Udemy Machine Learning AI Python

/41 - Kernel PCA/

002 Implementing Kernel PCA for Non-Linear Data Step-by-Step Guide.mp4

001 Kernel PCA in Python Improving Classification Accuracy with Feature Extraction.mp4

/01 - Welcome to the course! Here we will help you get started in the best conditions/

001 Welcome Challenge!.html

002 Get Excited about ML Predict Car Purchases with Python & Scikit-learn in 5 mins.mp4

003 Get all the Datasets, Codes and Slides here.html

004 How to Use Google Colab & Machine Learning Course Folder.mp4

005 Getting Started with R Programming Install R and RStudio on Windows & Mac.mp4

006 EXTRA Use ChatGPT to Boost your ML Skills.html

/02 - -------------------- Part 1 Data Preprocessing --------------------/

001 Welcome to Part 1 - Data Preprocessing.html

002 Machine Learning Workflow Importing, Modeling, and Evaluating Your ML Model.mp4

003 Data Preprocessing Importance of Training-Test Split in ML Model Evaluation.mp4

004 Feature Scaling in Machine Learning Normalization vs Standardization Explained.mp4

/03 - Data Preprocessing in Python/

001 Step 1 - Data Preprocessing in Python Preparing Your Dataset for ML Models.mp4

002 Step 2 - Data Preprocessing Techniques From Raw Data to ML-Ready Datasets.mp4

003 Machine Learning Toolkit Importing NumPy, Matplotlib, and Pandas Libraries.mp4

004 Step 1 - Machine Learning Basics Importing Datasets Using Pandas read_csv().mp4

005 Step 2 - Using Pandas iloc for Feature Selection in ML Data Preprocessing.mp4

006 Step 3 - Preprocessing Data Building X and Y Vectors for ML Model Training.mp4

007 For Python learners, summary of Object-oriented programming classes & objects.html

008 Coding Exercise 1 Importing and Preprocessing a Dataset for Machine Learning.html

009 Step 1 - Using Scikit-Learn to Replace Missing Values in Machine Learning.mp4

010 Step 2 - Imputing Missing Data in Python SimpleImputer and Numerical Columns.mp4

011 Coding Exercise 2 Handling Missing Data in a Dataset for Machine Learning.html

012 Step 1 - One-Hot Encoding Transforming Categorical Features for ML Algorithms.mp4

013 Step 2 - Handling Categorical Data One-Hot Encoding with ColumnTransformer.mp4

014 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.mp4

015 Coding Exercise 3 Encoding Categorical Data for Machine Learning.html

016 Step 1 - How to Prepare Data for Machine Learning Training vs Test Sets.mp4

017 Step 2 - Preparing Data Creating Training and Test Sets in Python for ML Models.mp4

018 Step 3 - Splitting Data into Training and Test Sets Best Practices in Python.mp4

019 Coding Exercise 4 Dataset Splitting and Feature Scaling.html

020 Step 1 - Feature Scaling in ML Why It's Crucial for Data Preprocessing.mp4

021 Step 2 - How to Scale Numeric Features in Python for ML Preprocessing.mp4

022 Step 3 - Implementing Feature Scaling Fit and Transform Methods Explained.mp4

023 Step 4 - Applying the Same Scaler to Training and Test Sets in Python.mp4

024 Coding exercise 5 Feature scaling for Machine Learning.html

/04 - Data Preprocessing in R/

001 Data Preprocessing for Beginners Preparing Your Dataset for Machine Learning.mp4

002 Data Preprocessing Tutorial Understanding Independent vs Dependent Variables.mp4

003 R Tutorial Importing and Viewing Datasets for Data Preprocessing.mp4

004 How to Handle Missing Values in R Data Preprocessing for Machine Learning.mp4

005 Using R's Factor Function to Handle Categorical Variables in Data Analysis.mp4

006 Step 1 - How to Prepare Data for Machine Learning Training vs Test Sets.mp4

007 Step 2 - Preparing Data Creating Training and Test Sets in R for ML Models.mp4

008 Feature Scaling in ML Step 1 Why It's Crucial for Data Preprocessing.mp4

009 How to Scale Numeric Features in R for Machine Learning Preprocessing - Step 2.mp4

010 Essential Steps in Data Preprocessing Preparing Your Dataset for ML Models.mp4

011 Data Preprocessing Quiz.html

/05 - -------------------- Part 2 Regression --------------------/

001 Welcome to Part 2 - Regression.html

/06 - Simple Linear Regression/

001 Simple Linear Regression Understanding the Equation and Potato Yield Prediction.mp4

002 How to Find the Best Fit Line Understanding Ordinary Least Squares Regression.mp4

003 Step 1a - Mastering Simple Linear Regression Key Concepts and Implementation.mp4

004 Step 1b Data Preprocessing for Linear Regression Import & Split Data in Python.mp4

005 Step 2a - Building a Simple Linear Regression Model with Scikit-learn in Python.mp4

006 Step 2b - Machine Learning Basics Training a Linear Regression Model in Python.mp4

007 Step 3 - Using Scikit-Learn's Predict Method for Linear Regression in Python.mp4

008 Step 4a - Linear Regression Plotting Real vs Predicted Salaries Visualization.mp4

009 Step 4b - Evaluating Linear Regression Model Performance on Test Data.mp4

