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Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2025]
Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Ligency Team
42.5 hours
Video Content
386
Lectures
1,142,501
Students
4.5
Rating
4.5
(1,142,501 students enrolled)What you'll learn
✓Master Machine Learning on Python & R
✓Have a great intuition of many Machine Learning models
✓Make accurate predictions
✓Make powerful analysis
✓Make robust Machine Learning models
✓Create strong added value to your business
✓Use Machine Learning for personal purpose
✓Handle specific topics like Reinforcement Learning, NLP and Deep Learning
✓Handle advanced techniques like Dimensionality Reduction
✓Know which Machine Learning model to choose for each type of problem
✓Build an army of powerful Machine Learning models and know how to combine them to solve any problem
Course Content
46 sections • 386 lectures • 42:44:57 total length
Welcome to the course! Here we will help you get started in the best conditions.
5 lectures • 12:13
Welcome to the course! Here we will help you get started in the best conditions.
5 lectures • 12:13
Get Excited about ML: Predict Car Purchases with Python & Scikit-learn in 5 mins04:45
Get all the Datasets, Codes and Slides here00:06
Recommended Workshops before we dive in!01:28
How to Use Google Colab & Machine Learning Course Folder05:44
Prizes $$ for Learning00:10
-------------------- Part 1: Data Preprocessing --------------------
4 lectures • 10:22
-------------------- Part 1: Data Preprocessing --------------------
4 lectures • 10:22
Welcome to Part 1 - Data Preprocessing00:22
Machine Learning Workflow: Importing, Modeling, and Evaluating Your ML Model01:31
Data Preprocessing: Importance of Training-Test Split in ML Model Evaluation02:02
Feature Scaling in Machine Learning: Normalization vs Standardization Explained06:27
Data Preprocessing in Python
19 lectures • 01:32:05
Data Preprocessing in Python
19 lectures • 01:32:05
Step 1 - Data Preprocessing in Python: Preparing Your Dataset for ML Models05:21
Step 2 - Data Preprocessing Techniques: From Raw Data to ML-Ready Datasets05:21
Machine Learning Toolkit: Importing NumPy, Matplotlib, and Pandas Libraries03:34
Step 1 - Machine Learning Basics: Importing Datasets Using Pandas read_csv()05:13
Step 2 - Using Pandas iloc for Feature Selection in ML Data Preprocessing04:42
+19 more lectures
Data Preprocessing in R
11 lectures • 47:29
Data Preprocessing in R
11 lectures • 47:29
Getting Started with R Programming: Install R and RStudio on Windows & Mac05:21
Data Preprocessing for Beginners: Preparing Your Dataset for Machine Learning01:35
Data Preprocessing Tutorial: Understanding Independent vs Dependent Variables01:57
R Tutorial: Importing and Viewing Datasets for Data Preprocessing02:44
How to Handle Missing Values in R: Data Preprocessing for Machine Learning05:55
+7 more lectures
-------------------- Part 2: Regression --------------------
1 lectures • 00:21
-------------------- Part 2: Regression --------------------
1 lectures • 00:21
Welcome to Part 2 - Regression00:21
Simple Linear Regression
16 lectures • 01:12:18
Simple Linear Regression
16 lectures • 01:12:18
Simple Linear Regression: Understanding the Equation and Potato Yield Prediction02:22
How to Find the Best Fit Line: Understanding Ordinary Least Squares Regression03:17
Step 1a - Mastering Simple Linear Regression: Key Concepts and Implementation05:49
Step 1b: Data Preprocessing for Linear Regression: Import & Split Data in Python05:58
Step 2a - Building a Simple Linear Regression Model with Scikit-learn in Python03:53
+12 more lectures
Multiple Linear Regression
25 lectures • 02:16:28
Multiple Linear Regression
25 lectures • 02:16:28
Startup Success Prediction: Regression Model for VC Fund Decision-Making03:44
Multiple Linear Regression: Independent Variables & Prediction Models02:26
Understanding Linear Regression Assumptions: Linearity, Homoscedasticity & More04:23
How to Handle Categorical Variables in Linear Regression Models07:21
Multicollinearity in Regression: Understanding the Dummy Variable Trap02:10
+21 more lectures
Polynomial Regression
20 lectures • 01:39:08
Polynomial Regression
20 lectures • 01:39:08
Understanding Polynomial Linear Regression: Applications and Examples05:08
