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| 001 Apriori Algorithm Uncovering Hidden Patterns in Data Mining Association Rules.mp4 |
56.06Мб |
| 001 Apriori Algorithm Uncovering Hidden Patterns in Data Mining Association Rules.srt |
30.40Кб |
| 001 Data Preprocessing for Beginners Preparing Your Dataset for Machine Learning.mp4 |
4.92Мб |
| 001 Data Preprocessing for Beginners Preparing Your Dataset for Machine Learning.srt |
2.78Кб |
| 001 dataset.zip |
221.28Мб |
| 001 From Linear to Non-Linear SVM Exploring Higher Dimensional Spaces.mp4 |
10.12Мб |
| 001 From Linear to Non-Linear SVM Exploring Higher Dimensional Spaces.srt |
5.16Кб |
| 001 How Decision Tree Algorithms Work Step-by-Step Guide with Examples.mp4 |
25.06Мб |
| 001 How Decision Tree Algorithms Work Step-by-Step Guide with Examples.srt |
14.82Кб |
| 001 How Does Support Vector Regression --(SVR--) Differ from Linear Regression.mp4 |
25.13Мб |
| 001 How Does Support Vector Regression --(SVR--) Differ from Linear Regression.srt |
13.64Кб |
| 001 How to Build a Regression Tree Step-by-Step Guide for Machine Learning.mp4 |
34.07Мб |
| 001 How to Build a Regression Tree Step-by-Step Guide for Machine Learning.srt |
18.67Кб |
| 001 How to Perform Hierarchical Clustering Step-by-Step Guide for Machine Learning.mp4 |
24.19Мб |
| 001 How to Perform Hierarchical Clustering Step-by-Step Guide for Machine Learning.srt |
16.02Кб |
| 001 How to Use XGBoost in Python for Cancer Prediction with High Accuracy.mp4 |
45.56Мб |
| 001 How to Use XGBoost in Python for Cancer Prediction with High Accuracy.srt |
29.98Кб |
| 001 Huge Congrats for completing the challenge!.html |
6.93Кб |
| 001 Kernel PCA in Python Improving Classification Accuracy with Feature Extraction.mp4 |
34.06Мб |
| 001 Kernel PCA in Python Improving Classification Accuracy with Feature Extraction.srt |
21.63Кб |
| 001 K-Nearest Neighbors --(KNN--) Explained A Beginner--'s Guide to Classification.mp4 |
15.00Мб |
| 001 K-Nearest Neighbors --(KNN--) Explained A Beginner--'s Guide to Classification.srt |
9.14Кб |
| 001 LDA Intuition Maximizing Class Separation in Machine Learning Algorithms.mp4 |
11.80Мб |
| 001 LDA Intuition Maximizing Class Separation in Machine Learning Algorithms.srt |
5.95Кб |
| 001 Logistic Regression Interpreting Predictions and Errors in Data Science.mp4 |
24.58Мб |
| 001 Logistic Regression Interpreting Predictions and Errors in Data Science.srt |
12.59Кб |
| 001 Logistic Regression Intuition.mp4 |
52.69Мб |
| 001 Logistic Regression Intuition.srt |
28.27Кб |
| 001 Machine-Learning-A-Z-Model-Selection.zip |
161.91Кб |
| 001 Machine-Learning-A-Z-Model-Selection.zip |
160.01Кб |
| 001 Make sure you have this Model Selection folder ready.html |
3.19Кб |
| 001 Make sure you have this Model Selection folder ready.html |
3.21Кб |
| 001 Mastering ECLAT Support-Based Approach to Market Basket Optimization.mp4 |
16.03Мб |
| 001 Mastering ECLAT Support-Based Approach to Market Basket Optimization.srt |
9.34Кб |
| 001 Mastering Model Evaluation K-Fold Cross-Validation Techniques Explained.mp4 |
29.69Мб |
| 001 Mastering Model Evaluation K-Fold Cross-Validation Techniques Explained.srt |
16.38Кб |
| 001 Multi-Armed Bandit Exploration vs Exploitation in Reinforcement Learning.mp4 |
48.33Мб |
| 001 Multi-Armed Bandit Exploration vs Exploitation in Reinforcement Learning.srt |
26.09Кб |
| 001 Optimizing Regression Models R-Squared vs Adjusted R-Squared Explained.mp4 |
27.47Мб |
| 001 Optimizing Regression Models R-Squared vs Adjusted R-Squared Explained.srt |
14.31Кб |
| 001 PCA Algorithm Intuition Reducing Dimensions in Unsupervised Learning.mp4 |
11.05Мб |
| 001 PCA Algorithm Intuition Reducing Dimensions in Unsupervised Learning.srt |
5.69Кб |
| 001 Simple Linear Regression Understanding the Equation and Potato Yield Prediction.mp4 |
7.34Мб |
| 001 Simple Linear Regression Understanding the Equation and Potato Yield Prediction.srt |
3.60Кб |
| 001 Startup Success Prediction Regression Model for VC Fund Decision-Making.mp4 |
11.59Мб |
| 001 Startup Success Prediction Regression Model for VC Fund Decision-Making.srt |
6.26Кб |
| 001 Step 1 - Data Preprocessing in Python Preparing Your Dataset for ML Models.mp4 |
16.49Мб |
| 001 Step 1 - Data Preprocessing in Python Preparing Your Dataset for ML Models.srt |
8.98Кб |
| 001 Support Vector Machines Explained Hyperplanes and Support Vectors in ML.mp4 |
31.64Мб |
| 001 Support Vector Machines Explained Hyperplanes and Support Vectors in ML.srt |
17.33Кб |
| 001 Understanding Bayes--' Theorem Intuitively From Probability to Machine Learning.mp4 |
62.58Мб |
| 001 Understanding Bayes--' Theorem Intuitively From Probability to Machine Learning.srt |
36.92Кб |
| 001 Understanding CNN Layers Convolution, ReLU, Pooling, and Flattening Explained.mp4 |
7.94Мб |
| 001 Understanding CNN Layers Convolution, ReLU, Pooling, and Flattening Explained.mp4 |
10.49Мб |
| 001 Understanding CNN Layers Convolution, ReLU, Pooling, and Flattening Explained.srt |
4.51Кб |
| 001 Understanding CNN Layers Convolution, ReLU, Pooling, and Flattening Explained.srt |
6.03Кб |
| 001 Understanding Logistic Regression Predicting Categorical Outcomes.mp4 |
11.08Мб |
| 001 Understanding Logistic Regression Predicting Categorical Outcomes.srt |
8.22Кб |
| 001 Understanding Polynomial Linear Regression Applications and Examples.mp4 |
15.82Мб |
| 001 Understanding Polynomial Linear Regression Applications and Examples.srt |
8.73Кб |
| 001 Understanding Random Forest Algorithm Intuition and Application in ML.mp4 |
22.70Мб |
| 001 Understanding Random Forest Algorithm Intuition and Application in ML.srt |
11.52Кб |
