Machine Learning A-Z AI,

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1 Get Excited about ML Predict Car Purchases with Python Scikit learn in 5 mins
3 How to Use Google Colab Machine Learning Course Folder

2 Machine Learning Workflow Importing Modeling and Evaluating Your ML Model
3 Data Preprocessing Importance of Training Test Split in ML Model Evaluation
4 Feature Scaling in Machine Learning Normalization vs Standardization Explained

1 Step 1 Data Preprocessing in Python Preparing Your Dataset for ML Models
2 Step 2 Data Preprocessing Techniques From Raw Data to ML Ready Datasets
3 Machine Learning Toolkit Importing NumPy Matplotlib and Pandas Libraries
4 Step 1 Machine Learning Basics Importing Datasets Using Pandas read_csv
5 Step 2 Using Pandas iloc for Feature Selection in ML Data Preprocessing
6 Step 3 Preprocessing Data Building X and Y Vectors for ML Model Training
9 Step 1 Using Scikit Learn to Replace Missing Values in Machine Learning
10 Step 2 Imputing Missing Data in Python SimpleImputer and Numerical Columns
12 Step 1 One Hot Encoding Transforming Categorical Features for ML Algorithms
13 Step 2 Handling Categorical Data One Hot Encoding with ColumnTransformer
14 Step 3 Preprocessing Categorical Data One Hot and Label Encoding Techniques
16 Step 1 How to Prepare Data for Machine Learning Training vs Test Sets
17 Step 2 Preparing Data Creating Training and Test Sets in Python for ML Models
18 Step 3 Splitting Data into Training and Test Sets Best Practices in Python
20 Step 1 Feature Scaling in ML Why Its Crucial for Data Preprocessing
21 Step 2 How to Scale Numeric Features in Python for ML Preprocessing
22 Step 3 Implementing Feature Scaling Fit and Transform Methods Explained
23 Step 4 Applying the Same Scaler to Training and Test Sets in Python

1 Getting Started with R Programming Install R and RStudio on Windows Mac
2 Data Preprocessing for Beginners Preparing Your Dataset for Machine Learning
3 Data Preprocessing Tutorial Understanding Independent vs Dependent Variables
4 R Tutorial Importing and Viewing Datasets for Data Preprocessing
5 How to Handle Missing Values in R Data Preprocessing for Machine Learning
6 Using Rs Factor Function to Handle Categorical Variables in Data Analysis
7 Step 1 How to Prepare Data for Machine Learning Training vs Test Sets
8 Step 2 Preparing Data Creating Training and Test Sets in R for ML Models
9 Feature Scaling in ML Step 1 Why Its Crucial for Data Preprocessing
10 How to Scale Numeric Features in R for Machine Learning Preprocessing Step 2
11 Essential Steps in Data Preprocessing Preparing Your Dataset for ML Models

1 Simple Linear Regression Understanding the Equation and Potato Yield Prediction
2 How to Find the Best Fit Line Understanding Ordinary Least Squares Regression
3 Step 1a Mastering Simple Linear Regression Key Concepts and Implementation
4 Step 1b Data Preprocessing for Linear Regression Import Split Data in Python
5 Step 2a Building a Simple Linear Regression Model with Scikit learn in Python
6 Step 2b Machine Learning Basics Training a Linear Regression Model in Python
7 Step 3 Using Scikit Learns Predict Method for Linear Regression in Python
8 Step 4a Linear Regression Plotting Real vs Predicted Salaries Visualization
9 Step 4b Evaluating Linear Regression Model Performance on Test Data
11 Step 1 Data Preprocessing in R Preparing for Linear Regression Modeling
12 Step 2 Fitting Simple Linear Regression in R LM Function and Model Summary
13 Step 3 How to Use predict Function in R for Linear Regression Analysis
14 Step 4a Plotting Linear Regression Data in R ggplot2 Step by Step Guide
15 Step 4b Creating a Scatter Plot with Regression Line in R Using ggplot2
16 Step 4c Comparing Training vs Test Set Predictions in Linear Regression