Step 1a - Building a Polynomial Regression Model for Salary Prediction in Python04:36
Step 1b - Setting Up Data for Linear vs Polynomial Regression Comparison05:55
Step 2a: Linear to Polynomial Regression - Preparing Data for Advanced Models05:55
Step 2b - Transforming Linear to Polynomial Regression: A Step-by-Step Guide05:43
+16 more lectures
Support Vector Regression (SVR)
13 lectures • 01:03:23
Support Vector Regression (SVR)
13 lectures • 01:03:23
How Does Support Vector Regression (SVR) Differ from Linear Regression?08:09
RBF Kernel SVR: From Linear to Non-Linear Support Vector Regression03:57
Step 1a - SVR Model Training: Feature Scaling and Dataset Preparation in Python05:46
Step 1b - SVR in Python: Importing Libraries and Dataset for Machine Learning03:29
Step 2a - Mastering Feature Scaling for Support Vector Regression in Python05:34
+9 more lectures
Decision Tree Regression
10 lectures • 52:27
Decision Tree Regression
10 lectures • 52:27
How to Build a Regression Tree: Step-by-Step Guide for Machine Learning11:06
Step 1a - Decision Tree Regression: Building a Model without Feature Scaling04:40
Step 1b: Uploading & Preprocessing Data for Decision Tree Regression in Python03:58
Step 2 - Implementing DecisionTreeRegressor: A Step-by-Step Guide in Python04:59
Step 3 - Implementing Decision Tree Regression in Python: Making Predictions03:16
+6 more lectures
Random Forest Regression
6 lectures • 35:47
Random Forest Regression
6 lectures • 35:47
Understanding Random Forest Algorithm: Intuition and Application in ML06:44
Step 1 - Building a Random Forest Regression Model with Python and Scikit-Learn05:53
Step 2 - Creating a Random Forest Regressor: Key Parameters and Model Fitting05:55
Step 1 - Building a Random Forest Model in R: Regression Tutorial05:51
Step 2 - Visualizing Random Forest Regression: Interpreting Stairs and Splits05:58
+2 more lectures
Evaluating Regression Models Performance
2 lectures • 10:05
Evaluating Regression Models Performance
2 lectures • 10:05
Understanding R-squared: Evaluating Goodness of Fit in Regression Models04:35
Understanding Adjusted R-Squared: Key Differences from R-Squared Explained05:30
Evaluating Regression Models Performance Quiz5 questions
Regression Model Selection in Python
7 lectures • 28:14
Regression Model Selection in Python
7 lectures • 28:14
Make sure you have this Model Selection folder ready00:31
Step 1 - Mastering Regression Toolkit: Comparing Models for Optimal Performance04:45
Step 2 - Creating Generic Code Templates for Various Regression Models in Python05:59
Step 3: Evaluating Regression Models - R-Squared & Performance Metrics Explained03:59
Step 4 - Implementing R-Squared Score in Python with Scikit-Learn's Metrics03:58
+2 more lectures
Regression Model Selection in R
3 lectures • 19:13
Regression Model Selection in R
3 lectures • 19:13
Optimizing Regression Models: R-Squared vs Adjusted R-Squared Explained08:54
Linear Regression Analysis: Interpreting Coefficients for Business Decisions09:16
Conclusion of Part 2 - Regression01:03
-------------------- Part 3: Classification --------------------
2 lectures • 02:51
-------------------- Part 3: Classification --------------------
2 lectures • 02:51
Welcome to Part 3 - Classification00:21
What is Classification in Machine Learning? Fundamentals and Applications02:30
Logistic Regression
29 lectures • 01:54:45
Logistic Regression
29 lectures • 01:54:45
Understanding Logistic Regression: Predicting Categorical Outcomes04:55
Logistic Regression: Finding the Best Fit Curve Using Maximum Likelihood03:50
Step 1a - Building a Logistic Regression Model for Customer Behavior Prediction05:43
Step 1b - Implementing Logistic Regression in Python: Data Preprocessing Guide03:59
Step 2a: Python Data Preprocessing for Logistic Regression Dataset Prep05:51
+25 more lectures
K-Nearest Neighbors (K-NN)
7 lectures • 37:50
K-Nearest Neighbors (K-NN)
7 lectures • 37:50
K-Nearest Neighbors (KNN) Explained: A Beginner's Guide to Classification04:52
Step 1 - Python KNN Tutorial: Classifying Customer Data for Targeted Marketing05:58
Step 2 - Building a K-Nearest Neighbors Model: Scikit-Learn KNeighborsClassifier05:51
Step 3 - Visualizing KNN Decision Boundaries: Python Tutorial for Beginners05:58
