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Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

Dive into the exciting world of Machine Learning with our comprehensive course in Python and R. Learn with experts, enjoy hands-on practice, and master key algorithms!

4.9

315,475 rating
11 Lessons
229 Students
Last updated 3 months ago

By Kirill Eremenko

By SuperDataScience Team

By Ligency Team

By Hadelin de Ponteves

via Udemy

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Brief Summary

This course simplifies complex Machine Learning concepts and coding for you. With flexible learning paths, practical exercises, and expert guidance, you'll build your skills effortlessly. Perfect for your career!

Key Points

Learn from Data Science experts
Choose between Python and R
Hands-on practice with real-life case studies
Covered topics: Regression, Classification, Clustering, NLP, and more

Learning Outcomes

Understand and implement various Machine Learning algorithms
Develop skills in Python and R for real-world applications
Gain confidence with hands-on coding exercises and projects

About This Course

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

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.

Master Machine Learning on Python & R
Have a great intuition of many Machine Learning models
Make accurate predictions

Course Curriculum

Welcome to the course! Here we will help you get started in the best conditions.

6 Lectures

Welcome Challenge!

ML for Beginners: Predict Car Purchases with Python & Scikit-learn

Get all the Datasets, Codes and Slides here

How to Use Google Colab for Machine Learning: A Beginner's Guide

Getting Started with R Programming: Install R and RStudio on Windows, Mac, Linux

EXTRA: Use ChatGPT to Boost your ML Skills

-------------------- Part 1: Data Preprocessing --------------------

4 Lectures

Welcome to Part 1 - Data Preprocessing

Machine Learning Workflow: Importing, Modeling, and Evaluating Your ML Model

Data Preprocessing: Importance of Training-Test Split in ML Model Evaluation

Feature Scaling in Machine Learning: Normalization vs Standardization Explained

Data Preprocessing in Python

19 Lectures

Step 1 - Data Preprocessing in Python: Preparing Your Dataset for ML Models

Step 2 - Data Preprocessing Techniques: From Raw Data to ML-Ready Datasets

Machine Learning Toolkit: Importing NumPy, Matplotlib, and Pandas Libraries

Step 1 - Machine Learning Basics: Importing Datasets Using Pandas read_csv()

