- Objectifs pédagogiques
- Sections du cours
- Avis
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.
Welcome to the course! Here we will help you get started in the best conditions.
-------------------- Part 1: Data Preprocessing --------------------
Data Preprocessing in Python
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Step 1 - Data Preprocessing in Python: Preparing Your Dataset for ML Models -
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Step 2 - Data Preprocessing Techniques: From Raw Data to ML-Ready Datasets
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Machine Learning Toolkit: Importing NumPy, Matplotlib, and Pandas Libraries
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Step 1 - Machine Learning Basics: Importing Datasets Using Pandas read_csv()
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Step 2 - Using Pandas iloc for Feature Selection in ML Data Preprocessing
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Step 3 - Preprocessing Data: Building X and Y Vectors for ML Model Training
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For Python learners, summary of Object-oriented programming: classes & objects -
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Step 1 - Using Scikit-Learn to Replace Missing Values in Machine Learning
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Step 2 - Imputing Missing Data in Python: SimpleImputer and Numerical Columns
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Step 1 - One-Hot Encoding: Transforming Categorical Features for ML Algorithms
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Step 2 - Handling Categorical Data: One-Hot Encoding with ColumnTransformer
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Step 3 - Preprocessing Categorical Data: One-Hot and Label Encoding Techniques
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Step 1 - How to Prepare Data for Machine Learning: Training vs Test Sets
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Step 2 - Preparing Data: Creating Training and Test Sets in Python for ML Models
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Step 3 - Splitting Data into Training and Test Sets: Best Practices in Python
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Step 1 - Feature Scaling in ML: Why It's Crucial for Data Preprocessing
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Step 2 - How to Scale Numeric Features in Python for ML Preprocessing
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Step 3 - Implementing Feature Scaling: Fit and Transform Methods Explained
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Step 4 - Applying the Same Scaler to Training and Test Sets in Python
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Data Preprocessing in R
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Getting Started with R Programming: Install R and RStudio on Windows & Mac
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Data Preprocessing for Beginners: Preparing Your Dataset for Machine Learning
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Data Preprocessing Tutorial: Understanding Independent vs Dependent Variables
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R Tutorial: Importing and Viewing Datasets for Data Preprocessing
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How to Handle Missing Values in R: Data Preprocessing for Machine Learning
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Using R's Factor Function to Handle Categorical Variables in Data Analysis
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Step 1 - How to Prepare Data for Machine Learning: Training vs Test Sets
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Step 2 - Preparing Data: Creating Training and Test Sets in R for ML Models
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Feature Scaling in ML Step 1: Why It's Crucial for Data Preprocessing
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How to Scale Numeric Features in R for Machine Learning Preprocessing - Step 2
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Essential Steps in Data Preprocessing: Preparing Your Dataset for ML Models
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Data Preprocessing Quiz
-------------------- Part 2: Regression --------------------
Simple Linear Regression
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Simple Linear Regression: Understanding the Equation and Potato Yield Prediction
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How to Find the Best Fit Line: Understanding Ordinary Least Squares Regression
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Step 1a - Mastering Simple Linear Regression: Key Concepts and Implementation
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Step 1b: Data Preprocessing for Linear Regression: Import & Split Data in Python
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Step 2a - Building a Simple Linear Regression Model with Scikit-learn in Python
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Step 2b - Machine Learning Basics: Training a Linear Regression Model in Python
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Step 3 - Using Scikit-Learn's Predict Method for Linear Regression in Python
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Step 4a - Linear Regression: Plotting Real vs Predicted Salaries Visualization
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Step 4b - Evaluating Linear Regression Model Performance on Test Data
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Simple Linear Regression in Python - Additional Lecture
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Step 1 - Data Preprocessing in R: Preparing for Linear Regression Modeling
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Step 2 - Fitting Simple Linear Regression in R: LM Function and Model Summary
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Step 3 - How to Use predict() Function in R for Linear Regression Analysis
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Step 4a - Plotting Linear Regression Data in R: ggplot2 Step-by-Step Guide
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Step 4b - Creating a Scatter Plot with Regression Line in R Using ggplot2