Step 1 - Implementing KNN Classification in R: Setup & Data Preparation05:54
+3 more lectures
Support Vector Machine (SVM)
6 lectures • 35:33
Support Vector Machine (SVM)
6 lectures • 35:33
Support Vector Machines Explained: Hyperplanes and Support Vectors in ML09:49
Step 1 - Building a Support Vector Machine Model with Scikit-learn in Python05:58
Step 2 - Building a Support Vector Machine Model with Sklearn's SVC in Python05:53
Step 3 - Understanding Linear SVM Limitations: Why It Didn't Beat kNN Classifier02:39
Step 1 - Building a Linear SVM Classifier in R: Data Import and Initial Setup05:47
+2 more lectures
Kernel SVM
10 lectures • 01:05:05
Kernel SVM
10 lectures • 01:05:05
From Linear to Non-Linear SVM: Exploring Higher Dimensional Spaces03:17
Support Vector Machines: Transforming Non-Linear Data for Linear Separation07:50
Kernel Trick: SVM Machine Learning for Non-Linear Classification12:20
Understanding Different Types of Kernel Functions for Machine Learning02:24
Mastering Support Vector Regression: Non-Linear SVR with RBF Kernel Explained10:55
+6 more lectures
Naive Bayes
10 lectures • 01:16:35
Naive Bayes
10 lectures • 01:16:35
Understanding Bayes' Theorem Intuitively: From Probability to Machine Learning20:25
Understanding Naive Bayes Algorithm: Probabilistic Classification Explained14:03
Bayes Theorem in Machine Learning: Step-by-Step Probability Calculation06:04
Why is Naive Bayes Called Naive? Understanding the Algorithm's Assumptions09:41
Step 1 - Naive Bayes in Python: Applying ML to Social Network Ads Optimisation05:56
+6 more lectures
Decision Tree Classification
6 lectures • 37:31
Decision Tree Classification
6 lectures • 37:31
How Decision Tree Algorithms Work: Step-by-Step Guide with Examples08:08
Step 1 - Implementing Decision Tree Classification in Python with Scikit-learn05:59
Step 2 - Training a Decision Tree Classifier: Optimizing Performance in Python05:56
Step 1 - R Tutorial: Creating a Decision Tree Classifier with rpart Library05:55
Step 2 - Decision Tree Classifier: Optimizing Prediction Boundaries in R05:51
+2 more lectures
Random Forest Classification
6 lectures • 33:40
Random Forest Classification
6 lectures • 33:40
Understanding Random Forest: Decision Trees and Majority Voting Explained04:28
Step 1 - Implementing Random Forest Classification in Python with Scikit-Learn05:56
Step 2: Random Forest Evaluation - Confusion Matrix & Accuracy Metrics05:56
Step 1: Random Forest Classifier - From Template to Implementation in R05:56
Step 2: Random Forest Classification - Visualizing Predictions & Results05:58
+2 more lectures
Classification Model Selection in Python
6 lectures • 25:45
Classification Model Selection in Python
6 lectures • 25:45
Make sure you have this Model Selection folder ready00:33
Mastering the Confusion Matrix: True Positives, Negatives, and Errors04:52
Step 1 - How to Choose the Right Classification Algorithm for Your Dataset05:51
Step 2 - Optimizing Model Selection: Streamlined Classification Code in Python05:59
Step 3 - Evaluating Classification Algorithms: Accuracy Metrics in Python05:52
+1 more lectures
Evaluating Classification Models Performance
5 lectures • 29:53
Evaluating Classification Models Performance
5 lectures • 29:53
Logistic Regression: Interpreting Predictions and Errors in Data Science07:57
Machine Learning Model Evaluation: Accuracy Paradox and Better Metrics02:12
Understanding CAP Curves: Assessing Model Performance in Data Science 202411:16
Mastering CAP Analysis: Assessing Classification Models with Accuracy Ratio06:19
Conclusion of Part 3 - Classification02:09
+1 more lectures
-------------------- Part 4: Clustering --------------------
1 lectures • 00:21
-------------------- Part 4: Clustering --------------------
1 lectures • 00:21
Welcome to Part 4 - Clustering00:21
K-Means Clustering
17 lectures • 01:24:25
K-Means Clustering
17 lectures • 01:24:25
What is Clustering in Machine Learning? Introduction to Unsupervised Learning03:19
K-Means Clustering Tutorial: Visualizing the Machine Learning Algorithm02:37
How to Use the Elbow Method in K-Means Clustering: A Step-by-Step Guide03:59
K-Means++ Algorithm: Solving the Random Initialization Trap in Clustering04:48
Step 1a - Python K-Means Tutorial: Identifying Customer Patterns in Mall Data04:59