| 001 Understanding Random Forest Decision Trees and Majority Voting Explained.mp4 |
15.69Мб |
| 001 Understanding Random Forest Decision Trees and Majority Voting Explained.srt |
8.04Кб |
| 001 Understanding R-squared Evaluating Goodness of Fit in Regression Models.mp4 |
7.96Мб |
| 001 Understanding R-squared Evaluating Goodness of Fit in Regression Models.srt |
7.56Кб |
| 001 Understanding Thompson Sampling Algorithm Intuition and Implementation.mp4 |
58.45Мб |
| 001 Understanding Thompson Sampling Algorithm Intuition and Implementation.srt |
33.41Кб |
| 001 Welcome Challenge!.html |
7.62Кб |
| 001 Welcome to Part 10 - Model Selection & Boosting.html |
3.14Кб |
| 001 Welcome to Part 1 - Data Preprocessing.html |
2.74Кб |
| 001 Welcome to Part 2 - Regression.html |
3.01Кб |
| 001 Welcome to Part 3 - Classification.html |
3.08Кб |
| 001 Welcome to Part 4 - Clustering.html |
2.97Кб |
| 001 Welcome to Part 5 - Association Rule Learning.html |
2.70Кб |
| 001 Welcome to Part 6 - Reinforcement Learning.html |
3.74Кб |
| 001 Welcome to Part 7 - Natural Language Processing.html |
3.97Кб |
| 001 Welcome to Part 8 - Deep Learning.html |
3.08Кб |
| 001 Welcome to Part 9 - Dimensionality Reduction.html |
3.44Кб |
| 001 What is Clustering in Machine Learning Introduction to Unsupervised Learning.mp4 |
6.94Мб |
| 001 What is Clustering in Machine Learning Introduction to Unsupervised Learning.srt |
5.88Кб |
| 002 Bonus How To UNLOCK Top Salaries (Live Training).html |
4.00Кб |
| 002 Data Preprocessing Tutorial Understanding Independent vs Dependent Variables.mp4 |
6.04Мб |
| 002 Data Preprocessing Tutorial Understanding Independent vs Dependent Variables.srt |
3.30Кб |
| 002 Deep Learning Basics Exploring Neurons, Synapses, and Activation Functions.mp4 |
49.82Мб |
| 002 Deep Learning Basics Exploring Neurons, Synapses, and Activation Functions.srt |
31.40Кб |
| 002 Deterministic vs Probabilistic UCB and Thompson Sampling in Machine Learning.mp4 |
25.23Мб |
| 002 Deterministic vs Probabilistic UCB and Thompson Sampling in Machine Learning.srt |
13.59Кб |
| 002 Get Excited about ML Predict Car Purchases with Python --& Scikit-learn in 5 mins.mp4 |
14.39Мб |
| 002 Get Excited about ML Predict Car Purchases with Python --& Scikit-learn in 5 mins.srt |
8.34Кб |
| 002 How to Find the Best Fit Line Understanding Ordinary Least Squares Regression.mp4 |
5.99Мб |
| 002 How to Find the Best Fit Line Understanding Ordinary Least Squares Regression.srt |
5.25Кб |
| 002 How to Master the Bias-Variance Tradeoff in Machine Learning Models.mp4 |
15.05Мб |
| 002 How to Master the Bias-Variance Tradeoff in Machine Learning Models.srt |
8.36Кб |
| 002 Implementing Kernel PCA for Non-Linear Data Step-by-Step Guide.mp4 |
68.90Мб |
| 002 Implementing Kernel PCA for Non-Linear Data Step-by-Step Guide.srt |
36.37Кб |
| 002 Introduction to CNNs Understanding Deep Learning for Computer Vision.mp4 |
48.76Мб |
| 002 Introduction to CNNs Understanding Deep Learning for Computer Vision.srt |
26.28Кб |
| 002 Introduction to Deep Learning From Historical Context to Modern Applications.mp4 |
42.89Мб |
| 002 Introduction to Deep Learning From Historical Context to Modern Applications.srt |
21.07Кб |
| 002 K-Means Clustering Tutorial Visualizing the Machine Learning Algorithm.mp4 |
5.66Мб |
| 002 K-Means Clustering Tutorial Visualizing the Machine Learning Algorithm.srt |
4.82Кб |
| 002 Linear Regression Analysis Interpreting Coefficients for Business Decisions.mp4 |
29.03Мб |
| 002 Linear Regression Analysis Interpreting Coefficients for Business Decisions.srt |
15.01Кб |
| 002 Logistic Regression Finding the Best Fit Curve Using Maximum Likelihood.mp4 |
9.62Мб |
| 002 Logistic Regression Finding the Best Fit Curve Using Maximum Likelihood.srt |
6.03Кб |
| 002 Machine Learning Model Evaluation Accuracy Paradox and Better Metrics.mp4 |
6.87Мб |
| 002 Machine Learning Model Evaluation Accuracy Paradox and Better Metrics.srt |
3.52Кб |
| 002 Machine Learning Workflow Importing, Modeling, and Evaluating Your ML Model.mp4 |
3.67Мб |
| 002 Machine Learning Workflow Importing, Modeling, and Evaluating Your ML Model.srt |
2.74Кб |
| 002 Mastering Linear Discriminant Analysis Step-by-Step Python Implementation.mp4 |
45.78Мб |
| 002 Mastering Linear Discriminant Analysis Step-by-Step Python Implementation.srt |
29.53Кб |
| 002 Mastering the Confusion Matrix True Positives, Negatives, and Errors.mp4 |
12.29Мб |
| 002 Mastering the Confusion Matrix True Positives, Negatives, and Errors.srt |
7.71Кб |
| 002 Model Selection and Boosting Additional Content.html |
3.37Кб |
| 002 Multiple Linear Regression Independent Variables --& Prediction Models.mp4 |
7.54Мб |
| 002 Multiple Linear Regression Independent Variables --& Prediction Models.srt |
4.06Кб |
| 002 NLP Basics Understanding Bag of Words and Its Applications in Machine Learning.mp4 |
9.24Мб |
| 002 NLP Basics Understanding Bag of Words and Its Applications in Machine Learning.srt |
4.99Кб |
| 002 Python Tutorial Adapting Apriori to Eclat for Efficient Frequent Itemset Mining.mp4 |
36.95Мб |
| 002 Python Tutorial Adapting Apriori to Eclat for Efficient Frequent Itemset Mining.srt |
24.86Кб |
| 002 RBF Kernel SVR From Linear to Non-Linear Support Vector Regression.mp4 |
10.67Мб |
| 002 RBF Kernel SVR From Linear to Non-Linear Support Vector Regression.srt |
6.87Кб |
| 002 Step 1a - Building a Polynomial Regression Model for Salary Prediction in Python.mp4 |
11.88Мб |
| 002 Step 1a - Building a Polynomial Regression Model for Salary Prediction in Python.srt |
7.28Кб |
| 002 Step 1a - Decision Tree Regression Building a Model without Feature Scaling.mp4 |
