Machine Learning A to Z with AI

Go from beginner to job-ready — master every core ML algorithm with hands-on projects in Python & R.

Machine Learning A to Z with AI takes you from absolute beginner to confident practitioner — covering every major ML technique with hands-on projects in both Python and R. You'll learn the intuition behind each algorithm first, then build it yourself: from regression and classification to clustering, NLP, and deep learning. By the end, you'll be able to build real-world models, make data-driven decisions, and step into AI and data-science roles with confidence.

What You'll Learn

Master data preprocessing in both Python and R
Build regression models: Linear, Polynomial, SVR, Decision Tree & Random Forest
Apply classification: Logistic Regression, K-NN, SVM, Kernel SVM & Naive Bayes
Cluster data with K-Means and Hierarchical Clustering
Mine patterns with Apriori & Eclat, plus Reinforcement Learning (UCB & Thompson Sampling)
Implement NLP and Deep Learning (ANN & CNN) with TensorFlow
Reduce dimensions with PCA, LDA & Kernel PCA
Select and boost models with K-Fold Cross-Validation and XGBoost

Course Curriculum

46 Modules · 394 Lessons

Module 01: Welcome to the Course 3 lessons

Welcome to the Course — Predict Car Purchases with Python & Scikit-learn in 5 Minutes
How to Use Google Colab & the Machine Learning Course Folder
Getting Started with R: Install R and RStudio on Windows & Mac

Module 02: Part 1 — Data Preprocessing 3 lessons

Machine Learning Workflow: Importing, Modeling, and Evaluating Your ML Model
Data Preprocessing: Importance of the Training/Test Split in ML Model Evaluation
Feature Scaling: Normalization vs Standardization Explained

Module 03: Data Preprocessing in Python 18 lessons

Data Preprocessing in Python: Preparing Your Dataset for ML Models
Data Preprocessing Techniques: From Raw Data to ML-Ready Datasets
ML Toolkit: Importing NumPy, Matplotlib, and Pandas Libraries
Importing Datasets Using Pandas read_csv()
Using Pandas iloc for Feature Selection in Data Preprocessing
Building X and Y Vectors for ML Model Training
Using Scikit-Learn to Replace Missing Values
Imputing Missing Data: SimpleImputer and Numerical Columns
One-Hot Encoding: Transforming Categorical Features for ML Algorithms
Handling Categorical Data: One-Hot Encoding with ColumnTransformer
Preprocessing Categorical Data: One-Hot and Label Encoding
How to Prepare Data for ML: Training vs Test Sets
Creating Training and Test Sets in Python
Splitting Data into Training and Test Sets: Best Practices
Feature Scaling: Why It's Crucial for Data Preprocessing
How to Scale Numeric Features in Python
Implementing Feature Scaling: Fit and Transform Methods Explained
Applying the Same Scaler to Training and Test Sets

Module 04: Data Preprocessing in R 10 lessons

Data Preprocessing for Beginners: Preparing Your Dataset
Understanding Independent vs Dependent Variables
R Tutorial: Importing and Viewing Datasets
How to Handle Missing Values in R
Using R's Factor Function to Handle Categorical Variables
How to Prepare Data for ML: Training vs Test Sets
Creating Training and Test Sets in R
Feature Scaling Step 1: Why It's Crucial
How to Scale Numeric Features in R — Step 2
Essential Steps in Data Preprocessing

Module 05: Part 2 — Regression 1 lesson

Welcome to Part 2 — Regression

Module 06: Simple Linear Regression 15 lessons

Simple Linear Regression: Understanding the Equation & Yield Prediction
How to Find the Best-Fit Line: Ordinary Least Squares Regression
Step 1a: Simple Linear Regression — Key Concepts and Implementation
Step 1b: Data Preprocessing for Linear Regression in Python
Step 2a: Building a Simple Linear Regression Model with Scikit-learn
Step 2b: Training a Linear Regression Model in Python
Step 3: Using Scikit-Learn's Predict Method for Linear Regression
Step 4a: Plotting Real vs Predicted Salaries
Step 4b: Evaluating Linear Regression Performance on Test Data
Step 1: Data Preprocessing in R for Linear Regression
Step 2: Fitting Simple Linear Regression in R (lm Function)
Step 3: Using the predict() Function in R
Step 4a: Plotting Linear Regression Data in R with ggplot2
Step 4b: Scatter Plot with Regression Line in R
Step 4c: Comparing Training vs Test Set Predictions