010 Simple Linear Regression in Python - Additional Lecture.html

011 Step 1 - Data Preprocessing in R Preparing for Linear Regression Modeling.mp4

012 Step 2 - Fitting Simple Linear Regression in R LM Function and Model Summary.mp4

013 Step 3 - How to Use predict() Function in R for Linear Regression Analysis.mp4

014 Step 4a - Plotting Linear Regression Data in R ggplot2 Step-by-Step Guide.mp4

015 Step 4b - Creating a Scatter Plot with Regression Line in R Using ggplot2.mp4

016 Step 4c - Comparing Training vs Test Set Predictions in Linear Regression.mp4

017 Simple Linear Regression Quiz.html

/07 - Multiple Linear Regression/

001 Startup Success Prediction Regression Model for VC Fund Decision-Making.mp4

002 Multiple Linear Regression Independent Variables & Prediction Models.mp4

003 Download-the-PDF.url

003 Understanding Linear Regression Assumptions Linearity, Homoscedasticity & More.mp4

004 How to Handle Categorical Variables in Linear Regression Models.mp4

005 Multicollinearity in Regression Understanding the Dummy Variable Trap.mp4

006 Understanding P-Values and Statistical Significance in Hypothesis Testing.mp4

007 Backward Elimination Building Robust Multiple Linear Regression Models.mp4

008 Step 1a - Hands-On Data Preprocessing for Multiple Linear Regression in Python.mp4

009 Step 1b - Hands-On Guide Implementing Multiple Linear Regression in Python.mp4

010 Step 2a - Hands-on Multiple Linear Regression Preparing Data in Python.mp4

011 Step 2b - Multiple Linear Regression in Python Preparing Your Dataset.mp4

012 Step 3a - Scikit-learn for Multiple Linear Regression Efficient Model Building.mp4

013 Step 3b - Scikit-Learn Building & Training Multiple Linear Regression Models.mp4

014 Step 4a Comparing Real vs Predicted Profits in Linear Regression - Hands-on Gui.mp4

015 Step 4b - ML in Python Evaluating Multiple Linear Regression Accuracy.mp4

016 Multiple Linear Regression in Python - Backward Elimination.html

017 Multiple Linear Regression in Python - EXTRA CONTENT.html

018 Step 1a - Data Preprocessing for MLR Handling Categorical Data.mp4

019 Step 1b - Preparing Datasets for Multiple Linear Regression in R.mp4

020 Step 2a - Multiple Linear Regression in R Building & Interpreting the Regressor.mp4

021 Step 2b Statistical Significance - P-values & Stars in Regression.mp4

022 Step 3 - How to Use predict() Function in R for Multiple Linear Regression.mp4

023 Optimizing Multiple Regression Models Backward Elimination Technique in R.mp4

024 Mastering Feature Selection Backward Elimination in R for Linear Regression.mp4

025 Multiple Linear Regression in R - Automatic Backward Elimination.html

026 Multiple Linear Regression Quiz.html

external-links.txt

/08 - Polynomial Regression/

001 Understanding Polynomial Linear Regression Applications and Examples.mp4

002 Step 1a - Building a Polynomial Regression Model for Salary Prediction in Python.mp4

003 Step 1b - Setting Up Data for Linear vs Polynomial Regression Comparison.mp4

004 Step 2a Linear to Polynomial Regression - Preparing Data for Advanced Models.mp4

005 Step 2b - Transforming Linear to Polynomial Regression A Step-by-Step Guide.mp4

006 Step 3a - Plotting Real vs Predicted Salaries Linear Regression Visualization.mp4

007 Step 3b - Polynomial vs Linear Regression Better Fit with Higher Degrees.mp4

008 Step 4a Predicting Salaries - Linear Regression in Python (Array Input Guide).mp4

009 Step 4b Python Polynomial Regression - Predicting Salaries Accurately.mp4

010 Step 1a - Implementing Polynomial Regression in R HR Salary Analysis Case Study.mp4

011 Step 1b - ML Fundamentals Preparing Data for Polynomial Regression.mp4

012 Step 2a - Building Linear & Polynomial Regression Models in R A Comparison.mp4

013 Step 2b - Building a Polynomial Regression Model Adding Squared & Cubed Terms.mp4

014 Step 3a Visualizing Regression Results - Creating Scatter Plots with ggplot2 in.mp4

015 Step 3b Visualizing Linear Regression - Plotting Predictions vs Observations.mp4

016 Step 3c - Polynomial Regression Curve Fitting for Better Predictions.mp4

017 Step 4a - How to Make Single Predictions Using Polynomial Regression in R.mp4

018 Step 4b - Predicting Salaries with Polynomial Regression A Practical Example.mp4

019 Step 1 - Building a Reusable Framework for Nonlinear Regression Analysis in R.mp4

020 Step 2 - Mastering Regression Model Visualization Increasing Data Resolution.mp4

021 Polynomial Regression Quiz.html

/09 - Support Vector Regression (SVR)/

001 How Does Support Vector Regression (SVR) Differ from Linear Regression.mp4

002 RBF Kernel SVR From Linear to Non-Linear Support Vector Regression.mp4

003 Step 1a - SVR Model Training Feature Scaling and Dataset Preparation in Python.mp4