1 Startup Success Prediction Regression Model for VC Fund Decision Making
2 Multiple Linear Regression Independent Variables Prediction Models
3 Understanding Linear Regression Assumptions Linearity Homoscedasticity More
4 How to Handle Categorical Variables in Linear Regression Models
5 Multicollinearity in Regression Understanding the Dummy Variable Trap
6 Understanding P Values and Statistical Significance in Hypothesis Testing
7 Backward Elimination Building Robust Multiple Linear Regression Models
8 Step 1a Hands On Data Preprocessing for Multiple Linear Regression in Python
9 Step 1b Hands On Guide Implementing Multiple Linear Regression in Python
10 Step 2a Hands on Multiple Linear Regression Preparing Data in Python
11 Step 2b Multiple Linear Regression in Python Preparing Your Dataset
12 Step 3a Scikit learn for Multiple Linear Regression Efficient Model Building
13 Step 3b Scikit Learn Building Training Multiple Linear Regression Models
14 Step 4a Comparing Real vs Predicted Profits in Linear Regression Hands on Gui
15 Step 4b ML in Python Evaluating Multiple Linear Regression Accuracy
18 Step 1a Data Preprocessing for MLR Handling Categorical Data
19 Step 1b Preparing Datasets for Multiple Linear Regression in R
20 Step 2a Multiple Linear Regression in R Building Interpreting the Regressor
21 Step 2b Statistical Significance P values Stars in Regression
22 Step 3 How to Use predict Function in R for Multiple Linear Regression
23 Optimizing Multiple Regression Models Backward Elimination Technique in R
24 Mastering Feature Selection Backward Elimination in R for Linear Regression

1 Understanding Polynomial Linear Regression Applications and Examples
2 Step 1a Building a Polynomial Regression Model for Salary Prediction in Python
3 Step 1b Setting Up Data for Linear vs Polynomial Regression Comparison
4 Step 2a Linear to Polynomial Regression Preparing Data for Advanced Models
5 Step 2b Transforming Linear to Polynomial Regression A Step by Step Guide
6 Step 3a Plotting Real vs Predicted Salaries Linear Regression Visualization
7 Step 3b Polynomial vs Linear Regression Better Fit with Higher Degrees
8 Step 4a Predicting Salaries Linear Regression in Python Array Input Guide
9 Step 4b Python Polynomial Regression Predicting Salaries Accurately
10 Step 1a Implementing Polynomial Regression in R HR Salary Analysis Case Study
11 Step 1b ML Fundamentals Preparing Data for Polynomial Regression
12 Step 2a Building Linear Polynomial Regression Models in R A Comparison
13 Step 2b Building a Polynomial Regression Model Adding Squared Cubed Terms
14 Step 3a Visualizing Regression Results Creating Scatter Plots with ggplot2 in
15 Step 3b Visualizing Linear Regression Plotting Predictions vs Observations
16 Step 3c Polynomial Regression Curve Fitting for Better Predictions
17 Step 4a How to Make Single Predictions Using Polynomial Regression in R
18 Step 4b Predicting Salaries with Polynomial Regression A Practical Example
19 Step 1 Building a Reusable Framework for Nonlinear Regression Analysis in R
20 Step 2 Mastering Regression Model Visualization Increasing Data Resolution

1 How Does Support Vector Regression SVR Differ from Linear Regression
2 RBF Kernel SVR From Linear to Non Linear Support Vector Regression
3 Step 1a SVR Model Training Feature Scaling and Dataset Preparation in Python
4 Step 1b SVR in Python Importing Libraries and Dataset for Machine Learning
5 Step 2a Mastering Feature Scaling for Support Vector Regression in Python
6 Step 2b Reshaping Data for SVR Preparing Y Vector for Feature Scaling Python
7 Step 2c SVR Data Prep Scaling X Y Independently with StandardScaler
8 Step 3 SVM Regression Creating Training SVR Model with RBF Kernel in Python
9 Step 4 SVR Model Prediction Handling Scaled Data and Inverse Transformation
10 Step 5a How to Plot Support Vector Regression SVR Models Step by Step Guide
11 Step 5b SVR Scaling Inverse Transformation in Python
12 Step 1 SVR Tutorial Creating a Support Vector Machine Regressor in R
13 Step 2 Support Vector Regression Building a Predictive Model in Python