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Step 4c - Comparing Training vs Test Set Predictions in Linear Regression
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Simple Linear Regression Quiz
Multiple Linear Regression
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Startup Success Prediction: Regression Model for VC Fund Decision-Making
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Multiple Linear Regression: Independent Variables & Prediction Models
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Understanding Linear Regression Assumptions: Linearity, Homoscedasticity & More
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How to Handle Categorical Variables in Linear Regression Models
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Multicollinearity in Regression: Understanding the Dummy Variable Trap
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Understanding P-Values and Statistical Significance in Hypothesis Testing
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Backward Elimination: Building Robust Multiple Linear Regression Models
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Step 1a - Hands-On Data Preprocessing for Multiple Linear Regression in Python
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Step 1b - Hands-On Guide: Implementing Multiple Linear Regression in Python
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Step 2a - Hands-on Multiple Linear Regression: Preparing Data in Python
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Step 2b - Multiple Linear Regression in Python: Preparing Your Dataset
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Step 3a - Scikit-learn for Multiple Linear Regression: Efficient Model Building
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Step 3b - Scikit-Learn: Building & Training Multiple Linear Regression Models
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Step 4a: Comparing Real vs Predicted Profits in Linear Regression - Hands-on Gui
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Step 4b - ML in Python: Evaluating Multiple Linear Regression Accuracy
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Multiple Linear Regression in Python - Backward Elimination
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Multiple Linear Regression in Python - EXTRA CONTENT
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Step 1a - Data Preprocessing for MLR: Handling Categorical Data
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Step 1b - Preparing Datasets for Multiple Linear Regression in R
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Step 2a - Multiple Linear Regression in R: Building & Interpreting the Regressor
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Step 2b: Statistical Significance - P-values & Stars in Regression
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Step 3 - How to Use predict() Function in R for Multiple Linear Regression
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Optimizing Multiple Regression Models: Backward Elimination Technique in R
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Mastering Feature Selection: Backward Elimination in R for Linear Regression
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Multiple Linear Regression in R - Automatic Backward Elimination
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Multiple Linear Regression Quiz
Polynomial Regression
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Understanding Polynomial Linear Regression: Applications and Examples
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Step 1a - Building a Polynomial Regression Model for Salary Prediction in Python
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Step 1b - Setting Up Data for Linear vs Polynomial Regression Comparison
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Step 2a: Linear to Polynomial Regression - Preparing Data for Advanced Models
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Step 2b - Transforming Linear to Polynomial Regression: A Step-by-Step Guide
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Step 3a - Plotting Real vs Predicted Salaries: Linear Regression Visualization
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Step 3b - Polynomial vs Linear Regression: Better Fit with Higher Degrees
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Step 4a: Predicting Salaries - Linear Regression in Python (Array Input Guide)
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Step 4b: Python Polynomial Regression - Predicting Salaries Accurately
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Step 1a - Implementing Polynomial Regression in R: HR Salary Analysis Case Study
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Step 1b - ML Fundamentals: Preparing Data for Polynomial Regression
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Step 2a - Building Linear & Polynomial Regression Models in R: A Comparison
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Step 2b - Building a Polynomial Regression Model: Adding Squared & Cubed Terms
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Step 3a: Visualizing Regression Results - Creating Scatter Plots with ggplot2 in
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Step 3b: Visualizing Linear Regression - Plotting Predictions vs Observations
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Step 3c - Polynomial Regression: Curve Fitting for Better Predictions
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Step 4a - How to Make Single Predictions Using Polynomial Regression in R
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Step 4b - Predicting Salaries with Polynomial Regression: A Practical Example
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Step 1 - Building a Reusable Framework for Nonlinear Regression Analysis in R
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Step 2 - Mastering Regression Model Visualization: Increasing Data Resolution
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Polynomial Regression Quiz
Support Vector Regression (SVR)
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How Does Support Vector Regression (SVR) Differ from Linear Regression?