Step 2 - Using Pandas iloc for Feature Selection in ML Data Preprocessing

Step 3 - Preprocessing Data: Building X and Y Vectors for ML Model Training

For Python learners, summary of Object-oriented programming: classes & objects

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

Step 1 - Using Scikit-Learn to Replace Missing Values in Machine Learning

Step 2 - Imputing Missing Data in Python: SimpleImputer and Numerical Columns

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

Step 1 - One-Hot Encoding: Transforming Categorical Features for ML Algorithms

Step 2 - Handling Categorical Data: One-Hot Encoding with ColumnTransformer

Step 3 - Preprocessing Categorical Data: One-Hot and Label Encoding Techniques

Coding Exercise 3: Encoding Categorical Data for Machine Learning

Step 1 - How to Prepare Data for Machine Learning: Training vs Test Sets

Step 2 - Preparing Data: Creating Training and Test Sets in R for ML Models

Step 3 - Splitting Data into Training and Test Sets: Best Practices in Python

Coding Exercise 4: Dataset Splitting and Feature Scaling

Feature Scaling in ML: Why It's Crucial for Data Preprocessing

Step 2 - How to Scale Numeric Features in R for Machine Learning Preprocessing

Step 3 - Implementing Feature Scaling: Fit and Transform Methods Explained

Step 4 - Applying the Same Scaler to Training and Test Sets in Python

Coding exercise 5: Feature scaling for Machine Learning

Data Preprocessing in R

10 Lectures

Data Preprocessing for Beginners: Preparing Your Dataset for Machine Learning

Data Preprocessing Tutorial: Understanding Independent vs Dependent Variables

R Tutorial: Importing and Viewing Datasets for Data Preprocessing

How to Handle Missing Values in R: Data Preprocessing for Machine Learning

Using R's Factor Function to Handle Categorical Variables in Data Analysis

Step 1 - How to Prepare Data for Machine Learning: Training vs Test Sets

Step 2 - Preparing Data: Creating Training and Test Sets in R for ML Models

Feature Scaling in ML: Why It's Crucial for Data Preprocessing

Step 2 - How to Scale Numeric Features in R for Machine Learning Preprocessing

Essential Steps in Data Preprocessing: Preparing Your Dataset for ML Models

Data Preprocessing Quiz

-------------------- Part 2: Regression --------------------

1 Lectures

Welcome to Part 2 - Regression

Simple Linear Regression

16 Lectures

Simple Linear Regression: Understanding the Equation and Potato Yield Prediction

How to Find the Best Fit Line: Understanding Ordinary Least Squares Regression

Step 1 - Mastering Simple Linear Regression: Key Concepts and Implementation

Data Preprocessing for Linear Regression: Import & Split Data in Python

Step 2 - Building a Simple Linear Regression Model with Scikit-learn in Python

Step 2b - Machine Learning Basics: Training a Linear Regression Model in Python

Step 3 - Using Scikit-Learn's Predict Method for Linear Regression in Python

Python Linear Regression: Plotting Real vs Predicted Salaries Visualization

Step 4b - Evaluating Linear Regression Model Performance on Test Data

Simple Linear Regression in Python - Additional Lecture

Step 1 - Data Preprocessing in R: Preparing for Linear Regression Modeling

Step 2 - Fitting Simple Linear Regression in R: LM Function and Model Summary

Step 3 - How to Use predict() Function in R for Linear Regression Analysis

Step 4a - Plotting Linear Regression Data in R: ggplot2 Step-by-Step Guide

Step 4b - Creating a Scatter Plot with Regression Line in R Using ggplot2

Step 4c - Comparing Training vs Test Set Predictions in Linear Regression

Simple Linear Regression Quiz

Multiple Linear Regression

25 Lectures

Startup Success Prediction: Regression Model for VC Fund Decision-Making

Multiple Linear Regression: Independent Variables & Prediction Models

Understanding Linear Regression Assumptions: Linearity, Homoscedasticity & More

Step 3 - How to Handle Categorical Variables in Linear Regression Models

Step 4 - Multicollinearity in Regression: Understanding the Dummy Variable Trap

Understanding P-Values and Statistical Significance in Hypothesis Testing

Step 5 - Backward Elimination: Building Robust Multiple Linear Regression Models

Step 1a - Hands-On Data Preprocessing for Multiple Linear Regression in Python

Step 1b - Hands-On Guide: Implementing Multiple Linear Regression in Python

Step 2a - Hands-on Multiple Linear Regression: Preparing Data in Python

Step 2b - Multiple Linear Regression in Python: Preparing Your Dataset

Step 3a - Scikit-learn for Multiple Linear Regression: Efficient Model Building

Step 3b - Scikit-Learn: Building & Training Multiple Linear Regression Models

Step 4a: Comparing Real vs Predicted Profits in Linear Regression - Hands-on Gui

Step 4b - ML in Python: Evaluating Multiple Linear Regression Accuracy

Multiple Linear Regression in Python - Backward Elimination

Multiple Linear Regression in Python - EXTRA CONTENT

Data Preprocessing for MLR: Handling Categorical Data

Preparing Datasets for Multiple Linear Regression in R: Step-by-Step Guide

Step 2a - Multiple Linear Regression in R: Building & Interpreting the Regressor

Step 2b: Statistical Significance - P-values & Stars in Regression

Step 3 - How to Use predict() Function in R for Multiple Linear Regression

Optimizing Multiple Regression Models: Backward Elimination Technique in R

Mastering Feature Selection: Backward Elimination in R for Linear Regression

Multiple Linear Regression in R - Automatic Backward Elimination

Multiple Linear Regression Quiz

Polynomial Regression

20 Lectures

Understanding Polynomial Linear Regression: Applications and Examples

Step 1a - Building a Polynomial Regression Model for Salary Prediction in Python

Step 1b - Setting Up Data for Linear vs Polynomial Regression Comparison

Step 2a: Linear to Polynomial Regression - Preparing Data for Advanced Models

Step 2b - Transforming Linear to Polynomial Regression: A Step-by-Step Guide

Step 3a - Plotting Real vs Predicted Salaries: Linear Regression Visualization

Step 3b - Polynomial vs Linear Regression: Better Fit with Higher Degrees

Step 4a: Predicting Salaries - Linear Regression in Python (Array Input Guide)

Step 4b: Python Polynomial Regression - Predicting Salaries Accurately

Step 1a - Implementing Polynomial Regression in R: HR Salary Analysis Case Study

Step 1b - ML Fundamentals: Preparing Data for Polynomial Regression

Step 2a - Building Linear & Polynomial Regression Models in R: A Comparison

Step 2b - Building a Polynomial Regression Model: Adding Squared & Cubed Terms

Step 3a: Visualizing Regression Results - Creating Scatter Plots with ggplot2 in

Step 3b: Visualizing Linear Regression - Plotting Predictions vs Observations

Step 3c - Polynomial Regression: Curve Fitting for Better Predictions

Step 4a - How to Make Single Predictions Using Polynomial Regression in R

Step 4b - Predicting Salaries with Polynomial Regression: A Practical Example

Step 1 - Building a Reusable Framework for Nonlinear Regression Analysis in R

Step 2 - Mastering Regression Model Visualization: Increasing Data Resolution

Polynomial Regression Quiz

Support Vector Regression (SVR)

13 Lectures

How Does Support Vector Regression (SVR) Differ from Linear Regression?