14.42Мб |
| 002 Step 1a - Decision Tree Regression Building a Model without Feature Scaling.srt |
8.00Кб |
| 002 Step 1 - Association Rule Learning Boost Sales with Python Data Mining.mp4 |
30.60Мб |
| 002 Step 1 - Association Rule Learning Boost Sales with Python Data Mining.srt |
15.77Кб |
| 002 Step 1 - Building a Random Forest Regression Model with Python and Scikit-Learn.mp4 |
18.14Мб |
| 002 Step 1 - Building a Random Forest Regression Model with Python and Scikit-Learn.srt |
10.29Кб |
| 002 Step 1 - Building a Support Vector Machine Model with Scikit-learn in Python.mp4 |
19.07Мб |
| 002 Step 1 - Building a Support Vector Machine Model with Scikit-learn in Python.srt |
10.12Кб |
| 002 Step 1 - Implementing Decision Tree Classification in Python with Scikit-learn.mp4 |
18.45Мб |
| 002 Step 1 - Implementing Decision Tree Classification in Python with Scikit-learn.srt |
10.39Кб |
| 002 Step 1 - Implementing Random Forest Classification in Python with Scikit-Learn.mp4 |
18.35Мб |
| 002 Step 1 - Implementing Random Forest Classification in Python with Scikit-Learn.srt |
10.10Кб |
| 002 Step 1 - Mastering Regression Toolkit Comparing Models for Optimal Performance.mp4 |
14.64Мб |
| 002 Step 1 - Mastering Regression Toolkit Comparing Models for Optimal Performance.srt |
7.67Кб |
| 002 Step 1 PCA in Python Reducing Wine Dataset Features with Scikit-learn.mp4 |
51.93Мб |
| 002 Step 1 PCA in Python Reducing Wine Dataset Features with Scikit-learn.srt |
34.57Кб |
| 002 Step 1 - Python KNN Tutorial Classifying Customer Data for Targeted Marketing.mp4 |
18.92Мб |
| 002 Step 1 - Python KNN Tutorial Classifying Customer Data for Targeted Marketing.srt |
9.81Кб |
| 002 Step 2 - Data Preprocessing Techniques From Raw Data to ML-Ready Datasets.mp4 |
16.52Мб |
| 002 Step 2 - Data Preprocessing Techniques From Raw Data to ML-Ready Datasets.srt |
11.38Кб |
| 002 Support Vector Machines Transforming Non-Linear Data for Linear Separation.mp4 |
18.53Мб |
| 002 Support Vector Machines Transforming Non-Linear Data for Linear Separation.srt |
12.61Кб |
| 002 Understanding Adjusted R-Squared Key Differences from R-Squared Explained.mp4 |
16.90Мб |
| 002 Understanding Adjusted R-Squared Key Differences from R-Squared Explained.srt |
8.43Кб |
| 002 Understanding Naive Bayes Algorithm Probabilistic Classification Explained.mp4 |
42.30Мб |
| 002 Understanding Naive Bayes Algorithm Probabilistic Classification Explained.srt |
27.22Кб |
| 002 Upper Confidence Bound Algorithm Solving Multi-Armed Bandit Problems in ML.mp4 |
43.75Мб |
| 002 Upper Confidence Bound Algorithm Solving Multi-Armed Bandit Problems in ML.srt |
26.55Кб |
| 002 Visualizing Cluster Dissimilarity Dendrograms in Hierarchical Clustering.mp4 |
27.08Мб |
| 002 Visualizing Cluster Dissimilarity Dendrograms in Hierarchical Clustering.srt |
15.84Кб |
| 002 What is Classification in Machine Learning Fundamentals and Applications.mp4 |
7.77Мб |
| 002 What is Classification in Machine Learning Fundamentals and Applications.srt |
4.14Кб |
| 003 Bayes Theorem in Machine Learning Step-by-Step Probability Calculation.mp4 |
18.61Мб |
| 003 Bayes Theorem in Machine Learning Step-by-Step Probability Calculation.srt |
10.68Кб |
| 003 Conclusion of Part 2 - Regression.html |
3.92Кб |
| 003 Data Preprocessing Importance of Training-Test Split in ML Model Evaluation.mp4 |
6.30Мб |
| 003 Data Preprocessing Importance of Training-Test Split in ML Model Evaluation.srt |
3.30Кб |
| 003 Deep Learning Quiz.html |
20.20Кб |
| 003 Deep NLP --& Sequence-to-Sequence Models Exploring Natural Language Processing.mp4 |
12.89Мб |
| 003 Deep NLP --& Sequence-to-Sequence Models Exploring Natural Language Processing.srt |
6.36Кб |
| 003 Download-the-PDF.url |
68б |
| 003 Eclat.zip |
48.54Кб |
| 003 Eclat vs Apriori Simplified Association Rule Learning in Data Mining.mp4 |
31.71Мб |
| 003 Eclat vs Apriori Simplified Association Rule Learning in Data Mining.srt |
17.19Кб |
| 003 Evaluating Regression Models Performance Quiz.html |
20.32Кб |
| 003 Get all the Datasets, Codes and Slides here.html |
2.66Кб |
| 003 How to Use the Elbow Method in K-Means Clustering A Step-by-Step Guide.mp4 |
11.47Мб |
| 003 How to Use the Elbow Method in K-Means Clustering A Step-by-Step Guide.srt |
6.90Кб |
| 003 Kernel Trick SVM Machine Learning for Non-Linear Classification.mp4 |
37.99Мб |
| 003 Kernel Trick SVM Machine Learning for Non-Linear Classification.srt |
19.30Кб |
| 003 K-Fold Cross-Validation in Python Improve Machine Learning Model Performance.mp4 |
44.73Мб |
| 003 K-Fold Cross-Validation in Python Improve Machine Learning Model Performance.srt |
27.12Кб |
| 003 Machine Learning Toolkit Importing NumPy, Matplotlib, and Pandas Libraries.mp4 |
10.98Мб |
| 003 Machine Learning Toolkit Importing NumPy, Matplotlib, and Pandas Libraries.srt |
6.20Кб |
| 003 Mastering Hierarchical Clustering Dendrogram Analysis and Threshold Setting.mp4 |
34.97Мб |
| 003 Mastering Hierarchical Clustering Dendrogram Analysis and Threshold Setting.srt |
19.36Кб |
| 003 Neural Network Basics Understanding Activation Functions in Deep Learning.mp4 |
26.12Мб |
| 003 Neural Network Basics Understanding Activation Functions in Deep Learning.srt |
14.20Кб |
| 003 Regression-Bonus.zip |
364.49Кб |
| 003 R Tutorial Importing and Viewing Datasets for Data Preprocessing.mp4 |
8.45Мб |
| 003 R Tutorial Importing and Viewing Datasets for Data Preprocessing.srt |
4.73Кб |
| 003 Step 1a - Building a Logistic Regression Model for Customer Behavior Prediction.mp4 |
17.62Мб |
| 003 Step 1a - Building a Logistic Regression Model for Customer Behavior Prediction.srt |
9.28Кб |