004 Step 1b - SVR in Python Importing Libraries and Dataset for Machine Learning.mp4

005 Step 2a - Mastering Feature Scaling for Support Vector Regression in Python.mp4

006 Step 2b Reshaping Data for SVR - Preparing Y Vector for Feature Scaling (Python.mp4

007 Step 2c SVR Data Prep - Scaling X & Y Independently with StandardScaler.mp4

008 Step 3 SVM Regression Creating & Training SVR Model with RBF Kernel in Python.mp4

009 Step 4 - SVR Model Prediction Handling Scaled Data and Inverse Transformation.mp4

010 Step 5a - How to Plot Support Vector Regression (SVR) Models Step-by-Step Guide.mp4

011 Step 5b - SVR Scaling & Inverse Transformation in Python.mp4

012 Step 1 - SVR Tutorial Creating a Support Vector Machine Regressor in R.mp4

013 Step 2 - Support Vector Regression Building a Predictive Model in Python.mp4

014 SVR Quiz.html

/10 - Decision Tree Regression/

001 How to Build a Regression Tree Step-by-Step Guide for Machine Learning.mp4

002 Step 1a - Decision Tree Regression Building a Model without Feature Scaling.mp4

003 Step 1b Uploading & Preprocessing Data for Decision Tree Regression in Python.mp4

004 Step 2 - Implementing DecisionTreeRegressor A Step-by-Step Guide in Python.mp4

005 Step 3 - Implementing Decision Tree Regression in Python Making Predictions.mp4

006 Step 4 - Visualizing Decision Tree Regression High-Resolution Results.mp4

007 Step 1 - Creating a Decision Tree Regressor Using rpart Function in R.mp4

008 Step 2 - Decision Tree Regression Fixing Splits with rpart Control Parameter.mp4

009 Step 3 Non-Continuous Regression - Decision Tree Visualization Challenges.mp4

010 Step 4 - Visualizing Decision Tree Understanding Intervals and Predictions.mp4

011 Decision Tree Regression Quiz.html

/11 - Random Forest Regression/

001 Understanding Random Forest Algorithm Intuition and Application in ML.mp4

002 Step 1 - Building a Random Forest Regression Model with Python and Scikit-Learn.mp4

003 Step 2 - Creating a Random Forest Regressor Key Parameters and Model Fitting.mp4

004 Step 1 - Building a Random Forest Model in R Regression Tutorial.mp4

005 Step 2 - Visualizing Random Forest Regression Interpreting Stairs and Splits.mp4

006 Step 3 - Fine-Tuning Random Forest From 10 to 500 Trees for Accurate Prediction.mp4

007 Random Forest Regression Quiz.html

/12 - Evaluating Regression Models Performance/

001 Understanding R-squared Evaluating Goodness of Fit in Regression Models.mp4

002 Understanding Adjusted R-Squared Key Differences from R-Squared Explained.mp4

003 Evaluating Regression Models Performance Quiz.html

/13 - Regression Model Selection in Python/

001 Machine-Learning-A-Z-Model-Selection.zip

001 Make sure you have this Model Selection folder ready.html

002 Step 1 - Mastering Regression Toolkit Comparing Models for Optimal Performance.mp4

003 Step 2 - Creating Generic Code Templates for Various Regression Models in Python.mp4

004 Step 3 Evaluating Regression Models - R-Squared & Performance Metrics Explained.mp4

005 Step 4 - Implementing R-Squared Score in Python with Scikit-Learn's Metrics.mp4

006 Step 1 - Selecting the Best Regression Model R-squared Evaluation in Python.mp4

007 Step 2 - Selecting the Best Regression Model Random Forest vs. SVR Performance.mp4

008 Conclusion of Part 2 - Regression.html

008 Regression-Bonus.zip

/14 - Regression Model Selection in R/

001 Optimizing Regression Models R-Squared vs Adjusted R-Squared Explained.mp4

002 Linear Regression Analysis Interpreting Coefficients for Business Decisions.mp4

003 Conclusion of Part 2 - Regression.html

003 Regression-Bonus.zip

/15 - -------------------- Part 3 Classification --------------------/

001 Welcome to Part 3 - Classification.html

002 What is Classification in Machine Learning Fundamentals and Applications.mp4

/16 - Logistic Regression/

001 Understanding Logistic Regression Predicting Categorical Outcomes.mp4

002 Logistic Regression Finding the Best Fit Curve Using Maximum Likelihood.mp4

003 Step 1a - Building a Logistic Regression Model for Customer Behavior Prediction.mp4

004 Step 1b - Implementing Logistic Regression in Python Data Preprocessing Guide.mp4

005 Step 2a Python Data Preprocessing for Logistic Regression Dataset Prep.mp4

006 Step 2b - Data Preprocessing Feature Scaling Techniques for Logistic Regression.mp4

007 Step 3a - How to Import and Use LogisticRegression Class from Scikit-learn.mp4

008 Step 3b - Training Logistic Regression Model Fit Method for Classification.mp4

009 Step 4a - Formatting Single Observation Input for Logistic Regression Predict.mp4

010 Step 4b Predicted vs. Real Purchase Decisions in Logistic Regression.mp4

011 Step 5 - Comparing Predicted vs Real Results Python Logistic Regression Guide.mp4

012 Step 6a - Implementing Confusion Matrix and Accuracy Score in Scikit-Learn.mp4