RBF Kernel SVR: From Linear to Non-Linear Support Vector Regression

Step 1a - SVR Model Training: Feature Scaling and Dataset Preparation in Python

Step 1b - SVR in Python: Importing Libraries and Dataset for Machine Learning

Step 2a - Mastering Feature Scaling for Support Vector Regression in Python

Step 2b: Reshaping Data for SVR - Preparing Y Vector for Feature Scaling (Python

Step 2c: SVR Data Prep - Scaling X & Y Independently with StandardScaler

SVM Regression: Creating & Training SVR Model with RBF Kernel in Python

Step 4 - SVR Model Prediction: Handling Scaled Data and Inverse Transformation

Step 5a - How to Plot Support Vector Regression (SVR) Models: Step-by-Step Guide

Step 5b - SVR: Scaling & Inverse Transformation in Python

Step 1 - SVR Tutorial: Creating a Support Vector Machine Regressor in R

Support Vector Regression: Building a Predictive Model in Python (Step 2)

SVR Quiz

Decision Tree Regression

10 Lectures

How to Build a Regression Tree: Step-by-Step Guide for Machine Learning

Step 1a - Decision Tree Regression: Building a Model without Feature Scaling

Step 1b: Uploading & Preprocessing Data for Decision Tree Regression in Python

Step 2 - Implementing DecisionTreeRegressor: A Step-by-Step Guide in Python

Step 3 - Implementing Decision Tree Regression in Python: Making Predictions

Decision Tree Regression in Python - Step 4

Decision Tree Regression in R - Step 1

Decision Tree Regression in R - Step 2

Decision Tree Regression in R - Step 3

Decision Tree Regression in R - Step 4

Decision Tree Regression Quiz

Random Forest Regression

6 Lectures

Understanding Random Forest Algorithm: Intuition and Application in ML

Step 1 - Building a Random Forest Regression Model with Python and Scikit-Learn

Step 2 - Creating a Random Forest Regressor: Key Parameters and Model Fitting

Step 1 - Building a Random Forest Model in R: Regression Tutorial

Step 2 - Visualizing Random Forest Regression: Interpreting Stairs and Splits

Fine-Tuning Random Forest: 10-500 Trees for Accurate Salary Data Prediction

Random Forest Regression Quiz

Evaluating Regression Models Performance

2 Lectures

Understanding R-squared: Evaluating Goodness of Fit in Regression Models

Understanding Adjusted R-Squared: Key Differences from R-Squared Explained

Evaluating Regression Models Performance Quiz

Regression Model Selection in Python

8 Lectures

Make sure you have this Model Selection folder ready

Step 1 - Mastering Regression Toolkit: Comparing Models for Optimal Performance

Step 2 - Creating Generic Code Templates for Various Regression Models in Python

Step 3: Evaluating Regression Models - R-Squared & Performance Metrics Explained

Step 4 - Implementing R-Squared Score in Python with Scikit-Learn's Metrics

Step 1 - Selecting the Best Regression Model: R-squared Evaluation in Python

Step 2 - Selecting the Best Regression Model: Random Forest vs. SVR Performance

Conclusion of Part 2 - Regression

Regression Model Selection in R

3 Lectures

Optimizing Regression Models: R-Squared vs Adjusted R-Squared Explained

Linear Regression Analysis: Interpreting Coefficients for Business Decisions

Conclusion of Part 2 - Regression

-------------------- Part 3: Classification --------------------

2 Lectures

Welcome to Part 3 - Classification

What is Classification in Machine Learning? Fundamentals and Applications

Logistic Regression

29 Lectures

Understanding Logistic Regression: Predicting Categorical Outcomes

Logistic Regression: Finding the Best Fit Curve Using Maximum Likelihood

Step 1a - Building a Logistic Regression Model for Customer Behavior Prediction

Step 1b - Implementing Logistic Regression in Python: Data Preprocessing Guide

Step 2a: Python Data Preprocessing for Logistic Regression Dataset Prep

Step 2b - Data Preprocessing: Feature Scaling Techniques for Logistic Regression

Step 3a - How to Import and Use LogisticRegression Class from Scikit-learn

Training Logistic Regression: Fit Method for Classification in Python (Step 3b)

Step 4a - Formatting Single Observation Input for Logistic Regression Predict

Step 4b: Predicted vs. Real Purchase Decisions in Logistic Regression

Step 5 - Comparing Predicted vs Real Results: Python Logistic Regression Guide

Step 6a - Implementing Confusion Matrix and Accuracy Score in Scikit-Learn

Step 6b: Evaluating Classification Models - Confusion Matrix & Accuracy Metrics

Step 7a - Visualizing Logistic Regression Decision Boundaries in Python: 2D Plot

Step 7b - Interpreting Logistic Regression Results: Prediction Regions Explained

Step 7c - Visualizing Logistic Regression Performance on New Data in Python

Logistic Regression in Python - Step 7 (Colour-blind friendly image)