+13 more lectures
Hierarchical Clustering
15 lectures • 01:21:07
Hierarchical Clustering
15 lectures • 01:21:07
How to Perform Hierarchical Clustering: Step-by-Step Guide for Machine Learning08:47
Visualizing Cluster Dissimilarity: Dendrograms in Hierarchical Clustering08:47
Mastering Hierarchical Clustering: Dendrogram Analysis and Threshold Setting11:21
Step 1 - Getting Started with Hierarchical Clustering: Data Setup in Python05:58
Step 2a - Implementing Hierarchical Clustering: Building a Dendrogram with SciPy04:52
+11 more lectures
-------------------- Part 5: Association Rule Learning --------------------
1 lectures • 00:11
-------------------- Part 5: Association Rule Learning --------------------
1 lectures • 00:11
Welcome to Part 5 - Association Rule Learning00:11
Apriori
8 lectures • 02:10:09
Apriori
8 lectures • 02:10:09
Apriori Algorithm: Uncovering Hidden Patterns in Data Mining | Association Rules18:13
Step 1 - Association Rule Learning: Boost Sales with Python Data Mining08:46
Step 2 - Creating a List of Transactions for Market Basket Analysis in Python17:07
Step 3 - Configuring Apriori Function: Support, Confidence, and Lift in Python12:48
Step 4: Visualizing Apriori Algorithm Results for Product Deals in Python19:41
+4 more lectures
Eclat
3 lectures • 28:14
Eclat
3 lectures • 28:14
Mastering ECLAT: Support-Based Approach to Market Basket Optimization06:05
Python Tutorial: Adapting Apriori to Eclat for Efficient Frequent Itemset Mining12:00
Eclat vs Apriori: Simplified Association Rule Learning in Data Mining10:09
Eclat Quiz5 questions
-------------------- Part 6: Reinforcement Learning --------------------
1 lectures • 00:41
-------------------- Part 6: Reinforcement Learning --------------------
1 lectures • 00:41
Welcome to Part 6 - Reinforcement Learning00:41
Upper Confidence Bound (UCB)
13 lectures • 02:22:24
Upper Confidence Bound (UCB)
13 lectures • 02:22:24
Multi-Armed Bandit: Exploration vs Exploitation in Reinforcement Learning15:36
Upper Confidence Bound Algorithm: Solving Multi-Armed Bandit Problems in ML14:53
Step 1 - Upper Confidence Bound: Solving Multi-Armed Bandit Problem in Python12:42
Step 2: Implementing UCB Algorithm in Python - Data Preparation03:51
Step 3 - Python Code for Upper Confidence Bound: Setting Up Key Variables07:16
+9 more lectures
Thompson Sampling
9 lectures • 01:30:14
Thompson Sampling
9 lectures • 01:30:14
Understanding Thompson Sampling Algorithm: Intuition and Implementation19:12
Deterministic vs Probabilistic: UCB and Thompson Sampling in Machine Learning08:12
Step 1 - Python Implementation of Thompson Sampling for Bandit Problems05:47
Step 2 - Optimizing Ad Selection with Thompson Sampling Algorithm in Python12:19
Step 3 - Python Code for Thompson Sampling: Maximizing Random Beta Distributions14:03
+5 more lectures
-------------------- Part 7: Natural Language Processing --------------------
25 lectures • 03:05:48
-------------------- Part 7: Natural Language Processing --------------------
25 lectures • 03:05:48
Welcome to Part 7 - Natural Language Processing01:05
NLP Basics: Understanding Bag of Words and Its Applications in Machine Learning03:02
Deep NLP & Sequence-to-Sequence Models: Exploring Natural Language Processing04:11
From If/Else Rules to CNNs: Evolution of Natural Language Processing11:22
Implementing Bag of Words in NLP: A Step-by-Step Tutorial17:05
+21 more lectures
-------------------- Part 8: Deep Learning --------------------
2 lectures • 12:57
-------------------- Part 8: Deep Learning --------------------
2 lectures • 12:57
Welcome to Part 8 - Deep Learning00:23
Introduction to Deep Learning: From Historical Context to Modern Applications12:34
Deep Learning Quiz5 questions
Artificial Neural Networks
20 lectures • 03:25:34
Artificial Neural Networks
20 lectures • 03:25:34
Understanding CNN Layers: Convolution, ReLU, Pooling, and Flattening Explained02:51
Deep Learning Basics: Exploring Neurons, Synapses, and Activation Functions16:24
Neural Network Basics: Understanding Activation Functions in Deep Learning08:29
How Do Neural Networks Work? Step-by-Step Guide to Deep Learning Algorithms12:47
How Do Neural Networks Learn? Deep Learning Fundamentals Explained12:58
+16 more lectures
Convolutional Neural Networks