013 Step 6b Evaluating Classification Models - Confusion Matrix & Accuracy Metrics.mp4

014 Step 7a - Visualizing Logistic Regression Decision Boundaries in Python 2D Plot.mp4

015 Step 7b - Interpreting Logistic Regression Results Prediction Regions Explained.mp4

016 Step 7c - Visualizing Logistic Regression Performance on New Data in Python.mp4

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

018 Step 1 - Data Preprocessing for Logistic Regression in R Preparing Your Dataset.mp4

019 Step 2 - How to Create a Logistic Regression Classifier Using R's GLM Function.mp4

020 Step 3 - How to Use R for Logistic Regression Prediction Step-by-Step Guide.mp4

021 Step 4 - How to Assess Model Accuracy Using a Confusion Matrix in R.mp4

022 Warning - Update.html

023 Step 5a - Interpreting Logistic Regression Plots Prediction Regions Explained.mp4

024 Step 5b Logistic Regression - Linear Classifiers & Prediction Boundaries.mp4

025 Step 5c - Data Viz in R Colorizing Pixels for Logistic Regression.mp4

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

027 Optimizing R Scripts for Machine Learning Building a Classification Template.mp4

028 Machine Learning Regression and Classification EXTRA.html

029 Logistic Regression Quiz.html

030 EXTRA CONTENT Logistic Regression Practical Case Study.html

/17 - K-Nearest Neighbors (K-NN)/

001 K-Nearest Neighbors (KNN) Explained A Beginner's Guide to Classification.mp4

002 Step 1 - Python KNN Tutorial Classifying Customer Data for Targeted Marketing.mp4

003 Step 2 - Building a K-Nearest Neighbors Model Scikit-Learn KNeighborsClassifier.mp4

004 Step 3 - Visualizing KNN Decision Boundaries Python Tutorial for Beginners.mp4

005 Step 1 - Implementing KNN Classification in R Setup & Data Preparation.mp4

006 Step 2 - Building a KNN Classifier Preparing Training and Test Sets in R.mp4

007 Step 3 - Implementing KNN Classification in R Adapting the Classifier Template.mp4

008 K-Nearest Neighbor Quiz.html

/18 - Support Vector Machine (SVM)/

001 Support Vector Machines Explained Hyperplanes and Support Vectors in ML.mp4

002 Step 1 - Building a Support Vector Machine Model with Scikit-learn in Python.mp4

003 Step 2 - Building a Support Vector Machine Model with Sklearn's SVC in Python.mp4

004 Step 3 - Understanding Linear SVM Limitations Why It Didn't Beat kNN Classifier.mp4

005 Step 1 - Building a Linear SVM Classifier in R Data Import and Initial Setup.mp4

005 SVM.zip

006 Step 2 Creating & Evaluating Linear SVM Classifier in R - Predictions & Results.mp4

007 SVM Quiz.html

/19 - Kernel SVM/

001 From Linear to Non-Linear SVM Exploring Higher Dimensional Spaces.mp4

002 Support Vector Machines Transforming Non-Linear Data for Linear Separation.mp4

003 Kernel Trick SVM Machine Learning for Non-Linear Classification.mp4

004 Understanding Different Types of Kernel Functions for Machine Learning.mp4

005 Mastering Support Vector Regression Non-Linear SVR with RBF Kernel Explained.mp4

006 Step 1 - Python Kernel SVM Applying RBF to Solve Non-Linear Classification.mp4

007 Step 2 - Mastering Kernel SVM Improving Accuracy with Non-Linear Classifiers.mp4

008 Step 1 - Kernel SVM vs Linear SVM Overcoming Non-Linear Separability in R.mp4

009 Step 2 - Building a Gaussian Kernel SVM Classifier for Advanced Machine Learning.mp4

010 Step 3 Visualizing Kernel SVM - Non-Linear Classification in Machine Learning.mp4

011 Kernel SVM Quiz.html

/20 - Naive Bayes/

001 Understanding Bayes' Theorem Intuitively From Probability to Machine Learning.mp4

002 Understanding Naive Bayes Algorithm Probabilistic Classification Explained.mp4

003 Bayes Theorem in Machine Learning Step-by-Step Probability Calculation.mp4

004 Why is Naive Bayes Called Naive Understanding the Algorithm's Assumptions.mp4

005 Step 1 - Naive Bayes in Python Applying ML to Social Network Ads Optimisation.mp4

006 Step 2 - Python Naive Bayes Training and Evaluating a Classifier on Real Data.mp4

007 Step 3 - Analyzing Naive Bayes Algorithm Results Accuracy and Predictions.mp4

008 Step 1 - Getting Started with Naive Bayes Algorithm in R for Classification.mp4

009 Step 2 - Troubleshooting Naive Bayes Classification Empty Prediction Vectors.mp4

010 Step 3 - Visualizing Naive Bayes Results Creating Confusion Matrix and Graphs.mp4

011 Naive Bayes Quiz.html

/21 - Decision Tree Classification/

001 How Decision Tree Algorithms Work Step-by-Step Guide with Examples.mp4

002 Step 1 - Implementing Decision Tree Classification in Python with Scikit-learn.mp4

003 Step 2 - Training a Decision Tree Classifier Optimizing Performance in Python.mp4

004 Step 1 - R Tutorial Creating a Decision Tree Classifier with rpart Library.mp4

005 Step 2 - Decision Tree Classifier Optimizing Prediction Boundaries in R.mp4