Step 1 - Data Preprocessing for Logistic Regression in R: Preparing Your Dataset

Step 2 - How to Create a Logistic Regression Classifier Using R's GLM Function

Step 3 - How to Use R for Logistic Regression Prediction: Step-by-Step Guide

Step 4 - How to Assess Model Accuracy Using a Confusion Matrix in R

Warning - Update

Step 5a - Interpreting Logistic Regression Plots: Prediction Regions Explained

Step 5b: Logistic Regression - Linear Classifiers & Prediction Boundaries

Step 5c - Data Viz in R: Colorizing Pixels for Logistic Regression

Logistic Regression in R - Step 5 (Colour-blind friendly image)

Optimizing R Scripts for Machine Learning: Building a Classification Template

Machine Learning Regression and Classification EXTRA

Logistic Regression Quiz

EXTRA CONTENT: Logistic Regression Practical Case Study

K-Nearest Neighbors (K-NN)

7 Lectures

K-Nearest Neighbors (KNN) Explained: A Beginner's Guide to Classification

Step 1 - Python KNN Tutorial: Classifying Customer Data for Targeted Marketing

Step 2 - Building a K-Nearest Neighbors Model: Scikit-Learn KNeighborsClassifier

Step 3 - Visualizing KNN Decision Boundaries: Python Tutorial for Beginners

Implementing KNN Classification in R: Setup & Data Preparation

Step 2 - Building a KNN Classifier: Preparing Training and Test Sets in R

Step 3 - Implementing KNN Classification in R: Adapting the Classifier Template

K-Nearest Neighbor Quiz

Support Vector Machine (SVM)

