ------------------------
There are no units in this module.
Module 27Section 27: Upper Confidence Bound (UCB)
There are no units in this module.
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Machine Learning

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Course Description

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is structured in a fun and exciting way, but at the same time we dive deep into Machine Learning. In this course you will learn the following algorithms:

  • Linear Regression
  • Multiple Linear Regression
  • K-Means Clustering
  • Hierarchical Clustering
  • K-Nearest Neighbour
  • Decision Trees
  • Random Forest

Moreover, the course is packed with practical exercises which are based on live examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both R and Python code templates which you can download and use on your own projects.