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RBF Kernel SVR: From Linear to Non-Linear Support Vector Regression
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Step 1a - SVR Model Training: Feature Scaling and Dataset Preparation in Python
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Step 1b - SVR in Python: Importing Libraries and Dataset for Machine Learning
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Step 2a - Mastering Feature Scaling for Support Vector Regression in Python
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Step 2b: Reshaping Data for SVR - Preparing Y Vector for Feature Scaling (Python
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Step 2c: SVR Data Prep - Scaling X & Y Independently with StandardScaler
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Step 3: SVM Regression: Creating & Training SVR Model with RBF Kernel in Python
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Step 4 - SVR Model Prediction: Handling Scaled Data and Inverse Transformation
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Step 5a - How to Plot Support Vector Regression (SVR) Models: Step-by-Step Guide
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Step 5b - SVR: Scaling & Inverse Transformation in Python
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Step 1 - SVR Tutorial: Creating a Support Vector Machine Regressor in R
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Step 2 - Support Vector Regression: Building a Predictive Model in Python
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SVR Quiz
Decision Tree Regression
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How to Build a Regression Tree: Step-by-Step Guide for Machine Learning
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Step 1a - Decision Tree Regression: Building a Model without Feature Scaling
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Step 1b: Uploading & Preprocessing Data for Decision Tree Regression in Python
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Step 2 - Implementing DecisionTreeRegressor: A Step-by-Step Guide in Python
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Step 3 - Implementing Decision Tree Regression in Python: Making Predictions
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Step 4 - Visualizing Decision Tree Regression: High-Resolution Results
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Step 1 - Creating a Decision Tree Regressor: Using rpart Function in R
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Step 2 - Decision Tree Regression: Fixing Splits with rpart Control Parameter
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Step 3: Non-Continuous Regression - Decision Tree Visualization Challenges
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Step 4 - Visualizing Decision Tree: Understanding Intervals and Predictions
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Decision Tree Regression Quiz
Random Forest Regression
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Understanding Random Forest Algorithm: Intuition and Application in ML
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Step 1 - Building a Random Forest Regression Model with Python and Scikit-Learn
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Step 2 - Creating a Random Forest Regressor: Key Parameters and Model Fitting
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Step 1 - Building a Random Forest Model in R: Regression Tutorial
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Step 2 - Visualizing Random Forest Regression: Interpreting Stairs and Splits
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Step 3 - Fine-Tuning Random Forest: From 10 to 500 Trees for Accurate Prediction
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Random Forest Regression Quiz
Evaluating Regression Models Performance
Regression Model Selection in Python
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Make sure you have this Model Selection folder ready
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Step 1 - Mastering Regression Toolkit: Comparing Models for Optimal Performance
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Step 2 - Creating Generic Code Templates for Various Regression Models in Python
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Step 3: Evaluating Regression Models - R-Squared & Performance Metrics Explained
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Step 4 - Implementing R-Squared Score in Python with Scikit-Learn's Metrics
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Step 1 - Selecting the Best Regression Model: R-squared Evaluation in Python
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Step 2 - Selecting the Best Regression Model: Random Forest vs. SVR Performance
Regression Model Selection in R
-------------------- Part 3: Classification --------------------
Logistic Regression
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Understanding Logistic Regression: Predicting Categorical Outcomes
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Logistic Regression: Finding the Best Fit Curve Using Maximum Likelihood
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Step 1a - Building a Logistic Regression Model for Customer Behavior Prediction
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Step 1b - Implementing Logistic Regression in Python: Data Preprocessing Guide
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Step 2a: Python Data Preprocessing for Logistic Regression Dataset Prep
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Step 2b - Data Preprocessing: Feature Scaling Techniques for Logistic Regression