6 Lectures

Support Vector Machines Explained: Hyperplanes and Support Vectors in ML

Step 1 - Building a Support Vector Machine Model with Scikit-learn in Python

Step 2 - Building a Support Vector Machine Model with Sklearn's SVC in Python

Step 3 - Understanding Linear SVM Limitations: Why It Didn't Beat kNN Classifier

Step 1 - Building a Linear SVM Classifier in R: Data Import and Initial Setup

Step 2: Creating & Evaluating Linear SVM Classifier in R - Predictions & Results

SVM Quiz

Kernel SVM

10 Lectures

From Linear to Non-Linear SVM: Exploring Higher Dimensional Spaces

Support Vector Machines: Transforming Non-Linear Data for Linear Separation

Kernel Trick: SVM Machine Learning for Non-Linear Classification

Understanding Different Types of Kernel Functions for Machine Learning

Mastering Support Vector Regression: Non-Linear SVR with RBF Kernel Explained

Step 1 - Python Kernel SVM: Applying RBF to Solve Non-Linear Classification

Step 2 - Mastering Kernel SVM: Improving Accuracy with Non-Linear Classifiers

Step 1 - Kernel SVM vs Linear SVM: Overcoming Non-Linear Separability in R

Step 2 - Building a Gaussian Kernel SVM Classifier for Advanced Machine Learning

Step 3: Visualizing Kernel SVM - Non-Linear Classification in Machine Learning

Kernel SVM Quiz

Naive Bayes

10 Lectures

Understanding Bayes' Theorem Intuitively: From Probability to Machine Learning

Understanding Naive Bayes Algorithm: Probabilistic Classification Explained

Bayes Theorem in Machine Learning: Step-by-Step Probability Calculation

Why is Naive Bayes Called Naive? Understanding the Algorithm's Assumptions

Python ML: Naive Bayes for Social Network Ads - Step 1

Step 2 - Python Naive Bayes: Training and Evaluating a Classifier on Real Data

Step 3 - Analyzing Naive Bayes Algorithm Results: Accuracy and Predictions

Step 1 - Getting Started with Naive Bayes Algorithm in R for Classification

Step 2 - Troubleshooting Naive Bayes Classification: Empty Prediction Vectors

Step 3 - Visualizing Naive Bayes Results: Creating Confusion Matrix and Graphs

Naive Bayes Quiz

Decision Tree Classification

6 Lectures

How Decision Tree Algorithms Work: Step-by-Step Guide with Examples

Step 1 - Implementing Decision Tree Classification in Python with Scikit-learn

Step 2 - Training a Decision Tree Classifier: Optimizing Performance in Python

Step 1 - R Tutorial: Creating a Decision Tree Classifier with rpart Library

Step 2 - Decision Tree Classifier: Optimizing Prediction Boundaries in R

Step 3 - Decision Tree Visualization: Exploring Splits and Conditions in Python

Decision Tree Classification Quiz

Random Forest Classification

6 Lectures

Understanding Random Forest: Decision Trees and Majority Voting Explained

Step 1 - Implementing Random Forest Classification in Python with Scikit-Learn

Step 2: Random Forest Evaluation - Confusion Matrix & Accuracy Metrics

Step 1: Random Forest Classifier - From Template to Implementation in R

Step 2: Random Forest Classification - Visualizing Predictions & Results

Step 3 - Evaluating Random Forest Performance: Test Set Results & Overfitting

Random Forest Classification Quiz

Classification Model Selection in Python

6 Lectures

Make sure you have this Model Selection folder ready

Mastering the Confusion Matrix: True Positives, Negatives, and Errors

Step 1 - How to Choose the Right Classification Algorithm for Your Dataset

Step 2 - Optimizing Model Selection: Streamlined Classification Code in Python

Step 3 - Evaluating Classification Algorithms: Accuracy Metrics in Python

Step 4 - Model Selection Process: Evaluating Classification Algorithms

Evaluating Classification Models Performance

5 Lectures

Logistic Regression: Interpreting Predictions and Errors in Data Science

Machine Learning Model Evaluation: Accuracy Paradox and Better Metrics

Understanding CAP Curves: Assessing Model Performance in Data Science 2024

Mastering CAP Analysis: Assessing Classification Models with Accuracy Ratio

Conclusion of Part 3 - Classification

Evaluating Classiification Model Performance Quiz

-------------------- Part 4: Clustering --------------------

1 Lectures

Welcome to Part 4 - Clustering

K-Means Clustering

17 Lectures

What is Clustering in Machine Learning? Introduction to Unsupervised Learning

K-Means Clustering Tutorial: Visualizing the Machine Learning Algorithm