| 003 Step 1a - Mastering Simple Linear Regression Key Concepts and Implementation.mp4 |
15.56Мб |
| 003 Step 1a - Mastering Simple Linear Regression Key Concepts and Implementation.srt |
9.86Кб |
| 003 Step 1a - SVR Model Training Feature Scaling and Dataset Preparation in Python.mp4 |
17.81Мб |
| 003 Step 1a - SVR Model Training Feature Scaling and Dataset Preparation in Python.srt |
9.60Кб |
| 003 Step 1b - Setting Up Data for Linear vs Polynomial Regression Comparison.mp4 |
18.29Мб |
| 003 Step 1b - Setting Up Data for Linear vs Polynomial Regression Comparison.srt |
10.64Кб |
| 003 Step 1b Uploading --& Preprocessing Data for Decision Tree Regression in Python.mp4 |
12.25Мб |
| 003 Step 1b Uploading --& Preprocessing Data for Decision Tree Regression in Python.srt |
7.00Кб |
| 003 Step 1 - How to Choose the Right Classification Algorithm for Your Dataset.mp4 |
18.04Мб |
| 003 Step 1 - How to Choose the Right Classification Algorithm for Your Dataset.srt |
10.04Кб |
| 003 Step 1 - Python Implementation of Thompson Sampling for Bandit Problems.mp4 |
17.91Мб |
| 003 Step 1 - Python Implementation of Thompson Sampling for Bandit Problems.srt |
10.69Кб |
| 003 Step 1 - Understanding Convolution in CNNs Feature Detection and Feature Maps.mp4 |
50.59Мб |
| 003 Step 1 - Understanding Convolution in CNNs Feature Detection and Feature Maps.srt |
28.71Кб |
| 003 Step 1 - Upper Confidence Bound Solving Multi-Armed Bandit Problem in Python.mp4 |
39.09Мб |
| 003 Step 1 - Upper Confidence Bound Solving Multi-Armed Bandit Problem in Python.srt |
27.75Кб |
| 003 Step 2 - Building a K-Nearest Neighbors Model Scikit-Learn KNeighborsClassifier.mp4 |
18.08Мб |
| 003 Step 2 - Building a K-Nearest Neighbors Model Scikit-Learn KNeighborsClassifier.srt |
10.05Кб |
| 003 Step 2 - Building a Support Vector Machine Model with Sklearn--'s SVC in Python.mp4 |
18.19Мб |
| 003 Step 2 - Building a Support Vector Machine Model with Sklearn--'s SVC in Python.srt |
10.00Кб |
| 003 Step 2 - Creating a List of Transactions for Market Basket Analysis in Python.mp4 |
52.69Мб |
| 003 Step 2 - Creating a List of Transactions for Market Basket Analysis in Python.srt |
35.36Кб |
| 003 Step 2 - Creating a Random Forest Regressor Key Parameters and Model Fitting.mp4 |
18.31Мб |
| 003 Step 2 - Creating a Random Forest Regressor Key Parameters and Model Fitting.srt |
9.78Кб |
| 003 Step 2 - Creating Generic Code Templates for Various Regression Models in Python.mp4 |
18.45Мб |
| 003 Step 2 - Creating Generic Code Templates for Various Regression Models in Python.srt |
10.36Кб |
| 003 Step 2 - PCA in Action Reducing Dimensions and Predicting Customer Segments.mp4 |
17.04Мб |
| 003 Step 2 - PCA in Action Reducing Dimensions and Predicting Customer Segments.srt |
9.47Кб |
| 003 Step 2 Random Forest Evaluation - Confusion Matrix --& Accuracy Metrics.mp4 |
18.94Мб |
| 003 Step 2 Random Forest Evaluation - Confusion Matrix --& Accuracy Metrics.srt |
10.77Кб |
| 003 Step 2 - Training a Decision Tree Classifier Optimizing Performance in Python.mp4 |
18.45Мб |
| 003 Step 2 - Training a Decision Tree Classifier Optimizing Performance in Python.srt |
10.44Кб |
| 003 Step-by-Step Guide Applying LDA for Feature Extraction in Machine Learning.mp4 |
64.93Мб |
| 003 Step-by-Step Guide Applying LDA for Feature Extraction in Machine Learning.srt |
33.56Кб |
| 003 Understanding CAP Curves Assessing Model Performance in Data Science 2024.mp4 |
32.55Мб |
| 003 Understanding CAP Curves Assessing Model Performance in Data Science 2024.srt |
18.25Кб |
| 003 Understanding Linear Regression Assumptions Linearity, Homoscedasticity --& More.mp4 |
13.09Мб |
| 003 Understanding Linear Regression Assumptions Linearity, Homoscedasticity --& More.srt |
7.75Кб |
| 003 XGBoost Tutorial Implementing Gradient Boosting for Classification Problems.mp4 |
57.54Мб |
| 003 XGBoost Tutorial Implementing Gradient Boosting for Classification Problems.srt |
29.92Кб |
| 004 Eclat Quiz.html |
20.15Кб |
| 004 Feature Scaling in Machine Learning Normalization vs Standardization Explained.mp4 |
16.33Мб |
| 004 Feature Scaling in Machine Learning Normalization vs Standardization Explained.srt |
10.73Кб |
| 004 From IfElse Rules to CNNs Evolution of Natural Language Processing.mp4 |
35.00Мб |
| 004 From IfElse Rules to CNNs Evolution of Natural Language Processing.srt |
17.79Кб |
| 004 How Do Neural Networks Work Step-by-Step Guide to Deep Learning Algorithms.mp4 |
39.41Мб |
| 004 How Do Neural Networks Work Step-by-Step Guide to Deep Learning Algorithms.srt |
22.91Кб |
| 004 How to Handle Categorical Variables in Linear Regression Models.mp4 |
22.67Мб |
| 004 How to Handle Categorical Variables in Linear Regression Models.srt |
12.12Кб |
| 004 How to Handle Missing Values in R Data Preprocessing for Machine Learning.mp4 |
18.29Мб |
| 004 How to Handle Missing Values in R Data Preprocessing for Machine Learning.srt |
9.61Кб |
| 004 How to Use Google Colab --& Machine Learning Course Folder.mp4 |
17.42Мб |
| 004 How to Use Google Colab --& Machine Learning Course Folder.srt |
10.60Кб |
| 004 K-Means++ Algorithm Solving the Random Initialization Trap in Clustering.mp4 |
13.19Мб |
| 004 K-Means++ Algorithm Solving the Random Initialization Trap in Clustering.srt |
8.08Кб |
| 004 LDA Quiz.html |
20.21Кб |
| 004 Mastering CAP Analysis Assessing Classification Models with Accuracy Ratio.mp4 |
19.46Мб |
| 004 Mastering CAP Analysis Assessing Classification Models with Accuracy Ratio.srt |
10.06Кб |
| 004 Optimizing SVM Models with GridSearchCV A Step-by-Step Python Tutorial.mp4 |
67.80Мб |
| 004 Optimizing SVM Models with GridSearchCV A Step-by-Step Python Tutorial.srt |