006 Step 3 - Decision Tree Visualization Exploring Splits and Conditions in R.mp4

007 Decision Tree Classification Quiz.html

/22 - Random Forest Classification/

001 Understanding Random Forest Decision Trees and Majority Voting Explained.mp4

002 Step 1 - Implementing Random Forest Classification in Python with Scikit-Learn.mp4

003 Step 2 Random Forest Evaluation - Confusion Matrix & Accuracy Metrics.mp4

004 Step 1 Random Forest Classifier - From Template to Implementation in R.mp4

005 Step 2 Random Forest Classification - Visualizing Predictions & Results.mp4

006 Step 3 - Evaluating Random Forest Performance Test Set Results & Overfitting.mp4

007 Random Forest Classification Quiz.html

/23 - Classification Model Selection in Python/

001 Machine-Learning-A-Z-Model-Selection.zip

001 Make sure you have this Model Selection folder ready.html

002 Mastering the Confusion Matrix True Positives, Negatives, and Errors.mp4

003 Step 1 - How to Choose the Right Classification Algorithm for Your Dataset.mp4

004 Step 2 - Optimizing Model Selection Streamlined Classification Code in Python.mp4

005 Step 3 - Evaluating Classification Algorithms Accuracy Metrics in Python.mp4

006 Step 4 - Model Selection Process Evaluating Classification Algorithms.mp4

/24 - Evaluating Classification Models Performance/

001 Logistic Regression Interpreting Predictions and Errors in Data Science.mp4

002 Machine Learning Model Evaluation Accuracy Paradox and Better Metrics.mp4

003 Understanding CAP Curves Assessing Model Performance in Data Science 2024.mp4

004 Mastering CAP Analysis Assessing Classification Models with Accuracy Ratio.mp4

005 Classification-Pros-Cons.pdf

005 Conclusion of Part 3 - Classification.html

006 Evaluating Classiification Model Performance Quiz.html

/25 - -------------------- Part 4 Clustering --------------------/

001 Welcome to Part 4 - Clustering.html

/26 - K-Means Clustering/

001 What is Clustering in Machine Learning Introduction to Unsupervised Learning.mp4

002 K-Means Clustering Tutorial Visualizing the Machine Learning Algorithm.mp4

003 How to Use the Elbow Method in K-Means Clustering A Step-by-Step Guide.mp4

004 K-Means++ Algorithm Solving the Random Initialization Trap in Clustering.mp4

005 Step 1a - Python K-Means Tutorial Identifying Customer Patterns in Mall Data.mp4

006 Step 1b K-Means Clustering - Data Preparation in Google ColabJupyter.mp4

007 Step 2a - K-Means Clustering in Python Selecting Relevant Features for Analysis.mp4

008 Step 2b K-Means Clustering - Optimizing Features for 2D Visualization.mp4

009 Step 3a - Implementing the Elbow Method for K-Means Clustering in Python.mp4

010 Step 3b - Optimizing K-means Clustering WCSS and Elbow Method Implementation.mp4

011 Step 3c - Plotting the Elbow Method Graph for K-Means Clustering in Python.mp4

012 Step 4 - Creating a Dependent Variable from K-Means Clustering Results in Python.mp4

013 Step 5a Visualizing K-Means Clusters of Customer Data with Python Scatter.mp4

014 Step 5b - Visualizing K-Means Clusters Plotting Customer Segments in Python.mp4

015 Step 5c - Analyzing Customer Segments Insights from K-means Clustering.mp4

016 Step 1 - K-Means Clustering in R Importing & Exploring Segmentation Data.mp4

017 Step 2 - K-Means Algorithm Implementation in R Fitting and Analyzing Mall Data.mp4

018 K-Means Clustering Quiz.html

/27 - Hierarchical Clustering/

001 How to Perform Hierarchical Clustering Step-by-Step Guide for Machine Learning.mp4

002 Visualizing Cluster Dissimilarity Dendrograms in Hierarchical Clustering.mp4

003 Mastering Hierarchical Clustering Dendrogram Analysis and Threshold Setting.mp4

004 Step 1 - Getting Started with Hierarchical Clustering Data Setup in Python.mp4

005 Step 2a - Implementing Hierarchical Clustering Building a Dendrogram with SciPy.mp4

006 Step 2b - Visualizing Hierarchical Clustering Dendrogram Basics in Python.mp4

007 Step 2c - Interpreting Dendrograms Optimal Clusters in Hierarchical Clustering.mp4

008 Step 3a - Building a Hierarchical Clustering Model with Scikit-learn in Python.mp4

009 Step 3b - Comparing 3 vs 5 Clusters in Hierarchical Clustering Python Example.mp4

010 Step 1 - R Data Import for Clustering Annual Income & Spending Score Analysis.mp4

011 Step 2 Using H.clust in R - Building & Interpreting Dendrograms for Clustering.mp4

012 Step 3 - Implementing Hierarchical Clustering Using Cat Tree Method in R.mp4

013 Step 4 - Cluster Plot Method Visualizing Hierarchical Clustering Results in R.mp4

014 Step 5 - Hierarchical Clustering in R Understanding Customer Spending Patterns.mp4

015 Hierarchical Clustering Quiz.html

016 Clustering-Pros-Cons.pdf

016 Conclusion of Part 4 - Clustering.html

/28 - -------------------- Part 5 Association Rule Learning --------------------/

