Machine Learning A-Z: 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 world-wide trust this course.
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 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:
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Part 1 – Data Preprocessing
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Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
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Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
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Part 4 – Clustering: K-Means, Hierarchical Clustering
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Part 5 – Association Rule Learning: Apriori, Eclat
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Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
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Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
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Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
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Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
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Part 10 – Model Selection & Boosting: k-fold 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 real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on 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.
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1Welcome Challenge!Text lesson
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2Machine Learning Demo - Get Excited!Video lesson
See the power of Machine Learning in action as we create a Logistic Regression predictive model for a real-world marketing and sales use-case!
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3Get all the Datasets, Codes and Slides hereText lesson
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4How to use the ML A-Z folder & Google ColabVideo lesson
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5Installing R and R Studio (Mac, Linux & Windows)Video lesson
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.
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6EXTRA: Use ChatGPT to Boost your ML SkillsText lesson
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7Welcome to Part 1 - Data PreprocessingText lesson
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8The Machine Learning processVideo lesson
Understand the steps involved in Machine Learning: Data Pre-Processing (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).
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9Splitting the data into a Training and Test setVideo lesson
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.
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10Feature ScalingVideo lesson
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.
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11Getting Started - Step 1Video lesson
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12Getting Started - Step 2Video lesson
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13Importing the LibrariesVideo lesson
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14Importing the Dataset - Step 1Video lesson
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15Importing the Dataset - Step 2Video lesson
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16Importing the Dataset - Step 3Video lesson
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17For Python learners, summary of Object-oriented programming: classes & objectsText lesson
A short written summary of what needs to know in Object-oriented programming, e.g. class, object, and method.
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18Coding Exercise 1: Importing and Preprocessing a Dataset for Machine LearningQuiz
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19Taking care of Missing Data - Step 1Video lesson
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20Taking care of Missing Data - Step 2Video lesson
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21Coding Exercise 2: Handling Missing Data in a Dataset for Machine LearningQuiz
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22Encoding Categorical Data - Step 1Video lesson
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23Encoding Categorical Data - Step 2Video lesson
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24Encoding Categorical Data - Step 3Video lesson
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25Coding Exercise 3: Encoding Categorical Data for Machine LearningQuiz
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26Splitting the dataset into the Training set and Test set - Step 1Video lesson
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27Splitting the dataset into the Training set and Test set - Step 2Video lesson
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28Splitting the dataset into the Training set and Test set - Step 3Video lesson
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29Coding Exercise 4: Dataset Splitting and Feature ScalingQuiz
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30Feature Scaling - Step 1Video lesson
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31Feature Scaling - Step 2Video lesson
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32Feature Scaling - Step 3Video lesson
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33Feature Scaling - Step 4Video lesson
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34Coding exercise 5: Feature scaling for Machine LearningQuiz
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35Getting StartedVideo lesson
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36Dataset DescriptionVideo lesson
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37Importing the DatasetVideo lesson
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38Taking care of Missing DataVideo lesson
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39Encoding Categorical DataVideo lesson
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40Splitting the dataset into the Training set and Test set - Step 1Video lesson
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41Splitting the dataset into the Training set and Test set - Step 2Video lesson
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42Feature Scaling - Step 1Video lesson
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43Feature Scaling - Step 2Video lesson
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44Data Preprocessing TemplateVideo lesson
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45Data Preprocessing QuizQuiz
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47Simple Linear Regression IntuitionVideo lesson
The math behind Simple Linear Regression.
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48Ordinary Least SquaresVideo lesson
Finding the best fitting line with Ordinary Least Squares method to model the linear relationship between independent variable and dependent variable.
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49Simple Linear Regression in Python - Step 1aVideo lesson
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50Simple Linear Regression in Python - Step 1bVideo lesson
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51Simple Linear Regression in Python - Step 2aVideo lesson
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52Simple Linear Regression in Python - Step 2bVideo lesson
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53Simple Linear Regression in Python - Step 3Video lesson
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54Simple Linear Regression in Python - Step 4aVideo lesson
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55Simple Linear Regression in Python - Step 4bVideo lesson
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56Simple Linear Regression in Python - Additional LectureText lesson
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57Simple Linear Regression in R - Step 1Video lesson
Data preprocessing for Simple Linear Regression in R.
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58Simple Linear Regression in R - Step 2Video lesson
Fitting Simple Linear Regression (SLR) model to the training set using R function ‘lm’.
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59Simple Linear Regression in R - Step 3Video lesson
Predicting the test set results with the SLR model using R function ‘predict’ .
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60Simple Linear Regression in R - Step 4aVideo lesson
Visualizing the training set results and test set results with R package ‘ggplot2’.
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61Simple Linear Regression in R - Step 4bVideo lesson
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62Simple Linear Regression in R - Step 4cVideo lesson
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63Simple Linear Regression QuizQuiz
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64Dataset + Business Problem DescriptionVideo lesson
An application of Multiple Linear Regression: profit prediction for Startups.
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65Multiple Linear Regression IntuitionVideo lesson
The math behind Multiple Linear Regression: modelling the linear relationship between the independent (explanatory) variables and dependent (response) variable.
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66Assumptions of Linear RegressionVideo lesson
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.
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67Multiple Linear Regression Intuition - Step 3Video lesson
Coding categorical variables in regression with dummy variables.
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68Multiple Linear Regression Intuition - Step 4Video lesson
Dummy variable trap and how to avoid it.
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69Understanding the P-ValueVideo lesson
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70Multiple Linear Regression Intuition - Step 5Video lesson
An intuitive guide to 5 Stepwise Regression methods of building multiple linear regression models: All-in, Backward Elimination, Forward Selection, Bidirectional Elimination, and Score Comparison.
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71Multiple Linear Regression in Python - Step 1aVideo lesson
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72Multiple Linear Regression in Python - Step 1bVideo lesson
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73Multiple Linear Regression in Python - Step 2aVideo lesson
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74Multiple Linear Regression in Python - Step 2bVideo lesson
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75Multiple Linear Regression in Python - Step 3aVideo lesson
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76Multiple Linear Regression in Python - Step 3bVideo lesson
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77Multiple Linear Regression in Python - Step 4aVideo lesson
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78Multiple Linear Regression in Python - Step 4bVideo lesson
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79Multiple Linear Regression in Python - Backward EliminationText lesson
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80Multiple Linear Regression in Python - EXTRA CONTENTText lesson
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81Multiple Linear Regression in R - Step 1aVideo lesson
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82Multiple Linear Regression in R - Step 1bVideo lesson
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83Multiple Linear Regression in R - Step 2aVideo lesson
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84Multiple Linear Regression in R - Step 2bVideo lesson
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85Multiple Linear Regression in R - Step 3Video lesson
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86Multiple Linear Regression in R - Backward Elimination - HOMEWORK !Video lesson
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87Multiple Linear Regression in R - Backward Elimination - Homework SolutionVideo lesson
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88Multiple Linear Regression in R - Automatic Backward EliminationText lesson
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89Multiple Linear Regression QuizQuiz