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Step 3a - How to Import and Use LogisticRegression Class from Scikit-learn
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Step 3b - Training Logistic Regression Model: Fit Method for Classification
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Step 4a - Formatting Single Observation Input for Logistic Regression Predict
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Step 4b: Predicted vs. Real Purchase Decisions in Logistic Regression
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Step 5 - Comparing Predicted vs Real Results: Python Logistic Regression Guide
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Step 6a - Implementing Confusion Matrix and Accuracy Score in Scikit-Learn
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Step 6b: Evaluating Classification Models - Confusion Matrix & Accuracy Metrics
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Step 7a - Visualizing Logistic Regression Decision Boundaries in Python: 2D Plot
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Step 7b - Interpreting Logistic Regression Results: Prediction Regions Explained
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Step 7c - Visualizing Logistic Regression Performance on New Data in Python
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Logistic Regression in Python - Step 7 (Colour-blind friendly image)
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Step 1 - Data Preprocessing for Logistic Regression in R: Preparing Your Dataset
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Step 2 - How to Create a Logistic Regression Classifier Using R's GLM Function
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Step 3 - How to Use R for Logistic Regression Prediction: Step-by-Step Guide
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Step 4 - How to Assess Model Accuracy Using a Confusion Matrix in R
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Warning - Update
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Step 5a - Interpreting Logistic Regression Plots: Prediction Regions Explained
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Step 5b: Logistic Regression - Linear Classifiers & Prediction Boundaries
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Step 5c - Data Viz in R: Colorizing Pixels for Logistic Regression
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Logistic Regression in R - Step 5 (Colour-blind friendly image)
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Optimizing R Scripts for Machine Learning: Building a Classification Template
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Machine Learning Regression and Classification EXTRA
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Logistic Regression Quiz
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EXTRA CONTENT: Logistic Regression Practical Case Study
K-Nearest Neighbors (K-NN)
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K-Nearest Neighbors (KNN) Explained: A Beginner's Guide to Classification
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Step 1 - Python KNN Tutorial: Classifying Customer Data for Targeted Marketing
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Step 2 - Building a K-Nearest Neighbors Model: Scikit-Learn KNeighborsClassifier
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Step 3 - Visualizing KNN Decision Boundaries: Python Tutorial for Beginners
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Step 1 - Implementing KNN Classification in R: Setup & Data Preparation
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Step 2 - Building a KNN Classifier: Preparing Training and Test Sets in R
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Step 3 - Implementing KNN Classification in R: Adapting the Classifier Template
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K-Nearest Neighbor Quiz
Support Vector Machine (SVM)
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Support Vector Machines Explained: Hyperplanes and Support Vectors in ML
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Step 1 - Building a Support Vector Machine Model with Scikit-learn in Python
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Step 2 - Building a Support Vector Machine Model with Sklearn's SVC in Python
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Step 3 - Understanding Linear SVM Limitations: Why It Didn't Beat kNN Classifier
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Step 1 - Building a Linear SVM Classifier in R: Data Import and Initial Setup
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Step 2: Creating & Evaluating Linear SVM Classifier in R - Predictions & Results
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SVM Quiz
Kernel SVM
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From Linear to Non-Linear SVM: Exploring Higher Dimensional Spaces
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Support Vector Machines: Transforming Non-Linear Data for Linear Separation
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Kernel Trick: SVM Machine Learning for Non-Linear Classification
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Understanding Different Types of Kernel Functions for Machine Learning
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Mastering Support Vector Regression: Non-Linear SVR with RBF Kernel Explained
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Step 1 - Python Kernel SVM: Applying RBF to Solve Non-Linear Classification
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Step 2 - Mastering Kernel SVM: Improving Accuracy with Non-Linear Classifiers