Module 07: Multiple Linear Regression 24 lessons

Startup Success Prediction: A Regression Model for VC Decisions
Multiple Linear Regression: Independent Variables & Prediction Models
Regression Assumptions: Linearity, Homoscedasticity & More
How to Handle Categorical Variables in Linear Regression
Multicollinearity: Understanding the Dummy Variable Trap
Understanding P-Values and Statistical Significance
Backward Elimination: Building Robust Regression Models
Step 1a: Data Preprocessing for Multiple Linear Regression (Python)
Step 1b: Implementing Multiple Linear Regression in Python
Step 2a: Preparing Data for MLR in Python
Step 2b: Multiple Linear Regression — Preparing Your Dataset
Step 3a: Scikit-learn for Efficient MLR Model Building
Step 3b: Building & Training Multiple Linear Regression Models
Step 4a: Comparing Real vs Predicted Profits
Step 4b: Evaluating Multiple Linear Regression Accuracy
Step 1a: Data Preprocessing for MLR — Handling Categorical Data (R)
Step 1b: Preparing Datasets for MLR in R
Step 2a: Building & Interpreting the Regressor in R
Step 2b: Statistical Significance — P-values & Stars
Step 3: Using predict() for Multiple Linear Regression in R
Optimizing Regression Models: Backward Elimination in R
Feature Selection: Backward Elimination in R
Multiple Linear Regression in R — Automatic Backward Elimination
Multiple Linear Regression Quiz

Module 08: Polynomial Regression 19 lessons

Understanding Polynomial Regression: Applications and Examples
Step 1a: Building a Polynomial Regression Model (Python)
Step 1b: Setting Up Data for Linear vs Polynomial Comparison
Step 2a: Preparing Data for Advanced Models
Step 2b: Transforming Linear to Polynomial Regression
Step 3a: Plotting Real vs Predicted Salaries
Step 3b: Polynomial vs Linear — Better Fit with Higher Degrees
Step 4a: Predicting Salaries — Linear Regression in Python
Step 4b: Polynomial Regression — Predicting Salaries Accurately
Step 1a: Polynomial Regression in R — HR Salary Case Study
Step 1b: Preparing Data for Polynomial Regression
Step 2a: Building Linear & Polynomial Models in R
Step 2b: Adding Squared & Cubed Terms
Step 3a: Visualizing Regression Results with ggplot2
Step 3b: Plotting Predictions vs Observations
Step 3c: Polynomial Regression Curve Fitting
Step 4a: Single Predictions Using Polynomial Regression in R
Step 4b: Predicting Salaries — A Practical Example
Step 1: A Reusable Framework for Nonlinear Regression in R

Module 09: Support Vector Regression (SVR) 14 lessons

How SVR Differs from Linear Regression
RBF Kernel SVR: From Linear to Non-Linear Regression
Step 1a: SVR Model Training — Feature Scaling & Dataset Prep (Python)
Step 1b: SVR in Python — Importing Libraries and Dataset
Step 2a: Feature Scaling for SVR in Python
Step 2b: Reshaping Data — Preparing the Y Vector
Step 2c: Scaling X & Y Independently with StandardScaler
Step 3: Training the SVR Model with an RBF Kernel
Step 4: SVR Prediction — Handling Scaled Data & Inverse Transformation
Step 5a: How to Plot Support Vector Regression Models
Step 5b: SVR Scaling & Inverse Transformation in Python
Step 1: Creating a Support Vector Machine Regressor in R
Step 2: Building a Predictive SVR Model
SVR Quiz