43.60Кб |
| 004 Step 1b - Applying ReLU to Convolutional Layers Breaking Up Image Linearity.mp4 |
20.58Мб |
| 004 Step 1b - Applying ReLU to Convolutional Layers Breaking Up Image Linearity.srt |
10.99Кб |
| 004 Step 1b Data Preprocessing for Linear Regression Import --& Split Data in Python.mp4 |
18.39Мб |
| 004 Step 1b Data Preprocessing for Linear Regression Import --& Split Data in Python.srt |
10.43Кб |
| 004 Step 1b - Implementing Logistic Regression in Python Data Preprocessing Guide.mp4 |
12.29Мб |
| 004 Step 1b - Implementing Logistic Regression in Python Data Preprocessing Guide.srt |
7.11Кб |
| 004 Step 1b - SVR in Python Importing Libraries and Dataset for Machine Learning.mp4 |
10.77Мб |
| 004 Step 1b - SVR in Python Importing Libraries and Dataset for Machine Learning.srt |
5.94Кб |
| 004 Step 1 - Building a Random Forest Model in R Regression Tutorial.mp4 |
18.29Мб |
| 004 Step 1 - Building a Random Forest Model in R Regression Tutorial.srt |
9.94Кб |
| 004 Step 1 - Getting Started with Hierarchical Clustering Data Setup in Python.mp4 |
18.38Мб |
| 004 Step 1 - Getting Started with Hierarchical Clustering Data Setup in Python.srt |
9.92Кб |
| 004 Step 1 in R - Understanding Principal Component Analysis for Feature Extraction.mp4 |
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| 004 Step 1 in R - Understanding Principal Component Analysis for Feature Extraction.srt |
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| 004 Step 1 - Machine Learning Basics Importing Datasets Using Pandas read_csv--(--).mp4 |
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| 004 Step 1 - Machine Learning Basics Importing Datasets Using Pandas read_csv--(--).srt |
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| 004 Step 1 Random Forest Classifier - From Template to Implementation in R.mp4 |
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| 004 Step 1 Random Forest Classifier - From Template to Implementation in R.srt |
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| 004 Step 1 - R Tutorial Creating a Decision Tree Classifier with rpart Library.mp4 |
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| 004 Step 1 - R Tutorial Creating a Decision Tree Classifier with rpart Library.srt |
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| 004 Step 2a Linear to Polynomial Regression - Preparing Data for Advanced Models.mp4 |
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| 004 Step 2a Linear to Polynomial Regression - Preparing Data for Advanced Models.srt |
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| 004 Step 2 - Implementing DecisionTreeRegressor A Step-by-Step Guide in Python.mp4 |
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| 004 Step 2 - Implementing DecisionTreeRegressor A Step-by-Step Guide in Python.srt |
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| 004 Step 2 Implementing UCB Algorithm in Python - Data Preparation.mp4 |
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| 004 Step 2 Implementing UCB Algorithm in Python - Data Preparation.srt |
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| 004 Step 2 - Optimizing Ad Selection with Thompson Sampling Algorithm in Python.mp4 |
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| 004 Step 2 - Optimizing Ad Selection with Thompson Sampling Algorithm in Python.srt |
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| 004 Step 2 - Optimizing Model Selection Streamlined Classification Code in Python.mp4 |
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| 004 Step 2 - Optimizing Model Selection Streamlined Classification Code in Python.srt |
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| 004 Step 3 - Configuring Apriori Function Support, Confidence, and Lift in Python.mp4 |
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| 004 Step 3 Evaluating Regression Models - R-Squared --& Performance Metrics Explained.mp4 |
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| 004 Step 3 Evaluating Regression Models - R-Squared --& Performance Metrics Explained.srt |
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| 004 Step 3 - Understanding Linear SVM Limitations Why It Didn--'t Beat kNN Classifier.mp4 |
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| 004 Step 3 - Understanding Linear SVM Limitations Why It Didn--'t Beat kNN Classifier.srt |
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| 004 Step 3 - Visualizing KNN Decision Boundaries Python Tutorial for Beginners.mp4 |
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| 004 Understanding Different Types of Kernel Functions for Machine Learning.mp4 |
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| 004 Why is Naive Bayes Called Naive Understanding the Algorithm--'s Assumptions.mp4 |
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| 005 Classification-Pros-Cons.pdf |
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| 005 Conclusion of Part 3 - Classification.html |
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| 005 Evaluating ML Model Accuracy K-Fold Cross-Validation Implementation in R.mp4 |
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| 005 Evaluating ML Model Accuracy K-Fold Cross-Validation Implementation in R.srt |
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| 005 Getting Started with R Programming Install R and RStudio on Windows --& Mac.mp4 |
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| 005 Getting Started with R Programming Install R and RStudio on Windows --& Mac.srt |
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| 005 How Do Neural Networks Learn Deep Learning Fundamentals Explained.mp4 |
