Machine Learning AZ: AI, Python & R + ChatGPT Prize [2024]
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Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.
Over 1 Million students worldwide trust this course.
We will walk you stepbystep into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative subfield of Data Science.
This course can be completed by either doing either the Python tutorials, or R tutorials, or both – Python & R. Pick the programming language that you need for your career.
This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:

Part 1 – Data Preprocessing

Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

Part 3 – Classification: Logistic Regression, KNN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Part 4 – Clustering: KMeans, Hierarchical Clustering

Part 5 – Association Rule Learning: Apriori, Eclat

Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

Part 7 – Natural Language Processing: Bagofwords model and algorithms for NLP

Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA

Part 10 – Model Selection & Boosting: kfold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.
Moreover, the course is packed with practical exercises that are based on reallife case studies. So not only will you learn the theory, but you will also get lots of handson practice building your own models.
And last but not least, this course includes both Python and R code templates which you can download and use on your own projects.

1Welcome Challenge!

2Machine Learning Demo  Get Excited!
See the power of Machine Learning in action as we create a Logistic Regression predictive model for a realworld marketing and sales usecase!

3Get all the Datasets, Codes and Slides here

4How to use the ML AZ folder & Google Colab

5Installing R and R Studio (Mac, Linux & Windows)
In this video, Hadelin explains in details how to install R programming language and R studio on your computer so you can swiftly go through the rest of the course.

6EXTRA: Use ChatGPT to Boost your ML Skills

7Welcome to Part 1  Data Preprocessing

8The Machine Learning process
Understand the steps involved in Machine Learning: Data PreProcessing (Import the data, Clean the data, Split into training & test sets, Feature Scaling), Modelling (Build the model, Train the model, Make predictions), and Evaluation (Calculate performance metrics, Make a verdict).

9Splitting the data into a Training and Test set
Understand why it's important to split the data into a training set and a test set, how they differ and what they are used for.

10Feature Scaling
Two types of feature scaling: Normalization and Standardization. In the practical tutorials we focus on Standardisation and here we will discuss the intuition behind Normalisation.

11Getting Started  Step 1

12Getting Started  Step 2

13Importing the Libraries

14Importing the Dataset  Step 1

15Importing the Dataset  Step 2

16Importing the Dataset  Step 3

17For Python learners, summary of Objectoriented programming: classes & objects
A short written summary of what needs to know in Objectoriented programming, e.g. class, object, and method.

18Coding Exercise 1: Importing and Preprocessing a Dataset for Machine Learning

19Taking care of Missing Data  Step 1

20Taking care of Missing Data  Step 2

21Coding Exercise 2: Handling Missing Data in a Dataset for Machine Learning

22Encoding Categorical Data  Step 1

23Encoding Categorical Data  Step 2

24Encoding Categorical Data  Step 3

25Coding Exercise 3: Encoding Categorical Data for Machine Learning

26Splitting the dataset into the Training set and Test set  Step 1

27Splitting the dataset into the Training set and Test set  Step 2

28Splitting the dataset into the Training set and Test set  Step 3

29Coding Exercise 4: Dataset Splitting and Feature Scaling

30Feature Scaling  Step 1

31Feature Scaling  Step 2

32Feature Scaling  Step 3

33Feature Scaling  Step 4

34Coding exercise 5: Feature scaling for Machine Learning

35Getting Started

36Dataset Description

37Importing the Dataset

38Taking care of Missing Data

39Encoding Categorical Data

40Splitting the dataset into the Training set and Test set  Step 1

41Splitting the dataset into the Training set and Test set  Step 2

42Feature Scaling  Step 1

43Feature Scaling  Step 2

44Data Preprocessing Template

45Data Preprocessing Quiz

47Simple Linear Regression Intuition
The math behind Simple Linear Regression.

48Ordinary Least Squares
Finding the best fitting line with Ordinary Least Squares method to model the linear relationship between independent variable and dependent variable.

49Simple Linear Regression in Python  Step 1a

50Simple Linear Regression in Python  Step 1b

51Simple Linear Regression in Python  Step 2a

52Simple Linear Regression in Python  Step 2b

53Simple Linear Regression in Python  Step 3

54Simple Linear Regression in Python  Step 4a

55Simple Linear Regression in Python  Step 4b

56Simple Linear Regression in Python  Additional Lecture

57Simple Linear Regression in R  Step 1
Data preprocessing for Simple Linear Regression in R.

58Simple Linear Regression in R  Step 2
Fitting Simple Linear Regression (SLR) model to the training set using R function ‘lm’.

59Simple Linear Regression in R  Step 3
Predicting the test set results with the SLR model using R function ‘predict’ .

60Simple Linear Regression in R  Step 4a
Visualizing the training set results and test set results with R package ‘ggplot2’.

61Simple Linear Regression in R  Step 4b

62Simple Linear Regression in R  Step 4c

63Simple Linear Regression Quiz

64Dataset + Business Problem Description
An application of Multiple Linear Regression: profit prediction for Startups.

65Multiple Linear Regression Intuition
The math behind Multiple Linear Regression: modelling the linear relationship between the independent (explanatory) variables and dependent (response) variable.

66Assumptions of Linear Regression
The 5 assumptions associated with a linear regression model: linearity, homoscedasticity, multivariate normality, independence (no autocorrelation), and lack of multicollinearity  plus an additional check for outliers.

67Multiple Linear Regression Intuition  Step 3
Coding categorical variables in regression with dummy variables.

68Multiple Linear Regression Intuition  Step 4
Dummy variable trap and how to avoid it.

69Understanding the PValue

70Multiple Linear Regression Intuition  Step 5
An intuitive guide to 5 Stepwise Regression methods of building multiple linear regression models: Allin, Backward Elimination, Forward Selection, Bidirectional Elimination, and Score Comparison.

71Multiple Linear Regression in Python  Step 1a

72Multiple Linear Regression in Python  Step 1b

73Multiple Linear Regression in Python  Step 2a

74Multiple Linear Regression in Python  Step 2b

75Multiple Linear Regression in Python  Step 3a

76Multiple Linear Regression in Python  Step 3b

77Multiple Linear Regression in Python  Step 4a

78Multiple Linear Regression in Python  Step 4b

79Multiple Linear Regression in Python  Backward Elimination

80Multiple Linear Regression in Python  EXTRA CONTENT

81Multiple Linear Regression in R  Step 1a

82Multiple Linear Regression in R  Step 1b

83Multiple Linear Regression in R  Step 2a

84Multiple Linear Regression in R  Step 2b

85Multiple Linear Regression in R  Step 3

86Multiple Linear Regression in R  Backward Elimination  HOMEWORK !

87Multiple Linear Regression in R  Backward Elimination  Homework Solution

88Multiple Linear Regression in R  Automatic Backward Elimination

89Multiple Linear Regression Quiz