Module 10: Decision Tree Regression 11 lessons

How to Build a Regression Tree — Step-by-Step
Step 1a: Decision Tree Regression Without Feature Scaling
Step 1b: Uploading & Preprocessing Data (Python)
Step 2: Implementing DecisionTreeRegressor in Python
Step 3: Making Predictions with Decision Tree Regression
Step 4: Visualizing Decision Tree Regression (High-Resolution)
Step 1: Creating a Decision Tree Regressor with rpart in R
Step 2: Fixing Splits with rpart Control Parameters
Step 3: Non-Continuous Regression — Visualization Challenges
Step 4: Understanding Intervals and Predictions
Decision Tree Regression Quiz

Module 11: Random Forest Regression 7 lessons

Understanding the Random Forest Algorithm — Intuition & Application
Step 1: Building a Random Forest Regression Model (Python)
Step 2: Key Parameters and Model Fitting
Step 1: Building a Random Forest Model in R
Step 2: Interpreting Stairs and Splits
Step 3: Fine-Tuning from 10 to 500 Trees
Random Forest Regression Quiz

Module 12: Evaluating Regression Models Performance 3 lessons

Understanding R-Squared: Goodness of Fit
Understanding Adjusted R-Squared
Evaluating Regression Models Performance Quiz

Module 13: Regression Model Selection in Python 7 lessons

Step 1: Comparing Models for Optimal Performance
Step 2: Generic Code Templates for Regression Models
Step 3: R-Squared & Performance Metrics Explained
Step 4: Implementing R-Squared Score with Scikit-Learn
Step 1: Selecting the Best Regression Model
Step 2: Random Forest vs SVR Performance
Conclusion of Part 2 — Regression

Module 14: Regression Model Selection in R 3 lessons

R-Squared vs Adjusted R-Squared Explained
Interpreting Coefficients for Business Decisions
Conclusion of Part 2 — Regression

Module 15: Part 3 — Classification 2 lessons

Welcome to Part 3 — Classification
What is Classification in Machine Learning

Module 16: Logistic Regression 28 lessons

Understanding Logistic Regression: Predicting Categorical Outcomes
Finding the Best-Fit Curve Using Maximum Likelihood
Step 1a: Logistic Regression for Customer Behavior Prediction
Step 1b: Data Preprocessing for Logistic Regression (Python)
Step 2a: Dataset Prep for Logistic Regression
Step 2b: Feature Scaling Techniques
Step 3a: Importing & Using the LogisticRegression Class
Step 3b: Training the Model with the Fit Method
Step 4a: Formatting Single Observation Input for Predict
Step 4b: Predicted vs Real Purchase Decisions
Step 5: Comparing Predicted vs Real Results
Step 6a: Confusion Matrix and Accuracy Score in Scikit-Learn
Step 6b: Confusion Matrix & Accuracy Metrics
Step 7a: Visualizing Decision Boundaries (2D Plot)
Step 7b: Interpreting Prediction Regions
Step 7c: Visualizing Performance on New Data
Logistic Regression in Python — Step 7 Recap
Step 1: Data Preprocessing for Logistic Regression in R
Step 2: Creating a Classifier Using R's GLM Function
Step 3: Logistic Regression Prediction in R
Step 4: Assessing Accuracy with a Confusion Matrix
Step 5a: Interpreting Prediction Regions in R
Step 5b: Linear Classifiers & Prediction Boundaries
Step 5c: Colorizing Pixels for Logistic Regression in R
Logistic Regression in R — Step 5 Recap
Building a Reusable Classification Template in R
Machine Learning Regression and Classification — EXTRA
Logistic Regression Quiz

Module 17: K-Nearest Neighbors (K-NN) 8 lessons

K-Nearest Neighbors Explained: A Beginner's Guide
Step 1: Python KNN — Classifying Customer Data
Step 2: Building a KNN Model with KNeighborsClassifier
Step 3: Visualizing KNN Decision Boundaries
Step 1: Implementing KNN Classification in R
Step 2: Preparing Training and Test Sets in R
Step 3: Adapting the Classifier Template in R
K-Nearest Neighbors Quiz

Module 18: Support Vector Machine (SVM) 6 lessons

Support Vector Machines Explained: Hyperplanes & Support Vectors
Step 1: Building an SVM Model with Scikit-learn
Step 2: Building an SVM Model with Sklearn's SVC
Step 3: Linear SVM Limitations vs KNN
Step 1: Building a Linear SVM Classifier in R
Step 2: Evaluating the Linear SVM Classifier in R