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Step 1 - Kernel SVM vs Linear SVM: Overcoming Non-Linear Separability in R
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Step 2 - Building a Gaussian Kernel SVM Classifier for Advanced Machine Learning
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Step 3: Visualizing Kernel SVM - Non-Linear Classification in Machine Learning
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Kernel SVM Quiz
Naive Bayes
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214
Understanding Bayes' Theorem Intuitively: From Probability to Machine Learning
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Understanding Naive Bayes Algorithm: Probabilistic Classification Explained
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Bayes Theorem in Machine Learning: Step-by-Step Probability Calculation
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Why is Naive Bayes Called Naive? Understanding the Algorithm's Assumptions
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Step 1 - Naive Bayes in Python: Applying ML to Social Network Ads Optimisation
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Step 2 - Python Naive Bayes: Training and Evaluating a Classifier on Real Data
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Step 3 - Analyzing Naive Bayes Algorithm Results: Accuracy and Predictions
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Step 1 - Getting Started with Naive Bayes Algorithm in R for Classification
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Step 2 - Troubleshooting Naive Bayes Classification: Empty Prediction Vectors
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Step 3 - Visualizing Naive Bayes Results: Creating Confusion Matrix and Graphs
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Naive Bayes Quiz
Decision Tree Classification
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How Decision Tree Algorithms Work: Step-by-Step Guide with Examples
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Step 1 - Implementing Decision Tree Classification in Python with Scikit-learn
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Step 2 - Training a Decision Tree Classifier: Optimizing Performance in Python
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Step 1 - R Tutorial: Creating a Decision Tree Classifier with rpart Library
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Step 2 - Decision Tree Classifier: Optimizing Prediction Boundaries in R
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Step 3 - Decision Tree Visualization: Exploring Splits and Conditions in R
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Decision Tree Classification Quiz
Random Forest Classification
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Understanding Random Forest: Decision Trees and Majority Voting Explained
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Step 1 - Implementing Random Forest Classification in Python with Scikit-Learn
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Step 2: Random Forest Evaluation - Confusion Matrix & Accuracy Metrics
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Step 1: Random Forest Classifier - From Template to Implementation in R
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Step 2: Random Forest Classification - Visualizing Predictions & Results
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Step 3 - Evaluating Random Forest Performance: Test Set Results & Overfitting
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Random Forest Classification Quiz
Classification Model Selection in Python
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Make sure you have this Model Selection folder ready
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Mastering the Confusion Matrix: True Positives, Negatives, and Errors
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Step 1 - How to Choose the Right Classification Algorithm for Your Dataset
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Step 2 - Optimizing Model Selection: Streamlined Classification Code in Python
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Step 3 - Evaluating Classification Algorithms: Accuracy Metrics in Python
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Step 4 - Model Selection Process: Evaluating Classification Algorithms
Evaluating Classification Models Performance
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Logistic Regression: Interpreting Predictions and Errors in Data Science
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Machine Learning Model Evaluation: Accuracy Paradox and Better Metrics
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Understanding CAP Curves: Assessing Model Performance in Data Science 2024
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Mastering CAP Analysis: Assessing Classification Models with Accuracy Ratio
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Conclusion of Part 3 - Classification
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Evaluating Classiification Model Performance Quiz
-------------------- Part 4: Clustering --------------------
K-Means Clustering
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What is Clustering in Machine Learning? Introduction to Unsupervised Learning
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K-Means Clustering Tutorial: Visualizing the Machine Learning Algorithm
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How to Use the Elbow Method in K-Means Clustering: A Step-by-Step Guide
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K-Means++ Algorithm: Solving the Random Initialization Trap in Clustering