1 How to Build a Regression Tree Step by Step Guide for Machine Learning
2 Step 1a Decision Tree Regression Building a Model without Feature Scaling
3 Step 1b Uploading Preprocessing Data for Decision Tree Regression in Python
4 Step 2 Implementing DecisionTreeRegressor A Step by Step Guide in Python
5 Step 3 Implementing Decision Tree Regression in Python Making Predictions
6 Step 4 Visualizing Decision Tree Regression High Resolution Results
7 Step 1 Creating a Decision Tree Regressor Using rpart Function in R
8 Step 2 Decision Tree Regression Fixing Splits with rpart Control Parameter
9 Step 3 Non Continuous Regression Decision Tree Visualization Challenges
10 Step 4 Visualizing Decision Tree Understanding Intervals and Predictions

1 Understanding Random Forest Algorithm Intuition and Application in ML
2 Step 1 Building a Random Forest Regression Model with Python and Scikit Learn
3 Step 2 Creating a Random Forest Regressor Key Parameters and Model Fitting
4 Step 1 Building a Random Forest Model in R Regression Tutorial
5 Step 2 Visualizing Random Forest Regression Interpreting Stairs and Splits
6 Step 3 Fine Tuning Random Forest From 10 to 500 Trees for Accurate Prediction

1 Understanding R squared Evaluating Goodness of Fit in Regression Models
2 Understanding Adjusted R Squared Key Differences from R Squared Explained

2 Step 1 Mastering Regression Toolkit Comparing Models for Optimal Performance
3 Step 2 Creating Generic Code Templates for Various Regression Models in Python
4 Step 3 Evaluating Regression Models R Squared Performance Metrics Explained
5 Step 4 Implementing R Squared Score in Python with Scikit Learns Metrics
6 Step 1 Selecting the Best Regression Model R squared Evaluation in Python
7 Step 2 Selecting the Best Regression Model Random Forest vs SVR Performance
1. Machine Learning A-Z (Model Selection)

1 Optimizing Regression Models R Squared vs Adjusted R Squared Explained
2 Linear Regression Analysis Interpreting Coefficients for Business Decisions
3. Regression_Bonus

2 What is Classification in Machine Learning Fundamentals and Applications

1 Understanding Logistic Regression Predicting Categorical Outcomes
2 Logistic Regression Finding the Best Fit Curve Using Maximum Likelihood
3 Step 1a Building a Logistic Regression Model for Customer Behavior Prediction
4 Step 1b Implementing Logistic Regression in Python Data Preprocessing Guide
5 Step 2a Python Data Preprocessing for Logistic Regression Dataset Prep
6 Step 2b Data Preprocessing Feature Scaling Techniques for Logistic Regression
7 Step 3a How to Import and Use LogisticRegression Class from Scikit learn
8 Step 3b Training Logistic Regression Model Fit Method for Classification
9 Step 4a Formatting Single Observation Input for Logistic Regression Predict
10 Step 4b Predicted vs Real Purchase Decisions in Logistic Regression
11 Step 5 Comparing Predicted vs Real Results Python Logistic Regression Guide
12 Step 6a Implementing Confusion Matrix and Accuracy Score in Scikit Learn
13 Step 6b Evaluating Classification Models Confusion Matrix Accuracy Metrics
14 Step 7a Visualizing Logistic Regression Decision Boundaries in Python 2D Plot
15 Step 7b Interpreting Logistic Regression Results Prediction Regions Explained
16 Step 7c Visualizing Logistic Regression Performance on New Data in Python
18 Step 1 Data Preprocessing for Logistic Regression in R Preparing Your Dataset
19 Step 2 How to Create a Logistic Regression Classifier Using Rs GLM Function
20 Step 3 How to Use R for Logistic Regression Prediction Step by Step Guide
21 Step 4 How to Assess Model Accuracy Using a Confusion Matrix in R
23 Step 5a Interpreting Logistic Regression Plots Prediction Regions Explained
24 Step 5b Logistic Regression Linear Classifiers Prediction Boundaries
25 Step 5c Data Viz in R Colorizing Pixels for Logistic Regression
27 Optimizing R Scripts for Machine Learning Building a Classification Template