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| 005 How Do Neural Networks Learn Deep Learning Fundamentals Explained.srt |
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| 005 Implementing Bag of Words in NLP A Step-by-Step Tutorial.mp4 |
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| 005 Implementing Bag of Words in NLP A Step-by-Step Tutorial.srt |
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| 005 Mastering Support Vector Regression Non-Linear SVR with RBF Kernel Explained.mp4 |
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| 005 Mastering Support Vector Regression Non-Linear SVR with RBF Kernel Explained.srt |
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| 005 Multicollinearity in Regression Understanding the Dummy Variable Trap.mp4 |
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| 005 Multicollinearity in Regression Understanding the Dummy Variable Trap.srt |
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| 005 Step 1a - Python K-Means Tutorial Identifying Customer Patterns in Mall Data.mp4 |
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| 005 Step 1 - Building a Linear SVM Classifier in R Data Import and Initial Setup.mp4 |
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| 005 Step 1 - Implementing KNN Classification in R Setup --& Data Preparation.mp4 |
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| 005 Step 1 - Naive Bayes in Python Applying ML to Social Network Ads Optimisation.mp4 |
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| 005 Step 1 - Naive Bayes in Python Applying ML to Social Network Ads Optimisation.srt |
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| 005 Step 2a - Building a Simple Linear Regression Model with Scikit-learn in Python.mp4 |
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| 005 Step 2a - Building a Simple Linear Regression Model with Scikit-learn in Python.srt |
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| 005 Step 2a - Implementing Hierarchical Clustering Building a Dendrogram with SciPy.mp4 |
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| 005 Step 2a - Implementing Hierarchical Clustering Building a Dendrogram with SciPy.srt |
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| 005 Step 2a - Mastering Feature Scaling for Support Vector Regression in Python.mp4 |
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| 005 Step 2a - Mastering Feature Scaling for Support Vector Regression in Python.srt |
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| 005 Step 2a Python Data Preprocessing for Logistic Regression Dataset Prep.mp4 |
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| 005 Step 2b - Transforming Linear to Polynomial Regression A Step-by-Step Guide.mp4 |
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| 005 Step 2b - Transforming Linear to Polynomial Regression A Step-by-Step Guide.srt |
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| 005 Step 2 - Decision Tree Classifier Optimizing Prediction Boundaries in R.mp4 |
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| 005 Step 2 - Decision Tree Classifier Optimizing Prediction Boundaries in R.srt |
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| 005 Step 2 - Max Pooling in CNNs Enhancing Spatial Invariance for Image Recognition.mp4 |
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| 005 Step 2 - Max Pooling in CNNs Enhancing Spatial Invariance for Image Recognition.srt |
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| 005 Step 2 Random Forest Classification - Visualizing Predictions --& Results.mp4 |
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| 005 Step 2 Random Forest Classification - Visualizing Predictions --& Results.srt |
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| 005 Step 2 - Using Pandas iloc for Feature Selection in ML Data Preprocessing.mp4 |
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| 005 Step 2 - Using preProcess Function in R for PCA Extracting Principal Components.mp4 |
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| 005 Step 2 - Using preProcess Function in R for PCA Extracting Principal Components.srt |
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| 005 Step 2 - Visualizing Random Forest Regression Interpreting Stairs and Splits.mp4 |
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| 005 Step 2 - Visualizing Random Forest Regression Interpreting Stairs and Splits.srt |
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| 005 Step 3 - Evaluating Classification Algorithms Accuracy Metrics in Python.mp4 |
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| 005 Step 3 - Evaluating Classification Algorithms Accuracy Metrics in Python.srt |
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| 005 Step 3 - Implementing Decision Tree Regression in Python Making Predictions.mp4 |
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| 005 Step 3 - Implementing Decision Tree Regression in Python Making Predictions.srt |
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| 005 Step 3 - Python Code for Thompson Sampling Maximizing Random Beta Distributions.mp4 |
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| 005 Step 3 - Python Code for Upper Confidence Bound Setting Up Key Variables.mp4 |
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| 005 Step 4 - Implementing R-Squared Score in Python with Scikit-Learn--'s Metrics.mp4 |
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| 005 Step 4 - Implementing R-Squared Score in Python with Scikit-Learn--'s Metrics.srt |