001 Welcome to Part 5 - Association Rule Learning.html

/29 - Apriori/

001 Apriori Algorithm Uncovering Hidden Patterns in Data Mining Association Rules.mp4

002 Step 1 - Association Rule Learning Boost Sales with Python Data Mining.mp4

003 Step 2 - Creating a List of Transactions for Market Basket Analysis in Python.mp4

004 Step 3 - Configuring Apriori Function Support, Confidence, and Lift in Python.mp4

005 Step 4 Visualizing Apriori Algorithm Results for Product Deals in Python.mp4

006 Step 1 - Creating a Sparse Matrix for Association Rule Mining in R.mp4

007 Step 2 - Optimizing Apriori Model Choosing Minimum Support and Confidence.mp4

008 Step 3 Optimizing Product Placement - Apriori Algorithm, Lift & Confidence.mp4

009 Apriori Quiz.html

/30 - Eclat/

001 Mastering ECLAT Support-Based Approach to Market Basket Optimization.mp4

002 Python Tutorial Adapting Apriori to Eclat for Efficient Frequent Itemset Mining.mp4

003 Eclat vs Apriori Simplified Association Rule Learning in Data Mining.mp4

003 Eclat.zip

004 Eclat Quiz.html

/31 - -------------------- Part 6 Reinforcement Learning --------------------/

001 Welcome to Part 6 - Reinforcement Learning.html

/32 - Upper Confidence Bound (UCB)/

001 Multi-Armed Bandit Exploration vs Exploitation in Reinforcement Learning.mp4

002 Upper Confidence Bound Algorithm Solving Multi-Armed Bandit Problems in ML.mp4

003 Step 1 - Upper Confidence Bound Solving Multi-Armed Bandit Problem in Python.mp4

004 Step 2 Implementing UCB Algorithm in Python - Data Preparation.mp4

005 Step 3 - Python Code for Upper Confidence Bound Setting Up Key Variables.mp4

006 Step 4 - Python for RL Coding the UCB Algorithm Step-by-Step.mp4

007 Step 5 - Coding Upper Confidence Bound Optimizing Ad Selection in Python.mp4

008 Step 6 - Reinforcement Learning Finalizing UCB Algorithm in Python.mp4

009 Step 7 - Visualizing UCB Algorithm Results Histogram Analysis in Python.mp4

010 Step 1 - Exploring Upper Confidence Bound in R Multi-Armed Bandit Problems.mp4

011 Step 2 - UCB Algorithm in R Calculating Average Reward & Confidence Interval.mp4

012 Step 3 Optimizing Ad Selection - UCB & Multi-Armed Bandit Algorithm Explained.mp4

013 Step 4 - UCB Algorithm Performance Analyzing Ad Selection with Histograms.mp4

014 Upper Confidence Bound Quiz.html

/33 - Thompson Sampling/

001 Understanding Thompson Sampling Algorithm Intuition and Implementation.mp4

002 Deterministic vs Probabilistic UCB and Thompson Sampling in Machine Learning.mp4

003 Step 1 - Python Implementation of Thompson Sampling for Bandit Problems.mp4

004 Step 2 - Optimizing Ad Selection with Thompson Sampling Algorithm in Python.mp4

005 Step 3 - Python Code for Thompson Sampling Maximizing Random Beta Distributions.mp4

006 Step 4 - Beating UCB with Thompson Sampling Python Multi-Armed Bandit Tutorial.mp4

007 Additional Resource for this Section.html

008 Step 1 - Thompson Sampling vs UCB Optimizing Ad Click-Through Rates in R.mp4

009 Step 2 - Reinforcement Learning Thompson Sampling Outperforms UCB Algorithm.mp4

010 Thompson Sampling Quiz.html

/34 - -------------------- Part 7 Natural Language Processing --------------------/