1 K Nearest Neighbors KNN Explained A Beginners Guide to Classification
2 Step 1 Python KNN Tutorial Classifying Customer Data for Targeted Marketing
3 Step 2 Building a K Nearest Neighbors Model Scikit Learn KNeighborsClassifier
4 Step 3 Visualizing KNN Decision Boundaries Python Tutorial for Beginners
5 Step 1 Implementing KNN Classification in R Setup Data Preparation
6 Step 2 Building a KNN Classifier Preparing Training and Test Sets in R
7 Step 3 Implementing KNN Classification in R Adapting the Classifier Template

1 Support Vector Machines Explained Hyperplanes and Support Vectors in ML
2 Step 1 Building a Support Vector Machine Model with Scikit learn in Python
3 Step 2 Building a Support Vector Machine Model with Sklearns SVC in Python
4 Step 3 Understanding Linear SVM Limitations Why It Didnt Beat kNN Classifier
5 Step 1 Building a Linear SVM Classifier in R Data Import and Initial Setup
6 Step 2 Creating Evaluating Linear SVM Classifier in R Predictions Results

1 From Linear to Non Linear SVM Exploring Higher Dimensional Spaces
2 Support Vector Machines Transforming Non Linear Data for Linear Separation
3 Kernel Trick SVM Machine Learning for Non Linear Classification
4 Understanding Different Types of Kernel Functions for Machine Learning
5 Mastering Support Vector Regression Non Linear SVR with RBF Kernel Explained
6 Step 1 Python Kernel SVM Applying RBF to Solve Non Linear Classification
7 Step 2 Mastering Kernel SVM Improving Accuracy with Non Linear Classifiers
8 Step 1 Kernel SVM vs Linear SVM Overcoming Non Linear Separability in R
9 Step 2 Building a Gaussian Kernel SVM Classifier for Advanced Machine Learning
10 Step 3 Visualizing Kernel SVM Non Linear Classification in Machine Learning

1 Understanding Bayes Theorem Intuitively From Probability to Machine Learning
2 Understanding Naive Bayes Algorithm Probabilistic Classification Explained
3 Bayes Theorem in Machine Learning Step by Step Probability Calculation
4 Why is Naive Bayes Called Naive Understanding the Algorithms Assumptions
5 Step 1 Naive Bayes in Python Applying ML to Social Network Ads Optimisation
6 Step 2 Python Naive Bayes Training and Evaluating a Classifier on Real Data
7 Step 3 Analyzing Naive Bayes Algorithm Results Accuracy and Predictions
8 Step 1 Getting Started with Naive Bayes Algorithm in R for Classification
9 Step 2 Troubleshooting Naive Bayes Classification Empty Prediction Vectors
10 Step 3 Visualizing Naive Bayes Results Creating Confusion Matrix and Graphs

1 How Decision Tree Algorithms Work Step by Step Guide with Examples
2 Step 1 Implementing Decision Tree Classification in Python with Scikit learn
3 Step 2 Training a Decision Tree Classifier Optimizing Performance in Python
4 Step 1 R Tutorial Creating a Decision Tree Classifier with rpart Library
5 Step 2 Decision Tree Classifier Optimizing Prediction Boundaries in R
6 Step 3 Decision Tree Visualization Exploring Splits and Conditions in R

1 Understanding Random Forest Decision Trees and Majority Voting Explained
2 Step 1 Implementing Random Forest Classification in Python with Scikit Learn
3 Step 2 Random Forest Evaluation Confusion Matrix Accuracy Metrics
4 Step 1 Random Forest Classifier From Template to Implementation in R
5 Step 2 Random Forest Classification Visualizing Predictions Results
6 Step 3 Evaluating Random Forest Performance Test Set Results Overfitting