Module 19: Kernel SVM 11 lessons

From Linear to Non-Linear SVM: Higher Dimensional Spaces
Transforming Non-Linear Data for Linear Separation
The Kernel Trick for Non-Linear Classification
Understanding Different Types of Kernel Functions
Non-Linear SVR with the RBF Kernel Explained
Step 1: Python Kernel SVM — Applying RBF
Step 2: Improving Accuracy with Non-Linear Classifiers
Step 1: Kernel SVM vs Linear SVM in R
Step 2: Building a Gaussian Kernel SVM Classifier
Step 3: Visualizing Kernel SVM
Kernel SVM Quiz

Module 20: Naive Bayes 11 lessons

Understanding Bayes' Theorem Intuitively
Understanding the Naive Bayes Algorithm
Bayes' Theorem: Step-by-Step Probability Calculation
Why is Naive Bayes Called 'Naive'?
Step 1: Naive Bayes in Python — Social Network Ads
Step 2: Training and Evaluating a Classifier
Step 3: Analyzing Results — Accuracy and Predictions
Step 1: Getting Started with Naive Bayes in R
Step 2: Troubleshooting Empty Prediction Vectors
Step 3: Visualizing Results — Confusion Matrix and Graphs
Naive Bayes Quiz

Module 21: Decision Tree Classification 7 lessons

How Decision Tree Algorithms Work — with Examples
Step 1: Decision Tree Classification in Python
Step 2: Optimizing Performance in Python
Step 1: Creating a Decision Tree Classifier with rpart (R)
Step 2: Optimizing Prediction Boundaries in R
Step 3: Exploring Splits and Conditions in R
Decision Tree Classification Quiz

Module 22: Random Forest Classification 7 lessons

Understanding Random Forest: Decision Trees & Majority Voting
Step 1: Random Forest Classification in Python
Step 2: Confusion Matrix & Accuracy Metrics
Step 1: From Template to Implementation in R
Step 2: Visualizing Predictions & Results
Step 3: Evaluating Performance & Overfitting
Random Forest Classification Quiz

Module 23: Classification Model Selection in Python 5 lessons

Mastering the Confusion Matrix: True/False Positives & Negatives
Step 1: Choosing the Right Classification Algorithm
Step 2: Streamlined Classification Code in Python
Step 3: Accuracy Metrics in Python
Step 4: The Model Selection Process

Module 24: Evaluating Classification Models Performance 7 lessons

Interpreting Predictions and Errors
The Accuracy Paradox and Better Metrics
Understanding CAP Curves
Mastering CAP Analysis with Accuracy Ratio
Classification — Pros & Cons
Conclusion of Part 3 — Classification
Evaluating Classification Model Performance Quiz

Module 25: Part 4 — Clustering 1 lesson

Welcome to Part 4 — Clustering

Module 26: K-Means Clustering 17 lessons

What is Clustering? Introduction to Unsupervised Learning
K-Means Clustering Tutorial: Visualizing the Algorithm
How to Use the Elbow Method in K-Means
K-Means++ : Solving the Random Initialization Trap
Step 1a: Python K-Means — Identifying Customer Patterns
Step 1b: Data Preparation in Colab/Jupyter
Step 2a: Selecting Relevant Features for Analysis
Step 2b: Optimizing Features for 2D Visualization
Step 3a: Implementing the Elbow Method
Step 3b: WCSS and Elbow Method Implementation
Step 3c: Plotting the Elbow Method Graph
Step 4: Creating a Dependent Variable from Clustering Results
Step 5a: Visualizing K-Means Clusters with a Scatter Plot
Step 5b: Plotting Customer Segments
Step 5c: Insights from K-Means Clustering
Step 1: K-Means in R — Importing & Exploring Data
Step 2: Fitting and Analyzing Mall Data in R