How to Use the Elbow Method in K-Means Clustering: A Step-by-Step Guide

K-Means++ Algorithm: Solving the Random Initialization Trap in Clustering

Step 1a - Python K-Means Tutorial: Identifying Customer Patterns in Mall Data

Step 1b: K-Means Clustering - Data Preparation in Google Colab/Jupyter

Step 2a - K-Means Clustering in Python: Selecting Relevant Features for Analysis

Step 2b: K-Means Clustering - Optimizing Features for 2D Visualization

Step 3a - Implementing the Elbow Method for K-Means Clustering in Python

Step 3b - Optimizing K-means Clustering: WCSS and Elbow Method Implementation

Step 3c - Plotting the Elbow Method Graph for K-Means Clustering in Python

Step 4 - Creating a Dependent Variable from K-Means Clustering Results in Python

Step 5a: Visualizing K-Means Clusters of Customer Data with Python Scatter

Step 5b - Visualizing K-Means Clusters: Plotting Customer Segments in Python

Step 5c - Analyzing Customer Segments: Insights from K-means Clustering

K-Means Clustering in R: Importing & Exploring Customer Segmentation Data

Step 2 - K-Means Algorithm Implementation: Fitting and Analyzing Mall Data

K-Means Clustering Quiz

Hierarchical Clustering

15 Lectures

How to Perform Hierarchical Clustering: Step-by-Step Guide for Machine Learning

Visualizing Cluster Dissimilarity: Dendrograms in Hierarchical Clustering

Mastering Hierarchical Clustering: Dendrogram Analysis and Threshold Setting

Step 1 - Getting Started with Hierarchical Clustering: Data Setup in Python

Implementing Hierarchical Clustering: Building a Dendrogram with SciPy

Step 2b - Visualizing Hierarchical Clustering: Dendrogram Basics in Python

Step 2c - Interpreting Dendrograms: Optimal Clusters in Hierarchical Clustering

Step 3a - Building a Hierarchical Clustering Model with Scikit-learn in Python

Step 3b - Comparing 3 vs 5 Clusters in Hierarchical Clustering: Python Example

R Data Import for Clustering: Annual Income & Spending Score Analysis

Step 2: Using H.clust in R - Building & Interpreting Dendrograms for Clustering

Step 3 - Implementing Hierarchical Clustering: Using Cat Tree Method in R

Step 4 - Cluster Plot Method: Visualizing Hierarchical Clustering Results in R

Step 5 - Hierarchical Clustering in R: Understanding Customer Spending Patterns

Hierarchical Clustering Quiz

Conclusion of Part 4 - Clustering

-------------------- Part 5: Association Rule Learning --------------------

1 Lectures

Welcome to Part 5 - Association Rule Learning

Apriori

8 Lectures

Apriori Algorithm: Uncovering Hidden Patterns in Data Mining | Association Rules

Step 1 - Association Rule Learning: Boost Sales with Python Data Mining

Step 2 - Creating a List of Transactions for Market Basket Analysis in Python

Step 3 - Configuring Apriori Function: Support, Confidence, and Lift in Python

Step 4: Visualizing Apriori Algorithm Results for Product Deals in Python

Step 1 - Creating a Sparse Matrix for Association Rule Mining in R

Step 2 - Optimizing Apriori Model: Choosing Minimum Support and Confidence

Step 3: Optimizing Product Placement - Apriori Algorithm, Lift & Confidence

Apriori Quiz

Eclat

3 Lectures

Mastering ECLAT: Support-Based Approach to Market Basket Optimization

Python Tutorial: Adapting Apriori to Eclat for Efficient Frequent Itemset Mining

Eclat vs Apriori: Simplified Association Rule Learning in Data Mining

Eclat Quiz

-------------------- Part 6: Reinforcement Learning --------------------

1 Lectures

Welcome to Part 6 - Reinforcement Learning

Upper Confidence Bound (UCB)

13 Lectures

Multi-Armed Bandit: Exploration vs Exploitation in Reinforcement Learning

Upper Confidence Bound Algorithm: Solving Multi-Armed Bandit Problems in ML

UCB in Python: Solving Multi-Armed Bandit Problem - Step 1 Upper Confidence Boun

Step 2: Implementing UCB Algorithm in Python - Data Preparation

Step 3 - Python Code for Upper Confidence Bound: Setting Up Key Variables

Python for RL: Coding the UCB Algorithm Step-by-Step (Step 4)

Step 5 - Coding Upper Confidence Bound: Optimizing Ad Selection in Python

Step 6 - Reinforcement Learning: Finalizing UCB Algorithm in Python

Step 7 - Visualizing UCB Algorithm Results: Histogram Analysis in Python

Step 1 - Exploring Upper Confidence Bound (UCB) in Multi-Armed Bandit Problems

UCB Algorithm: Calculating Average Reward & Confidence Interval (Step 2)