2 Mastering the Confusion Matrix True Positives Negatives and Errors
3 Step 1 How to Choose the Right Classification Algorithm for Your Dataset
4 Step 2 Optimizing Model Selection Streamlined Classification Code in Python
5 Step 3 Evaluating Classification Algorithms Accuracy Metrics in Python
6 Step 4 Model Selection Process Evaluating Classification Algorithms
1. Machine Learning A-Z (Model Selection)

1 Logistic Regression Interpreting Predictions and Errors in Data Science
2 Machine Learning Model Evaluation Accuracy Paradox and Better Metrics
3 Understanding CAP Curves Assessing Model Performance in Data Science 2024
4 Mastering CAP Analysis Assessing Classification Models with Accuracy Ratio

1 What is Clustering in Machine Learning Introduction to Unsupervised Learning
2 K Means Clustering Tutorial Visualizing the Machine Learning Algorithm
3 How to Use the Elbow Method in K Means Clustering A Step by Step Guide
4 K Means Algorithm Solving the Random Initialization Trap in Clustering
5 Step 1a Python K Means Tutorial Identifying Customer Patterns in Mall Data
6 Step 1b K Means Clustering Data Preparation in Google ColabJupyter
7 Step 2a K Means Clustering in Python Selecting Relevant Features for Analysis
8 Step 2b K Means Clustering Optimizing Features for 2D Visualization
9 Step 3a Implementing the Elbow Method for K Means Clustering in Python
10 Step 3b Optimizing K means Clustering WCSS and Elbow Method Implementation
11 Step 3c Plotting the Elbow Method Graph for K Means Clustering in Python
12 Step 4 Creating a Dependent Variable from K Means Clustering Results in Python
13 Step 5a Visualizing K Means Clusters of Customer Data with Python Scatter
14 Step 5b Visualizing K Means Clusters Plotting Customer Segments in Python
15 Step 5c Analyzing Customer Segments Insights from K means Clustering
16 Step 1 K Means Clustering in R Importing Exploring Segmentation Data
17 Step 2 K Means Algorithm Implementation in R Fitting and Analyzing Mall Data

1 How to Perform Hierarchical Clustering Step by Step Guide for Machine Learning
2 Visualizing Cluster Dissimilarity Dendrograms in Hierarchical Clustering
3 Mastering Hierarchical Clustering Dendrogram Analysis and Threshold Setting
4 Step 1 Getting Started with Hierarchical Clustering Data Setup in Python
5 Step 2a Implementing Hierarchical Clustering Building a Dendrogram with SciPy
6 Step 2b Visualizing Hierarchical Clustering Dendrogram Basics in Python
7 Step 2c Interpreting Dendrograms Optimal Clusters in Hierarchical Clustering
8 Step 3a Building a Hierarchical Clustering Model with Scikit learn in Python
9 Step 3b Comparing 3 vs 5 Clusters in Hierarchical Clustering Python Example
10 Step 1 R Data Import for Clustering Annual Income Spending Score Analysis
11 Step 2 Using Hclust in R Building Interpreting Dendrograms for Clustering
12 Step 3 Implementing Hierarchical Clustering Using Cat Tree Method in R
13 Step 4 Cluster Plot Method Visualizing Hierarchical Clustering Results in R
14 Step 5 Hierarchical Clustering in R Understanding Customer Spending Patterns

1 Apriori Algorithm Uncovering Hidden Patterns in Data Mining Association Rules
2 Step 1 Association Rule Learning Boost Sales with Python Data Mining
3 Step 2 Creating a List of Transactions for Market Basket Analysis in Python
4 Step 3 Configuring Apriori Function Support Confidence and Lift in Python
5 Step 4 Visualizing Apriori Algorithm Results for Product Deals in Python
6 Step 1 Creating a Sparse Matrix for Association Rule Mining in R
7 Step 2 Optimizing Apriori Model Choosing Minimum Support and Confidence
8 Step 3 Optimizing Product Placement Apriori Algorithm Lift Confidence