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| 005 Step 4 Visualizing Apriori Algorithm Results for Product Deals in Python.mp4 |
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| 005 Step 4 Visualizing Apriori Algorithm Results for Product Deals in Python.srt |
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| 005 SVM.zip |
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| 005 Using R--'s Factor Function to Handle Categorical Variables in Data Analysis.mp4 |
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| 005 Using R--'s Factor Function to Handle Categorical Variables in Data Analysis.srt |
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| 006 Deep Learning Fundamentals Gradient Descent vs Brute Force Optimization.mp4 |
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| 006 Deep Learning Fundamentals Gradient Descent vs Brute Force Optimization.srt |
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| 006 Evaluating Classiification Model Performance Quiz.html |
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| 006 EXTRA Use ChatGPT to Boost your ML Skills.html |
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| 006 Optimizing SVM Models with Grid Search A Step-by-Step R Tutorial.mp4 |
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| 006 Step 1b K-Means Clustering - Data Preparation in Google ColabJupyter.mp4 |
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| 006 Step 1 - Creating a Sparse Matrix for Association Rule Mining in R.mp4 |
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| 006 Step 1 - Getting Started with Natural Language Processing Sentiment Analysis.mp4 |
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| 006 Step 1 - How to Prepare Data for Machine Learning Training vs Test Sets.mp4 |
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| 006 Step 1 - Python Kernel SVM Applying RBF to Solve Non-Linear Classification.mp4 |
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| 006 Step 1 - Python Kernel SVM Applying RBF to Solve Non-Linear Classification.srt |
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| 006 Step 1 - Selecting the Best Regression Model R-squared Evaluation in Python.mp4 |
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| 006 Step 2b - Data Preprocessing Feature Scaling Techniques for Logistic Regression.mp4 |
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| 006 Step 2b - Data Preprocessing Feature Scaling Techniques for Logistic Regression.srt |
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| 006 Step 2b - Machine Learning Basics Training a Linear Regression Model in Python.mp4 |
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| 006 Step 2b Reshaping Data for SVR - Preparing Y Vector for Feature Scaling --(Python.mp4 |
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| 006 Step 2b Reshaping Data for SVR - Preparing Y Vector for Feature Scaling --(Python.srt |
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| 006 Step 2 - Building a KNN Classifier Preparing Training and Test Sets in R.mp4 |
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| 006 Step 2 - Building a KNN Classifier Preparing Training and Test Sets in R.srt |
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| 006 Step 2b - Visualizing Hierarchical Clustering Dendrogram Basics in Python.mp4 |
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| 006 Step 2 Creating --& Evaluating Linear SVM Classifier in R - Predictions --& Results.mp4 |
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| 006 Step 2 Creating --& Evaluating Linear SVM Classifier in R - Predictions --& Results.srt |
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| 006 Step 2 - Python Naive Bayes Training and Evaluating a Classifier on Real Data.mp4 |
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| 006 Step 3a - Plotting Real vs Predicted Salaries Linear Regression Visualization.mp4 |
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| 006 Step 3a - Plotting Real vs Predicted Salaries Linear Regression Visualization.srt |
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| 006 Step 3 - Decision Tree Visualization Exploring Splits and Conditions in R.mp4 |
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| 006 Step 3 - Decision Tree Visualization Exploring Splits and Conditions in R.srt |
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| 006 Step 3 - Evaluating Random Forest Performance Test Set Results --& Overfitting.mp4 |
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| 006 Step 3 - Evaluating Random Forest Performance Test Set Results --& Overfitting.srt |
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| 006 Step 3 - Fine-Tuning Random Forest From 10 to 500 Trees for Accurate Prediction.mp4 |
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| 006 Step 3 - Fine-Tuning Random Forest From 10 to 500 Trees for Accurate Prediction.srt |
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| 006 Step 3 - Implementing PCA and SVM for Customer Segmentation Practical Guide.mp4 |
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| 006 Step 3 - Implementing PCA and SVM for Customer Segmentation Practical Guide.srt |
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| 006 Step 3 - Preprocessing Data Building X and Y Vectors for ML Model Training.mp4 |
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| 006 Step 3 - Preprocessing Data Building X and Y Vectors for ML Model Training.srt |
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| 006 Step 3 - Understanding Flattening in Convolutional Neural Network Architecture.mp4 |