Module 27: Hierarchical Clustering 17 lessons

How to Perform Hierarchical Clustering — Step-by-Step
Dendrograms: Visualizing Cluster Dissimilarity
Dendrogram Analysis and Threshold Setting
Step 1: Getting Started — Data Setup in Python
Step 2a: Building a Dendrogram with SciPy
Step 2b: Dendrogram Basics in Python
Step 2c: Interpreting Dendrograms for Optimal Clusters
Step 3a: Building a Model with Scikit-learn
Step 3b: Comparing 3 vs 5 Clusters
Step 1: R Data Import — Income & Spending Score
Step 2: Building & Interpreting Dendrograms in R
Step 3: Hierarchical Clustering Using hclust in R
Step 4: Visualizing Clustering Results in R
Step 5: Understanding Customer Spending Patterns
Hierarchical Clustering Quiz
Clustering — Pros & Cons
Conclusion of Part 4 — Clustering

Module 28: Part 5 — Association Rule Learning 1 lesson

Welcome to Part 5 — Association Rule Learning

Module 29: Apriori 9 lessons

Apriori Algorithm: Uncovering Hidden Patterns in Data
Step 1: Boost Sales with Python Data Mining
Step 2: Creating a List of Transactions
Step 3: Configuring Apriori — Support, Confidence & Lift
Step 4: Visualizing Apriori Results for Product Deals
Step 1: Creating a Sparse Matrix in R
Step 2: Choosing Minimum Support and Confidence
Step 3: Optimizing Product Placement — Lift & Confidence
Apriori Quiz

Module 30: Eclat 5 lessons

Mastering ECLAT: Support-Based Market Basket Optimization
Adapting Apriori to Eclat for Frequent Itemset Mining
Eclat vs Apriori Simplified
Eclat — Hands-On
Eclat Quiz

Module 31: Part 6 — Reinforcement Learning 1 lesson

Welcome to Part 6 — Reinforcement Learning

Module 32: Upper Confidence Bound (UCB) 14 lessons

The Multi-Armed Bandit: Exploration vs Exploitation
The UCB Algorithm: Solving the Multi-Armed Bandit Problem
Step 1: UCB in Python — Problem Setup
Step 2: Implementing UCB — Data Preparation
Step 3: Setting Up Key Variables
Step 4: Coding the UCB Algorithm Step-by-Step
Step 5: Optimizing Ad Selection in Python
Step 6: Finalizing the UCB Algorithm
Step 7: Visualizing UCB Results with a Histogram
Step 1: Exploring UCB in R
Step 2: Calculating Average Reward & Confidence Interval
Step 3: Optimizing Ad Selection with UCB
Step 4: Analyzing Ad Selection with Histograms
Upper Confidence Bound Quiz

Module 33: Thompson Sampling 10 lessons

Understanding Thompson Sampling: Intuition & Implementation
Deterministic vs Probabilistic: UCB vs Thompson Sampling
Step 1: Python Implementation for Bandit Problems
Step 2: Optimizing Ad Selection
Step 3: Maximizing Random Beta Distributions
Step 4: Beating UCB with Thompson Sampling
Additional Resource for this Section
Step 1: Thompson Sampling vs UCB in R
Step 2: Thompson Sampling Outperforms UCB
Thompson Sampling Quiz

Module 34: Part 7 — Natural Language Processing 24 lessons

Welcome to Part 7 — Natural Language Processing
NLP Basics: Understanding Bag of Words
Deep NLP & Sequence-to-Sequence Models
From If/Else Rules to CNNs: The Evolution of NLP
Implementing Bag of Words — Step-by-Step
Step 1: Getting Started with Sentiment Analysis
Step 2: Importing TSV Data for Sentiment Analysis
Step 3: Text Cleaning — Punctuation & Lowercase
Step 4: Stemming and Stop-Word Removal (Python)
Step 5: Tokenization and Feature Extraction
Step 6: Training & Evaluating a Naive Bayes Classifier
Natural Language Processing in Python — EXTRA
Homework Challenge
Step 1: Text Classification with Bag-of-Words & Random Forest (R)
Step 2: NLP Data Preprocessing in R
Step 3: Initialising a Corpus for Sentiment Analysis
Step 4: Lowercase Transformation in R
Step 5: Removing Numbers with tm_map
Step 6: Removing Punctuation for NLP
Step 7: Removing Stop Words with SnowballC
Step 8: Stemming for Efficient Feature Matrices
Step 9: Removing Extra Spaces
Step 10: Building a Document-Term Matrix
Homework Challenge

