• Get Excited about ML: Predict Car Purchases with Python & Scikit-learn in 5 mins
• Get all the Datasets, Codes and Slides here
• Recommended Workshops before we dive in!
• How to Use Google Colab & Machine Learning Course Folder
• Prizes $$ for Learning

• 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

• 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
• 1 question
• 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
• 1 question
• 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
• 1 question
• Step 1 – How to Prepare Data for Machine Learning: Training vs Test Sets
• Step 2 – Preparing Data: Creating Training and Test Sets in Python for ML Models
• Step 3 – Splitting Data into Training and Test Sets: Best Practices in Python
• Coding Exercise 4: Dataset Splitting and Feature Scaling
• 1 question
• Step 1 – Feature Scaling in ML: Why It’s Crucial for Data Preprocessing
• Step 2 – How to Scale Numeric Features in Python for ML 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

• Getting Started with R Programming: Install R and RStudio on Windows & Mac
• 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 Step 1: Why It’s Crucial for Data Preprocessing
• How to Scale Numeric Features in R for Machine Learning Preprocessing – Step 2
• Essential Steps in Data Preprocessing: Preparing Your Dataset for ML Models
• Data Preprocessing Quiz

• Welcome to Part 2 – Regression

• Simple Linear Regression: Understanding the Equation and Potato Yield Prediction
• How to Find the Best Fit Line: Understanding Ordinary Least Squares Regression
• Step 1a – Mastering Simple Linear Regression: Key Concepts and Implementation
• Step 1b: Data Preprocessing for Linear Regression: Import & Split Data in Python
• Step 2a – 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
• Step 4a – 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

• Startup Success Prediction: Regression Model for VC Fund Decision-Making
• Multiple Linear Regression: Independent Variables & Prediction Models
• Understanding Linear Regression Assumptions: Linearity, Homoscedasticity & More
• How to Handle Categorical Variables in Linear Regression Models
• Multicollinearity in Regression: Understanding the Dummy Variable Trap
• Understanding P-Values and Statistical Significance in Hypothesis Testing
• 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
• Step 1a – Data Preprocessing for MLR: Handling Categorical Data
• Step 1b – Preparing Datasets for Multiple Linear Regression in R
• 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

• 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

• 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
• Step 3: 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
• Step 2 – Support Vector Regression: Building a Predictive Model in Python
• SVR Quiz

• 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
• Step 4 – Visualizing Decision Tree Regression: High-Resolution Results
• Step 1 – Creating a Decision Tree Regressor: Using rpart Function in R
• Step 2 – Decision Tree Regression: Fixing Splits with rpart Control Parameter
• Step 3: Non-Continuous Regression – Decision Tree Visualization Challenges
• Step 4 – Visualizing Decision Tree: Understanding Intervals and Predictions
• Decision Tree Regression Quiz

• 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
• Step 3 – Fine-Tuning Random Forest: From 10 to 500 Trees for Accurate Prediction
• Random Forest Regression Quiz

• 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

• 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

• Optimizing Regression Models: R-Squared vs Adjusted R-Squared Explained
• Linear Regression Analysis: Interpreting Coefficients for Business Decisions
• Conclusion of Part 2 – Regression

• 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
• Step 3b – Training Logistic Regression Model: Fit Method for Classification
• 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
• 5 questions
• EXTRA CONTENT: Logistic Regression Practical Case Study

• 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
• Step 1 – 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 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

• 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

• 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
• Step 1 – Naive Bayes in Python: Applying ML to Social Network Ads Optimisation
• 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

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 R

Decision Tree Classification Quiz

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

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

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

Welcome to Part 4 – Clustering

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

Step 1 – K-Means Clustering in R: Importing & Exploring Segmentation Data

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

K-Means Clustering Quiz

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

5 questions

Conclusion of Part 4 – Clustering

Welcome to Part 5 – Association Rule Learning

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

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

Welcome to Part 6 – Reinforcement Learning

Multi-Armed Bandit: Exploration vs Exploitation in Reinforcement Learning
Upper Confidence Bound Algorithm: Solving Multi-Armed Bandit Problems in ML
Step 1 – Upper Confidence Bound: Solving Multi-Armed Bandit Problem in Python
Step 2: Implementing UCB Algorithm in Python – Data Preparation
Step 3 – Python Code for Upper Confidence Bound: Setting Up Key Variables
Step 4 – Python for RL: Coding the UCB Algorithm Step-by-Step
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 in R: Multi-Armed Bandit Problems
Step 2 – UCB Algorithm in R: Calculating Average Reward & Confidence Interval
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

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

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 1 – Text Classification Using Bag-of-Words and Random Forest in R
Warning – Update
Step 2 – NLP Data Preprocessing in R: Importing TSV Files for Sentiment Analysis
Step 3 – NLP in R: Initialising a Corpus for Sentiment Analysis
Step 4 – NLP Data Cleaning: Lowercase Transformation in R for Text Analysis
Step 5 – Sentiment Analysis Data Cleaning: Removing Numbers with TM Map
Step 6 – Cleaning Text Data: Removing Punctuation for NLP and Classification
Step 7 – Simplifying Corpus: Using SnowballC Package to Remove Stop Words in R
Step 8 – Enhancing Text Classification: Stemming for Efficient Feature Matrices
Step 9: Removing Extra Spaces for NLP Sentiment Analysis Text Cleaning
Step 10 – Building a Document-Term Matrix for NLP Text Classification
Homework Challenge
Natural Language Processing Quiz

Welcome to Part 8 – Deep Learning
Introduction to Deep Learning: From Historical Context to Modern Applications
Deep Learning Quiz

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
Step 1 ANN in Python: Predicting Customer Churn with TensorFlow
Step 2 – TensorFlow 2.0 Tutorial: Preprocessing Data for Customer Churn Model
Step 3 – Designing ANN: Sequential Model & Dense Layers for Deep Learning
Step 4 – 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

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 1b – 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
Step 3 – TensorFlow CNN: Convolution to Output Layer for Vision Tasks
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

Welcome to Part 9 – Dimensionality Reduction

PCA Algorithm Intuition: Reducing Dimensions in Unsupervised Learning
Step 1 PCA in Python : Reducing Wine Dataset Features with Scikit-learn
Step 2 – PCA in Action: Reducing Dimensions and Predicting Customer Segments
Step 1 in R – 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

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 in Python: Improving Classification Accuracy with Feature Extraction
Implementing Kernel PCA for Non-Linear Data: Step-by-Step Guide

Welcome to Part 10 – Model Selection & Boosting

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

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

Logistic Regression Intuition

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