17 lectures • 03:14:44
Convolutional Neural Networks
17 lectures • 03:14:44
Understanding CNN Layers: Convolution, ReLU, Pooling, and Flattening Explained03:31
Introduction to CNNs: Understanding Deep Learning for Computer Vision15:49
Step 1 - Understanding Convolution in CNNs: Feature Detection and Feature Maps16:38
Step 1b - Applying ReLU to Convolutional Layers: Breaking Up Image Linearity06:41
Step 2 - Max Pooling in CNNs: Enhancing Spatial Invariance for Image Recognition14:13
+13 more lectures
-------------------- Part 9: Dimensionality Reduction --------------------
1 lectures • 00:33
-------------------- Part 9: Dimensionality Reduction --------------------
1 lectures • 00:33
Welcome to Part 9 - Dimensionality Reduction00:33
Principal Component Analysis (PCA)
6 lectures • 01:03:23
Principal Component Analysis (PCA)
6 lectures • 01:03:23
PCA Algorithm Intuition: Reducing Dimensions in Unsupervised Learning03:49
Step 1 PCA in Python : Reducing Wine Dataset Features with Scikit-learn16:52
Step 2 - PCA in Action: Reducing Dimensions and Predicting Customer Segments05:30
Step 1 in R - Understanding Principal Component Analysis for Feature Extraction12:08
Step 2 - Using preProcess Function in R for PCA: Extracting Principal Components11:22
+2 more lectures
Linear Discriminant Analysis (LDA)
3 lectures • 38:41
Linear Discriminant Analysis (LDA)
3 lectures • 38:41
LDA Intuition: Maximizing Class Separation in Machine Learning Algorithms03:50
Mastering Linear Discriminant Analysis: Step-by-Step Python Implementation14:52
Step-by-Step Guide: Applying LDA for Feature Extraction in Machine Learning19:59
LDA Quiz5 questions
Kernel PCA
2 lectures • 31:33
Kernel PCA
2 lectures • 31:33
Kernel PCA in Python: Improving Classification Accuracy with Feature Extraction11:03
Implementing Kernel PCA for Non-Linear Data: Step-by-Step Guide20:30
-------------------- Part 10: Model Selection & Boosting --------------------
1 lectures • 00:29
-------------------- Part 10: Model Selection & Boosting --------------------
1 lectures • 00:29
Welcome to Part 10 - Model Selection & Boosting00:29
Model Selection
6 lectures • 01:22:53
Model Selection
6 lectures • 01:22:53
Mastering Model Evaluation: K-Fold Cross-Validation Techniques Explained08:57
How to Master the Bias-Variance Tradeoff in Machine Learning Models04:47
K-Fold Cross-Validation in Python: Improve Machine Learning Model Performance13:45
Optimizing SVM Models with GridSearchCV: A Step-by-Step Python Tutorial21:56
Evaluating ML Model Accuracy: K-Fold Cross-Validation Implementation in R19:29
+1 more lectures
XGBoost
3 lectures • 33:34
XGBoost
3 lectures • 33:34
How to Use XGBoost in Python for Cancer Prediction with High Accuracy14:48
Model Selection and Boosting Additional Content00:32
XGBoost Tutorial: Implementing Gradient Boosting for Classification Problems18:14
Annex: Logistic Regression (Long Explanation)
1 lectures • 17:06
Annex: Logistic Regression (Long Explanation)
1 lectures • 17:06
Logistic Regression Intuition17:06
Congratulations!! Don't forget your Prize :)
2 lectures • 00:47
Congratulations!! Don't forget your Prize :)
2 lectures • 00:47
Find your Career Path!00:02
Bonus Lecture00:44
Description
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:
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, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Part 4 - Clustering: K-Means, Hierarchical Clustering
Part 5 - Association Rule Learning: Apriori, Eclat
Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
Part 7 - Natural Language Processing: Bag-of-words 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: 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.
Who this course is for:
- Anyone interested in Machine Learning.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any students in college who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
- Any people who want to create added value to their business by using powerful Machine Learning tools.
This course includes:
- 42.5 hours on-demand video
- 40 articles 0
- Access on mobile and TV
- ∞Full lifetime access
- Certificate of completion
Instructors
Kirill Eremenko
Hadelin de Ponteves
SuperDataScience Team
Ligency Team
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