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

  • Rakesh

    Curious about the course

  • If possible, it would be nice if you can include XGBoost and Deep learning in this course, or make it a separate courses.

    Thank you.

  • modifyme

    Hello, I know it’s still early but there are no instructions or link to get the datasets

  • 曾思瑋

    Hi Kirill,

    May I access this course on Udemy?

    • Hi Tony,
      Sure you can, but you will need to pay in order to get the course on Udemy, as usual.