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Step 1a - Python K-Means Tutorial: Identifying Customer Patterns in Mall Data
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Step 1b: K-Means Clustering - Data Preparation in Google Colab/Jupyter
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Step 2a - K-Means Clustering in Python: Selecting Relevant Features for Analysis
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Step 2b: K-Means Clustering - Optimizing Features for 2D Visualization
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Step 3a - Implementing the Elbow Method for K-Means Clustering in Python
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Step 3b - Optimizing K-means Clustering: WCSS and Elbow Method Implementation
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Step 3c - Plotting the Elbow Method Graph for K-Means Clustering in Python
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Step 4 - Creating a Dependent Variable from K-Means Clustering Results in Python
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Step 5a: Visualizing K-Means Clusters of Customer Data with Python Scatter
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Step 5b - Visualizing K-Means Clusters: Plotting Customer Segments in Python
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Step 5c - Analyzing Customer Segments: Insights from K-means Clustering
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Step 1 - K-Means Clustering in R: Importing & Exploring Segmentation Data
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Step 2 - K-Means Algorithm Implementation in R: Fitting and Analyzing Mall Data
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K-Means Clustering Quiz
Hierarchical Clustering
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270
How to Perform Hierarchical Clustering: Step-by-Step Guide for Machine Learning
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Visualizing Cluster Dissimilarity: Dendrograms in Hierarchical Clustering
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Mastering Hierarchical Clustering: Dendrogram Analysis and Threshold Setting
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Step 1 - Getting Started with Hierarchical Clustering: Data Setup in Python
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Step 2a - Implementing Hierarchical Clustering: Building a Dendrogram with SciPy
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Step 2b - Visualizing Hierarchical Clustering: Dendrogram Basics in Python
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Step 2c - Interpreting Dendrograms: Optimal Clusters in Hierarchical Clustering
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Step 3a - Building a Hierarchical Clustering Model with Scikit-learn in Python
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Step 3b - Comparing 3 vs 5 Clusters in Hierarchical Clustering: Python Example
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Step 1 - R Data Import for Clustering: Annual Income & Spending Score Analysis
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Step 2: Using H.clust in R - Building & Interpreting Dendrograms for Clustering
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Step 3 - Implementing Hierarchical Clustering: Using Cat Tree Method in R
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Step 4 - Cluster Plot Method: Visualizing Hierarchical Clustering Results in R
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Step 5 - Hierarchical Clustering in R: Understanding Customer Spending Patterns
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Hierarchical Clustering Quiz
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Conclusion of Part 4 - Clustering
-------------------- Part 5: Association Rule Learning --------------------
Apriori
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Apriori Algorithm: Uncovering Hidden Patterns in Data Mining | Association Rules
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Step 1 - Association Rule Learning: Boost Sales with Python Data Mining
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Step 2 - Creating a List of Transactions for Market Basket Analysis in Python
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Step 3 - Configuring Apriori Function: Support, Confidence, and Lift in Python
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Step 4: Visualizing Apriori Algorithm Results for Product Deals in Python
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Step 1 - Creating a Sparse Matrix for Association Rule Mining in R
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Step 2 - Optimizing Apriori Model: Choosing Minimum Support and Confidence
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294
Step 3: Optimizing Product Placement - Apriori Algorithm, Lift & Confidence
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295
Apriori Quiz
Eclat
-------------------- Part 6: Reinforcement Learning --------------------
Upper Confidence Bound (UCB)
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301
Multi-Armed Bandit: Exploration vs Exploitation in Reinforcement Learning
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302
Upper Confidence Bound Algorithm: Solving Multi-Armed Bandit Problems in ML
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303
Step 1 - Upper Confidence Bound: Solving Multi-Armed Bandit Problem in Python
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304
Step 2: Implementing UCB Algorithm in Python - Data Preparation
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305