Module 35: Part 8 — Deep Learning 3 lessons

Welcome to Part 8 — Deep Learning
Introduction to Deep Learning: History to Modern Applications
Deep Learning Quiz

Module 36: Artificial Neural Networks (ANN) 18 lessons

The Neuron: Neurons, Synapses & Activation Functions
Understanding Activation Functions in Deep Learning
How Do Neural Networks Work? Step-by-Step
How Do Neural Networks Learn?
Gradient Descent vs Brute Force Optimization
Stochastic vs Batch Gradient Descent
Training Neural Networks Step-by-Step
Bank Customer Churn Prediction with TensorFlow
Step 1: ANN in Python — Predicting Customer Churn
Step 2: Preprocessing Data for the Churn Model
Step 3: Designing the ANN — Sequential Model & Dense Layers
Step 4: Compile & Fit for Churn Prediction
Step 5: From Model to Confusion Matrix
Step 1: Preprocessing Data for ANNs in R
Step 2: Installing and Initializing H2O in R
Step 3: H2O Neural Network Layer Config
Step 4: Making Predictions & Evaluating Accuracy with H2O
Deep Learning Additional Content

Module 37: Convolutional Neural Networks (CNN) 18 lessons

Understanding CNN Layers: Convolution, ReLU, Pooling & Flattening
Introduction to CNNs for Computer Vision
Step 1: Convolution — Feature Detection & Feature Maps
Step 1b: Applying ReLU to Convolutional Layers
Step 2: Max Pooling — Spatial Invariance
Step 3: Understanding Flattening
Step 4: Fully Connected Layers in CNNs
How CNNs Process Images
Understanding Softmax and Cross-Entropy in CNNs
Make Sure Your Dataset is Ready
Step 1: Intro to CNNs for Image Classification
Step 2: Keras ImageDataGenerator — Prevent Overfitting
Step 3: TensorFlow CNN — Convolution to Output Layer
Step 4: CNN Training — Epochs, Loss Function & Metrics
Step 5: Making Single Predictions with a CNN
Hands-on CNN Training in Jupyter Notebook
Deep Learning Additional Content #2
CNN Quiz

Module 38: Part 9 — Dimensionality Reduction 1 lesson

Welcome to Part 9 — Dimensionality Reduction

Module 39: Principal Component Analysis (PCA) 7 lessons

PCA Intuition: Reducing Dimensions in Unsupervised Learning
Step 1: PCA in Python — Reducing the Wine Dataset
Step 2: PCA in Action — Predicting Customer Segments
Step 1: Understanding PCA for Feature Extraction (R)
Step 2: Using preProcess in R for PCA
Step 3: Implementing PCA and SVM for Segmentation
PCA Quiz

Module 40: Linear Discriminant Analysis (LDA) 4 lessons

LDA Intuition: Maximizing Class Separation
LDA Step-by-Step Python Implementation
Applying LDA for Feature Extraction
LDA Quiz

Module 41: Kernel PCA 2 lessons

Kernel PCA in Python: Improving Classification Accuracy
Implementing Kernel PCA for Non-Linear Data

Module 42: Part 10 — Model Selection & Boosting 1 lesson

Welcome to Part 10 — Model Selection & Boosting

Module 43: Model Selection 6 lessons

K-Fold Cross-Validation Techniques Explained
Mastering the Bias-Variance Tradeoff
K-Fold Cross-Validation in Python
Optimizing SVM Models with GridSearchCV (Python)
K-Fold Cross-Validation in R
Optimizing SVM Models with Grid Search (R)

Module 44: XGBoost 3 lessons

XGBoost in Python for Cancer Prediction with High Accuracy
Model Selection and Boosting — Additional Content
XGBoost Tutorial: Gradient Boosting for Classification

Module 45: Annex — Logistic Regression 1 lesson

Logistic Regression Intuition

Module 46: Congratulations! 1 lesson

Huge Congrats for Completing the Challenge!

Machine Learning A to Z with AI

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