1 Mastering ECLAT Support Based Approach to Market Basket Optimization
2 Python Tutorial Adapting Apriori to Eclat for Efficient Frequent Itemset Mining
3 Eclat vs Apriori Simplified Association Rule Learning in Data Mining
3. Eclat

1 Multi Armed Bandit Exploration vs Exploitation in Reinforcement Learning
2 Upper Confidence Bound Algorithm Solving Multi Armed Bandit Problems in ML
3 Step 1 Upper Confidence Bound Solving Multi Armed Bandit Problem in Python
4 Step 2 Implementing UCB Algorithm in Python Data Preparation
5 Step 3 Python Code for Upper Confidence Bound Setting Up Key Variables
6 Step 4 Python for RL Coding the UCB Algorithm Step by Step
7 Step 5 Coding Upper Confidence Bound Optimizing Ad Selection in Python
8 Step 6 Reinforcement Learning Finalizing UCB Algorithm in Python
9 Step 7 Visualizing UCB Algorithm Results Histogram Analysis in Python
10 Step 1 Exploring Upper Confidence Bound in R Multi Armed Bandit Problems
11 Step 2 UCB Algorithm in R Calculating Average Reward Confidence Interval
12 Step 3 Optimizing Ad Selection UCB Multi Armed Bandit Algorithm Explained
13 Step 4 UCB Algorithm Performance Analyzing Ad Selection with Histograms

1 Understanding Thompson Sampling Algorithm Intuition and Implementation
2 Deterministic vs Probabilistic UCB and Thompson Sampling in Machine Learning
3 Step 1 Python Implementation of Thompson Sampling for Bandit Problems
4 Step 2 Optimizing Ad Selection with Thompson Sampling Algorithm in Python
5 Step 3 Python Code for Thompson Sampling Maximizing Random Beta Distributions
6 Step 4 Beating UCB with Thompson Sampling Python Multi Armed Bandit Tutorial
8 Step 1 Thompson Sampling vs UCB Optimizing Ad Click Through Rates in R
9 Step 2 Reinforcement Learning Thompson Sampling Outperforms UCB Algorithm

2 NLP Basics Understanding Bag of Words and Its Applications in Machine Learning
3 Deep NLP Sequence to Sequence Models Exploring Natural Language Processing
4 From IfElse Rules to CNNs Evolution of Natural Language Processing
5 Implementing Bag of Words in NLP A Step by Step Tutorial
6 Step 1 Getting Started with Natural Language Processing Sentiment Analysis
7 Step 2 Importing TSV Data for Sentiment Analysis Python NLP Data Processing
8 Step 3 Text Cleaning for NLP Remove Punctuation and Convert to Lowercase
9 Step 4 Text Preprocessing Stemming and Stop Word Removal for NLP in Python
10 Step 5 Tokenization and Feature Extraction for Naive Bayes Sentiment Analysis
11 Step 6 Training and Evaluating a Naive Bayes Classifier for Sentiment Analysis
14 Step 1 Text Classification Using Bag of Words and Random Forest in R
16 Step 2 NLP Data Preprocessing in R Importing TSV Files for Sentiment Analysis
17 Step 3 NLP in R Initialising a Corpus for Sentiment Analysis
18 Step 4 NLP Data Cleaning Lowercase Transformation in R for Text Analysis
19 Step 5 Sentiment Analysis Data Cleaning Removing Numbers with TM Map
20 Step 6 Cleaning Text Data Removing Punctuation for NLP and Classification
21 Step 7 Simplifying Corpus Using SnowballC Package to Remove Stop Words in R
22 Step 8 Enhancing Text Classification Stemming for Efficient Feature Matrices
23 Step 9 Removing Extra Spaces for NLP Sentiment Analysis Text Cleaning
24 Step 10 Building a Document Term Matrix for NLP Text Classification