Step 3: Optimizing Ad Selection - UCB & Multi-Armed Bandit Algorithm Explained

Step 4 - UCB Algorithm Performance: Analyzing Ad Selection with Histograms

Upper Confidence Bound Quiz

Thompson Sampling

9 Lectures

Understanding Thompson Sampling Algorithm: Intuition and Implementation

Deterministic vs Probabilistic: UCB and Thompson Sampling in Machine Learning

Step 1 - Python Implementation of Thompson Sampling for Bandit Problems

Step 2 - Optimizing Ad Selection with Thompson Sampling Algorithm in Python

Step 3 - Python Code for Thompson Sampling: Maximizing Random Beta Distributions

Step 4 - Beating UCB with Thompson Sampling: Python Multi-Armed Bandit Tutorial

Additional Resource for this Section

Step 1 - Thompson Sampling vs UCB: Optimizing Ad Click-Through Rates in R

Step 2 - Reinforcement Learning: Thompson Sampling Outperforms UCB Algorithm

Thompson Sampling Quiz

-------------------- Part 7: Natural Language Processing --------------------

25 Lectures

Welcome to Part 7 - Natural Language Processing

NLP Basics: Understanding Bag of Words and Its Applications in Machine Learning

Deep NLP & Sequence-to-Sequence Models: Exploring Natural Language Processing

From If/Else Rules to CNNs: Evolution of Natural Language Processing

Implementing Bag of Words in NLP: A Step-by-Step Tutorial

Step 1 - Getting Started with Natural Language Processing: Sentiment Analysis

Step 2 - Importing TSV Data for Sentiment Analysis: Python NLP Data Processing

Step 3 - Text Cleaning for NLP: Remove Punctuation and Convert to Lowercase

Step 4 - Text Preprocessing: Stemming and Stop Word Removal for NLP in Python

Step 5 - Tokenization and Feature Extraction for Naive Bayes Sentiment Analysis

Step 6 - Training and Evaluating a Naive Bayes Classifier for Sentiment Analysis

Natural Language Processing in Python - EXTRA

Homework Challenge

Step 10 - Text Classification Using Bag-of-Words and Random Forest in R

Warning - Update

Step 1 - NLP Data Preprocessing: Importing TSV Files for Sentiment Analysis

NLP Step 2: Initializing a Corpus for Sentiment Analysis

Step 3 - NLP Data Cleaning: Lowercase Transformation in R for Text Analysis

Step 4 - Sentiment Analysis Data Cleaning: Removing Numbers with TM Map

Step 5 - Cleaning Text Data: Removing Punctuation for NLP and Classification

Step 6 - Simplifying Corpus: Using SnowballC Package to Remove Stop Words in R

Step 7 - Enhancing Text Classification: Stemming for Efficient Feature Matrices

Step 8: Removing Extra Spaces for NLP Sentiment Analysis Text Cleaning

Step 9 - Building a Document-Term Matrix for NLP Text Classification

Natural Language Processing Quiz

-------------------- Part 8: Deep Learning --------------------

2 Lectures

Welcome to Part 8 - Deep Learning

Introduction to Deep Learning: From Historical Context to Modern Applications

Deep Learning Quiz

Artificial Neural Networks

20 Lectures

Understanding CNN Layers: Convolution, ReLU, Pooling, and Flattening Explained

Deep Learning Basics: Exploring Neurons, Synapses, and Activation Functions

Neural Network Basics: Understanding Activation Functions in Deep Learning

How Do Neural Networks Work? Step-by-Step Guide to Deep Learning Algorithms

How Do Neural Networks Learn? Deep Learning Fundamentals Explained

Deep Learning Fundamentals: Gradient Descent vs Brute Force Optimization

Stochastic vs Batch Gradient Descent: Deep Learning Fundamentals

Deep Learning Fundamentals: Training Neural Networks Step-by-Step

Bank Customer Churn Prediction: Machine Learning Model with TensorFlow

Artificial Neural Networks: Predicting Customer Churn with TensorFlow

Step 2 - TensorFlow 2.0 Tutorial: Preprocessing Data for Customer Churn Model

Designing ANN: Sequential Model & Dense Layers for Deep Learning

Train Neural Network: Compile & Fit for Customer Churn Prediction

Step 5 - Implementing ANN for Churn Prediction: From Model to Confusion Matrix

Step 1 - How to Preprocess Data for Artificial Neural Networks in R

Step 2 - How to Install and Initialize H2O for Efficient Deep Learning in R

Step 3: Building Deep Learning Model - H2O Neural Network Layer Config

Step 4 - H2O Deep Learning: Making Predictions and Evaluating Model Accuracy

Deep Learning Additional Content

EXTRA CONTENT: ANN Case Study

ANN QUIZ

Convolutional Neural Networks

17 Lectures

Understanding CNN Layers: Convolution, ReLU, Pooling, and Flattening Explained

Introduction to CNNs: Understanding Deep Learning for Computer Vision

Step 1 - Understanding Convolution in CNNs: Feature Detection and Feature Maps

Step 1 - Applying ReLU to Convolutional Layers: Breaking Up Image Linearity

Step 2 - Max Pooling in CNNs: Enhancing Spatial Invariance for Image Recognition

Step 3 - Understanding Flattening in Convolutional Neural Network Architecture

Step 4 - Fully Connected Layers in CNNs: Optimizing Feature Combination

Deep Learning Basics: How Convolutional Neural Networks (CNNs) Process Images

Deep Learning Essentials: Understanding Softmax and Cross-Entropy in CNNs

Make sure you have your dataset ready

Step 1: Intro to CNNs for Image Classification

Step 2 - Keras ImageDataGenerator: Prevent Overfitting in CNN Models

TensorFlow CNN: Convolution to Output Layer for Vision Tasks (Step 3)

Step 4: CNN Training - Epochs, Loss Function & Metrics in TensorFlow

Step 5 - Making Single Predictions with Convolutional Neural Networks in Python

Hands-on CNN Training: Using Jupyter Notebook for Image Classification

Deep Learning Additional Content #2

CNN Quiz

-------------------- Part 9: Dimensionality Reduction --------------------

1 Lectures

Welcome to Part 9 - Dimensionality Reduction

Principal Component Analysis (PCA)

6 Lectures

PCA Algorithm Intuition: Reducing Dimensions in Unsupervised Learning

PCA Tutorial: Reducing Wine Dataset Features with Scikit-learn in Python

Step 2 - PCA in Action: Reducing Dimensions and Predicting Customer Segments

Step 1 - Understanding Principal Component Analysis for Feature Extraction

Step 2 - Using preProcess Function in R for PCA: Extracting Principal Components

Step 3 - Implementing PCA and SVM for Customer Segmentation: Practical Guide

PCA Quiz

Linear Discriminant Analysis (LDA)

3 Lectures

LDA Intuition: Maximizing Class Separation in Machine Learning Algorithms

Mastering Linear Discriminant Analysis: Step-by-Step Python Implementation

Step-by-Step Guide: Applying LDA for Feature Extraction in Machine Learning

LDA Quiz

Kernel PCA

2 Lectures

Kernel PCA in Python: Improving Classification Accuracy with Feature Extraction

Implementing Kernel PCA for Non-Linear Data: Step-by-Step Guide

-------------------- Part 10: Model Selection & Boosting --------------------

1 Lectures

Welcome to Part 10 - Model Selection & Boosting

Model Selection

6 Lectures

Mastering Model Evaluation: K-Fold Cross-Validation Techniques Explained

How to Master the Bias-Variance Tradeoff in Machine Learning Models

K-Fold Cross-Validation in Python: Improve Machine Learning Model Performance

Optimizing SVM Models with GridSearchCV: A Step-by-Step Python Tutorial

Evaluating ML Model Accuracy: K-Fold Cross-Validation Implementation in R

Optimizing SVM Models with Grid Search: A Step-by-Step R Tutorial

XGBoost

3 Lectures

How to Use XGBoost in Python for Cancer Prediction with High Accuracy

Model Selection and Boosting Additional Content

XGBoost Tutorial: Implementing Gradient Boosting for Classification Problems

Annex: Logistic Regression (Long Explanation)

1 Lectures

Logistic Regression Intuition

Congratulations!! Don't forget your Prize :)

2 Lectures

Huge Congrats for completing the challenge!