001 Welcome to Part 7 - Natural Language Processing.html

002 NLP Basics Understanding Bag of Words and Its Applications in Machine Learning.mp4

003 Deep NLP & Sequence-to-Sequence Models Exploring Natural Language Processing.mp4

004 From IfElse Rules to CNNs Evolution of Natural Language Processing.mp4

005 Implementing Bag of Words in NLP A Step-by-Step Tutorial.mp4

006 Step 1 - Getting Started with Natural Language Processing Sentiment Analysis.mp4

007 Step 2 - Importing TSV Data for Sentiment Analysis Python NLP Data Processing.mp4

008 Step 3 - Text Cleaning for NLP Remove Punctuation and Convert to Lowercase.mp4

009 Step 4 - Text Preprocessing Stemming and Stop Word Removal for NLP in Python.mp4

010 Step 5 - Tokenization and Feature Extraction for Naive Bayes Sentiment Analysis.mp4

011 Step 6 - Training and Evaluating a Naive Bayes Classifier for Sentiment Analysis.mp4

012 Natural Language Processing in Python - EXTRA.html

013 Homework Challenge.html

014 Step 1 - Text Classification Using Bag-of-Words and Random Forest in R.mp4

015 Warning - Update.html

016 Step 2 - NLP Data Preprocessing in R Importing TSV Files for Sentiment Analysis.mp4

017 Step 3 - NLP in R Initialising a Corpus for Sentiment Analysis.mp4

018 Step 4 - NLP Data Cleaning Lowercase Transformation in R for Text Analysis.mp4

019 Step 5 - Sentiment Analysis Data Cleaning Removing Numbers with TM Map.mp4

020 Step 6 - Cleaning Text Data Removing Punctuation for NLP and Classification.mp4

021 Step 7 - Simplifying Corpus Using SnowballC Package to Remove Stop Words in R.mp4

022 Step 8 - Enhancing Text Classification Stemming for Efficient Feature Matrices.mp4

023 Step 9 Removing Extra Spaces for NLP Sentiment Analysis Text Cleaning.mp4

024 Step 10 - Building a Document-Term Matrix for NLP Text Classification.mp4

025 Homework Challenge.html

026 Natural Language Processing Quiz.html

/35 - -------------------- Part 8 Deep Learning --------------------/

001 Welcome to Part 8 - Deep Learning.html

002 Introduction to Deep Learning From Historical Context to Modern Applications.mp4

003 Deep Learning Quiz.html

/36 - Artificial Neural Networks/

001 Understanding CNN Layers Convolution, ReLU, Pooling, and Flattening Explained.mp4

002 Deep Learning Basics Exploring Neurons, Synapses, and Activation Functions.mp4

003 Neural Network Basics Understanding Activation Functions in Deep Learning.mp4

004 How Do Neural Networks Work Step-by-Step Guide to Deep Learning Algorithms.mp4

005 How Do Neural Networks Learn Deep Learning Fundamentals Explained.mp4

006 Deep Learning Fundamentals Gradient Descent vs Brute Force Optimization.mp4

007 Stochastic vs Batch Gradient Descent Deep Learning Fundamentals.mp4

008 Deep Learning Fundamentals Training Neural Networks Step-by-Step.mp4

009 Bank Customer Churn Prediction Machine Learning Model with TensorFlow.mp4

010 Step 1 ANN in Python Predicting Customer Churn with TensorFlow.mp4

011 Step 2 - TensorFlow 2.0 Tutorial Preprocessing Data for Customer Churn Model.mp4

012 Step 3 - Designing ANN Sequential Model & Dense Layers for Deep Learning.mp4

013 Step 4 - Train Neural Network Compile & Fit for Customer Churn Prediction.mp4

014 Step 5 - Implementing ANN for Churn Prediction From Model to Confusion Matrix.mp4

015 Step 1 - How to Preprocess Data for Artificial Neural Networks in R.mp4

016 Step 2 - How to Install and Initialize H2O for Efficient Deep Learning in R.mp4

017 Step 3 Building Deep Learning Model - H2O Neural Network Layer Config.mp4

018 Step 4 - H2O Deep Learning Making Predictions and Evaluating Model Accuracy.mp4

019 Deep Learning Additional Content.html