Step 3 - Python Code for Upper Confidence Bound: Setting Up Key Variables
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306
Step 4 - Python for RL: Coding the UCB Algorithm Step-by-Step
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307
Step 5 - Coding Upper Confidence Bound: Optimizing Ad Selection in Python
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308
Step 6 - Reinforcement Learning: Finalizing UCB Algorithm in Python
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Step 7 - Visualizing UCB Algorithm Results: Histogram Analysis in Python
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310
Step 1 - Exploring Upper Confidence Bound in R: Multi-Armed Bandit Problems
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311
Step 2 - UCB Algorithm in R: Calculating Average Reward & Confidence Interval
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Step 3: Optimizing Ad Selection - UCB & Multi-Armed Bandit Algorithm Explained
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313
Step 4 - UCB Algorithm Performance: Analyzing Ad Selection with Histograms
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314
Upper Confidence Bound Quiz
Thompson Sampling
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315
Understanding Thompson Sampling Algorithm: Intuition and Implementation
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316
Deterministic vs Probabilistic: UCB and Thompson Sampling in Machine Learning
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317
Step 1 - Python Implementation of Thompson Sampling for Bandit Problems
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318
Step 2 - Optimizing Ad Selection with Thompson Sampling Algorithm in Python
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319
Step 3 - Python Code for Thompson Sampling: Maximizing Random Beta Distributions
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320
Step 4 - Beating UCB with Thompson Sampling: Python Multi-Armed Bandit Tutorial
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321
Additional Resource for this Section
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322
Step 1 - Thompson Sampling vs UCB: Optimizing Ad Click-Through Rates in R
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323
Step 2 - Reinforcement Learning: Thompson Sampling Outperforms UCB Algorithm
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324
Thompson Sampling Quiz
-------------------- Part 7: Natural Language Processing --------------------
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325
Welcome to Part 7 - Natural Language Processing
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326
NLP Basics: Understanding Bag of Words and Its Applications in Machine Learning
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327
Deep NLP & Sequence-to-Sequence Models: Exploring Natural Language Processing
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328
From If/Else Rules to CNNs: Evolution of Natural Language Processing
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329
Implementing Bag of Words in NLP: A Step-by-Step Tutorial
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330
Step 1 - Getting Started with Natural Language Processing: Sentiment Analysis
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331
Step 2 - Importing TSV Data for Sentiment Analysis: Python NLP Data Processing
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332
Step 3 - Text Cleaning for NLP: Remove Punctuation and Convert to Lowercase
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333
Step 4 - Text Preprocessing: Stemming and Stop Word Removal for NLP in Python
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334
Step 5 - Tokenization and Feature Extraction for Naive Bayes Sentiment Analysis
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335
Step 6 - Training and Evaluating a Naive Bayes Classifier for Sentiment Analysis
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336
Natural Language Processing in Python - EXTRA
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337
Homework Challenge
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338
Step 1 - Text Classification Using Bag-of-Words and Random Forest in R
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339
Warning - Update
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340
Step 2 - NLP Data Preprocessing in R: Importing TSV Files for Sentiment Analysis
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341
Step 3 - NLP in R: Initialising a Corpus for Sentiment Analysis
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342
Step 4 - NLP Data Cleaning: Lowercase Transformation in R for Text Analysis
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343
Step 5 - Sentiment Analysis Data Cleaning: Removing Numbers with TM Map
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344
Step 6 - Cleaning Text Data: Removing Punctuation for NLP and Classification
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345
Step 7 - Simplifying Corpus: Using SnowballC Package to Remove Stop Words in R
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346
Step 8 - Enhancing Text Classification: Stemming for Efficient Feature Matrices
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347
Step 9: Removing Extra Spaces for NLP Sentiment Analysis Text Cleaning
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348
Step 10 - Building a Document-Term Matrix for NLP Text Classification
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349
Homework Challenge
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350
Natural Language Processing Quiz