2 Introduction to Deep Learning From Historical Context to Modern Applications

1 Understanding CNN Layers Convolution ReLU Pooling and Flattening Explained
2 Deep Learning Basics Exploring Neurons Synapses and Activation Functions
3 Neural Network Basics Understanding Activation Functions in Deep Learning
4 How Do Neural Networks Work Step by Step Guide to Deep Learning Algorithms
5 How Do Neural Networks Learn Deep Learning Fundamentals Explained
6 Deep Learning Fundamentals Gradient Descent vs Brute Force Optimization
7 Stochastic vs Batch Gradient Descent Deep Learning Fundamentals
8 Deep Learning Fundamentals Training Neural Networks Step by Step
9 Bank Customer Churn Prediction Machine Learning Model with TensorFlow
10 Step 1 ANN in Python Predicting Customer Churn with TensorFlow
11 Step 2 TensorFlow 20 Tutorial Preprocessing Data for Customer Churn Model
12 Step 3 Designing ANN Sequential Model Dense Layers for Deep Learning
13 Step 4 Train Neural Network Compile Fit for Customer Churn Prediction
14 Step 5 Implementing ANN for Churn Prediction From Model to Confusion Matrix
15 Step 1 How to Preprocess Data for Artificial Neural Networks in R
16 Step 2 How to Install and Initialize H2O for Efficient Deep Learning in R
17 Step 3 Building Deep Learning Model H2O Neural Network Layer Config
18 Step 4 H2O Deep Learning Making Predictions and Evaluating Model Accuracy

1 Understanding CNN Layers Convolution ReLU Pooling and Flattening Explained
2 Introduction to CNNs Understanding Deep Learning for Computer Vision
3 Step 1 Understanding Convolution in CNNs Feature Detection and Feature Maps
4 Step 1b Applying ReLU to Convolutional Layers Breaking Up Image Linearity
5 Step 2 Max Pooling in CNNs Enhancing Spatial Invariance for Image Recognition
6 Step 3 Understanding Flattening in Convolutional Neural Network Architecture
7 Step 4 Fully Connected Layers in CNNs Optimizing Feature Combination
8 Deep Learning Basics How Convolutional Neural Networks CNNs Process Images
9 Deep Learning Essentials Understanding Softmax and Cross Entropy in CNNs
11 Step 1 Intro to CNNs for Image Classification
12 Step 2 Keras ImageDataGenerator Prevent Overfitting in CNN Models
13 Step 3 TensorFlow CNN Convolution to Output Layer for Vision Tasks
14 Step 4 CNN Training Epochs Loss Function Metrics in TensorFlow
15 Step 5 Making Single Predictions with Convolutional Neural Networks in Python
16 Hands on CNN Training Using Jupyter Notebook for Image Classification

1 PCA Algorithm Intuition Reducing Dimensions in Unsupervised Learning
2 Step 1 PCA in Python Reducing Wine Dataset Features with Scikit learn
3 Step 2 PCA in Action Reducing Dimensions and Predicting Customer Segments
4 Step 1 in R Understanding Principal Component Analysis for Feature Extraction
5 Step 2 Using preProcess Function in R for PCA Extracting Principal Components
6 Step 3 Implementing PCA and SVM for Customer Segmentation Practical Guide

1 LDA Intuition Maximizing Class Separation in Machine Learning Algorithms
2 Mastering Linear Discriminant Analysis Step by Step Python Implementation
3 Step by Step Guide Applying LDA for Feature Extraction in Machine Learning

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

1 Mastering Model Evaluation K Fold Cross Validation Techniques Explained
2 How to Master the Bias Variance Tradeoff in Machine Learning Models
3 K Fold Cross Validation in Python Improve Machine Learning Model Performance
4 Optimizing SVM Models with GridSearchCV A Step by Step Python Tutorial
5 Evaluating ML Model Accuracy K Fold Cross Validation Implementation in R
6 Optimizing SVM Models with Grid Search A Step by Step R Tutorial

1 How to Use XGBoost in Python for Cancer Prediction with High Accuracy
3 XGBoost Tutorial Implementing Gradient Boosting for Classification Problems

1 Logistic Regression Intuition

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Course modified date: 26 Feb 2025

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Udemy - Machine Learning A-Z AI, Python & R + ChatGPT Prize 2025

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