020 EXTRA CONTENT ANN Case Study.html

021 ANN QUIZ.html

/37 - Convolutional Neural Networks/

001 dataset.zip

001 Understanding CNN Layers Convolution, ReLU, Pooling, and Flattening Explained.mp4

002 Introduction to CNNs Understanding Deep Learning for Computer Vision.mp4

003 Step 1 - Understanding Convolution in CNNs Feature Detection and Feature Maps.mp4

004 Step 1b - Applying ReLU to Convolutional Layers Breaking Up Image Linearity.mp4

005 Step 2 - Max Pooling in CNNs Enhancing Spatial Invariance for Image Recognition.mp4

006 Step 3 - Understanding Flattening in Convolutional Neural Network Architecture.mp4

007 Step 4 - Fully Connected Layers in CNNs Optimizing Feature Combination.mp4

008 Deep Learning Basics How Convolutional Neural Networks (CNNs) Process Images.mp4

009 Deep Learning Essentials Understanding Softmax and Cross-Entropy in CNNs.mp4

010 dataset.zip

010 Make sure you have your dataset ready.html

011 Step 1 Intro to CNNs for Image Classification.mp4

012 Step 2 - Keras ImageDataGenerator Prevent Overfitting in CNN Models.mp4

013 Step 3 - TensorFlow CNN Convolution to Output Layer for Vision Tasks.mp4

014 Step 4 CNN Training - Epochs, Loss Function & Metrics in TensorFlow.mp4

015 Step 5 - Making Single Predictions with Convolutional Neural Networks in Python.mp4

016 Hands-on CNN Training Using Jupyter Notebook for Image Classification.mp4

017 Deep Learning Additional Content #2.html

018 CNN Quiz.html

/38 - -------------------- Part 9 Dimensionality Reduction --------------------/

001 Welcome to Part 9 - Dimensionality Reduction.html

/39 - Principal Component Analysis (PCA)/

001 PCA Algorithm Intuition Reducing Dimensions in Unsupervised Learning.mp4

002 Step 1 PCA in Python Reducing Wine Dataset Features with Scikit-learn.mp4

003 Step 2 - PCA in Action Reducing Dimensions and Predicting Customer Segments.mp4

004 Step 1 in R - Understanding Principal Component Analysis for Feature Extraction.mp4

005 Step 2 - Using preProcess Function in R for PCA Extracting Principal Components.mp4

006 Step 3 - Implementing PCA and SVM for Customer Segmentation Practical Guide.mp4

007 PCA Quiz.html

/40 - Linear Discriminant Analysis (LDA)/

001 LDA Intuition Maximizing Class Separation in Machine Learning Algorithms.mp4

002 Mastering Linear Discriminant Analysis Step-by-Step Python Implementation.mp4

003 Step-by-Step Guide Applying LDA for Feature Extraction in Machine Learning.mp4

004 LDA Quiz.html

/

XiPCuLT.nfo

/42 - -------------------- Part 10 Model Selection & Boosting --------------------/

001 Welcome to Part 10 - Model Selection & Boosting.html

/43 - Model Selection/

001 Mastering Model Evaluation K-Fold Cross-Validation Techniques Explained.mp4

002 How to Master the Bias-Variance Tradeoff in Machine Learning Models.mp4

003 K-Fold Cross-Validation in Python Improve Machine Learning Model Performance.mp4

004 Optimizing SVM Models with GridSearchCV A Step-by-Step Python Tutorial.mp4

005 Evaluating ML Model Accuracy K-Fold Cross-Validation Implementation in R.mp4

006 Optimizing SVM Models with Grid Search A Step-by-Step R Tutorial.mp4

/44 - XGBoost/

001 How to Use XGBoost in Python for Cancer Prediction with High Accuracy.mp4

002 Model Selection and Boosting Additional Content.html

003 XGBoost Tutorial Implementing Gradient Boosting for Classification Problems.mp4

/45 - Annex Logistic Regression (Long Explanation)/

001 Logistic Regression Intuition.mp4

/46 - Congratulations!! Don't forget your Prize )/

001 Huge Congrats for completing the challenge!.html

002 Bonus How To UNLOCK Top Salaries (Live Training).html

Total files 433