-------------------- Part 8: Deep Learning --------------------
Artificial Neural Networks
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354
Understanding CNN Layers: Convolution, ReLU, Pooling, and Flattening Explained
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355
Deep Learning Basics: Exploring Neurons, Synapses, and Activation Functions
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356
Neural Network Basics: Understanding Activation Functions in Deep Learning
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357
How Do Neural Networks Work? Step-by-Step Guide to Deep Learning Algorithms
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358
How Do Neural Networks Learn? Deep Learning Fundamentals Explained
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359
Deep Learning Fundamentals: Gradient Descent vs Brute Force Optimization
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360
Stochastic vs Batch Gradient Descent: Deep Learning Fundamentals
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361
Deep Learning Fundamentals: Training Neural Networks Step-by-Step
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362
Bank Customer Churn Prediction: Machine Learning Model with TensorFlow
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363
Step 1 ANN in Python: Predicting Customer Churn with TensorFlow
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364
Step 2 - TensorFlow 2.0 Tutorial: Preprocessing Data for Customer Churn Model
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365
Step 3 - Designing ANN: Sequential Model & Dense Layers for Deep Learning
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366
Step 4 - Train Neural Network: Compile & Fit for Customer Churn Prediction
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367
Step 5 - Implementing ANN for Churn Prediction: From Model to Confusion Matrix
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368
Step 1 - How to Preprocess Data for Artificial Neural Networks in R
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369
Step 2 - How to Install and Initialize H2O for Efficient Deep Learning in R
-
370
Step 3: Building Deep Learning Model - H2O Neural Network Layer Config
-
371
Step 4 - H2O Deep Learning: Making Predictions and Evaluating Model Accuracy
-
372
Deep Learning Additional Content
-
373
EXTRA CONTENT: ANN Case Study
-
374
ANN QUIZ
Convolutional Neural Networks
-
375
Understanding CNN Layers: Convolution, ReLU, Pooling, and Flattening Explained
-
376
Introduction to CNNs: Understanding Deep Learning for Computer Vision
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377
Step 1 - Understanding Convolution in CNNs: Feature Detection and Feature Maps
-
378
Step 1b - Applying ReLU to Convolutional Layers: Breaking Up Image Linearity
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379
Step 2 - Max Pooling in CNNs: Enhancing Spatial Invariance for Image Recognition
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380
Step 3 - Understanding Flattening in Convolutional Neural Network Architecture
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381
Step 4 - Fully Connected Layers in CNNs: Optimizing Feature Combination
-
382
Deep Learning Basics: How Convolutional Neural Networks (CNNs) Process Images
-
383
Deep Learning Essentials: Understanding Softmax and Cross-Entropy in CNNs
-
384
Make sure you have your dataset ready
-
385
Step 1: Intro to CNNs for Image Classification
-
386
Step 2 - Keras ImageDataGenerator: Prevent Overfitting in CNN Models
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387
Step 3 - TensorFlow CNN: Convolution to Output Layer for Vision Tasks
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388
Step 4: CNN Training - Epochs, Loss Function & Metrics in TensorFlow
-
389
Step 5 - Making Single Predictions with Convolutional Neural Networks in Python
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390
Hands-on CNN Training: Using Jupyter Notebook for Image Classification
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391
Deep Learning Additional Content #2
-
392
CNN Quiz
-------------------- Part 9: Dimensionality Reduction --------------------
Principal Component Analysis (PCA)
-
394
PCA Algorithm Intuition: Reducing Dimensions in Unsupervised Learning
-
395
Step 1 PCA in Python : Reducing Wine Dataset Features with Scikit-learn
-
396
Step 2 - PCA in Action: Reducing Dimensions and Predicting Customer Segments
-
397
Step 1 in R - Understanding Principal Component Analysis for Feature Extraction
-
398
Step 2 - Using preProcess Function in R for PCA: Extracting Principal Components
-
399
Step 3 - Implementing PCA and SVM for Customer Segmentation: Practical Guide
-
400
PCA Quiz
Linear Discriminant Analysis (LDA)
Kernel PCA
-------------------- Part 10: Model Selection & Boosting --------------------
Model Selection
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408
Mastering Model Evaluation: K-Fold Cross-Validation Techniques Explained
-
409
How to Master the Bias-Variance Tradeoff in Machine Learning Models
-
410
K-Fold Cross-Validation in Python: Improve Machine Learning Model Performance
-
411
Optimizing SVM Models with GridSearchCV: A Step-by-Step Python Tutorial
-
412
Evaluating ML Model Accuracy: K-Fold Cross-Validation Implementation in R
-
413
Optimizing SVM Models with Grid Search: A Step-by-Step R Tutorial
XGBoost
Annex: Logistic Regression (Long Explanation)
Congratulations!! Don't forget your Prize :)
Étoiles 5
118434
Étoiles 4
49346
Étoiles 3
19738
Étoiles 2
5921
Étoiles 1
3947
Détails du cours
Vidéo
42.75 heure(s)
Sections
46
Certificat d'achèvement
Durée totale du cours
Accès sur le mobile et la télévision