Bonus: How To UNLOCK Top Salaries (Live Training)

Instructors

Kirill Eremenko

4.9

315,475 Reviews
345 Students
34 Course

My name is Kirill Eremenko and I am super-psyched that you are reading this!Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists.From my...

Instructors

SuperDataScience Team

4.9

315,475 Reviews
345 Students
34 Course

Hi there,We are the SuperDataScience team. You will hear from us when new SuperDataScience courses are released, when we publish new podcasts, blogs, share cheat sheets, and more!We are here to help you stay on the cutting edge of Data Science and Technology. See you in class,Sincerely,SuperDataScience Team!

Instructors

Ligency Team

4.9

315,475 Reviews
345 Students
34 Course

Hi there,We are the Ligency PR and Marketing team. You will be hearing from us when new courses are released, when we publish new podcasts, blogs, share cheatsheets and more!We are here to help you stay on the cutting edge of Data Science and Technology.See you in class,Sincerely,The Real People at Ligency

Instructors

Hadelin de Ponteves

4.9

315,475 Reviews
345 Students
34 Course

Hadelin is an online entrepreneur who has created 30+ top-rated educational e-courses to the world on new technology topics such as Artificial Intelligence, Machine Learning, Deep Learning, Blockchain and Cryptocurrencies. He is passionate about bringing this knowledge to the world and help as much people as possible. So far more than 2 million students have subscribed to his courses.

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4.9 course rating

4K ratings

Peter M.

4.0

7 months ago

The Course was very good. The practical lessons were fantastic. A few times a bit more explanation would have been appreciated such as with Convolutional Neural Networks that the binary class was being pulled from the folder sub-folder names. This at times made you think through how parts worked. Overall excellent course.

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Bab U. R. P.

5.0

7 months ago

The Best in terms of intuition and understanding. 100% recommended.

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Joel H.

3.0

7 months ago

great course
hands on trading could be shorter allot o repeating but the intuition is too short need more in depth explanation

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Ricardo J. C.

1.0

7 months ago

Not possible to finish te course due to bugged exercises

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Trokon K.

5.0

7 months ago

This was an awesome course!! One of the many things that set this apart from the average online courses is the intuition part before every new topic. I am graduate cybersecurity major and you can't begin to imagine how I wish you guys had an equivalent CyberSecurity A-Z course :), but I am happy there is a course on blockchain. Will be taking that for sure.
Thanks Kirill and Hadelin!

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David K.

4.0

7 months ago

Some of it is outdated, particularly the libraries. Could do with being updated.

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Wee T.

4.0

7 months ago

Theory part is little confusing but overall is good

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Eike H.

3.0

7 months ago

Good overall information.
But the content is drawn out, taking longer than it needed.
The course has 42.5 hours, divided into:
- Intuition lectures
- Python Code
- R Code (I did not listen to this)

The Intuition is a basic explanation of the content with no deep knowledge. The Python-Code is about 80% here is where you copy code and then paste it in and it works. 20% is actual explaining of the code.
There is only a little explanation of the preprocessing of data, which is in my experience the largest part of any deep learning.

Regardless, if you want to have a course that gives a rundown of the possibilities of deep learning, then this course will give you that. But, in my opinion, it will not give you proper coding experience, as most is just copy and paste work.
Thus, if you want to buy it, only buy when it's on sale!

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Milena A. B.

4.5

7 months ago

I found this course to be excellent ! The explanations was clear and well-structured, making even complex topics easy to understand.The efficient pace and practical approach kept me engaged throughout. The content was easy to follow, and the step-by-step guidance was incredibly helpful. Overall, a highly recommended course for everyone looking to learn effectively and confidently!

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Raghuveer

4.0

7 months ago

Explanation is fantastic, but many videos can be merged.
simple topics are split into multiple videos, which is not necessary.

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Ratings

This course includes:

54.5 hours on-demand video
3 articles
249 downloadable resources
Access on mobile and TV
Full lifetime access
Certificate of completion

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More Courses By Kirill Eremenko, SuperDataScience Team, Ligency Team

Peter M.

Bab U. R. P.

Joel H.

Ricardo J. C.

Trokon K.

David K.

Wee T.

Eike H.

Milena A. B.

Raghuveer

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