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| 006 Step 3 - Understanding Flattening in Convolutional Neural Network Architecture.srt |
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| 006 Step 4 - Beating UCB with Thompson Sampling Python Multi-Armed Bandit Tutorial.mp4 |
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| 006 Step 4 - Beating UCB with Thompson Sampling Python Multi-Armed Bandit Tutorial.srt |
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| 006 Step 4 - Model Selection Process Evaluating Classification Algorithms.mp4 |
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| 006 Step 4 - Python for RL Coding the UCB Algorithm Step-by-Step.mp4 |
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| 006 Step 4 - Python for RL Coding the UCB Algorithm Step-by-Step.srt |
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| 006 Step 4 - Visualizing Decision Tree Regression High-Resolution Results.mp4 |
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| 006 Step 4 - Visualizing Decision Tree Regression High-Resolution Results.srt |
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| 006 Understanding P-Values and Statistical Significance in Hypothesis Testing.mp4 |
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| 006 Understanding P-Values and Statistical Significance in Hypothesis Testing.srt |
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| 007 Additional Resource for this Section.html |
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| 007 Backward Elimination Building Robust Multiple Linear Regression Models.mp4 |
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| 007 Backward Elimination Building Robust Multiple Linear Regression Models.srt |
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| 007 Decision Tree Classification Quiz.html |
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| 007 For Python learners, summary of Object-oriented programming classes & objects.html |
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| 007 PCA Quiz.html |
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| 007 Random Forest Classification Quiz.html |
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| 007 Random Forest Regression Quiz.html |
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| 007 Step 1 - Creating a Decision Tree Regressor Using rpart Function in R.mp4 |
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| 007 Step 1 - Creating a Decision Tree Regressor Using rpart Function in R.srt |
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| 007 Step 2a - K-Means Clustering in Python Selecting Relevant Features for Analysis.mp4 |
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| 007 Step 2a - K-Means Clustering in Python Selecting Relevant Features for Analysis.srt |
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| 007 Step 2c - Interpreting Dendrograms Optimal Clusters in Hierarchical Clustering.mp4 |
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| 007 Step 2c - Interpreting Dendrograms Optimal Clusters in Hierarchical Clustering.srt |
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| 007 Step 2c SVR Data Prep - Scaling X --& Y Independently with StandardScaler.mp4 |
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| 007 Step 2c SVR Data Prep - Scaling X --& Y Independently with StandardScaler.srt |
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| 007 Step 2 - Importing TSV Data for Sentiment Analysis Python NLP Data Processing.mp4 |
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| 007 Step 2 - Importing TSV Data for Sentiment Analysis Python NLP Data Processing.srt |
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| 007 Step 2 - Mastering Kernel SVM Improving Accuracy with Non-Linear Classifiers.mp4 |
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| 007 Step 2 - Mastering Kernel SVM Improving Accuracy with Non-Linear Classifiers.srt |
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| 007 Step 2 - Optimizing Apriori Model Choosing Minimum Support and Confidence.mp4 |
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| 007 Step 2 - Optimizing Apriori Model Choosing Minimum Support and Confidence.srt |
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| 007 Step 2 - Preparing Data Creating Training and Test Sets in R for ML Models.mp4 |
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| 007 Step 2 - Preparing Data Creating Training and Test Sets in R for ML Models.srt |
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| 007 Step 2 - Selecting the Best Regression Model Random Forest vs. SVR Performance.mp4 |
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| 007 Step 2 - Selecting the Best Regression Model Random Forest vs. SVR Performance.srt |
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| 007 Step 3a - How to Import and Use LogisticRegression Class from Scikit-learn.mp4 |
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| 007 Step 3a - How to Import and Use LogisticRegression Class from Scikit-learn.srt |
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| 007 Step 3 - Analyzing Naive Bayes Algorithm Results Accuracy and Predictions.mp4 |
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| 007 Step 3 - Analyzing Naive Bayes Algorithm Results Accuracy and Predictions.srt |
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| 007 Step 3b - Polynomial vs Linear Regression Better Fit with Higher Degrees.mp4 |
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| 007 Step 3b - Polynomial vs Linear Regression Better Fit with Higher Degrees.srt |
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| 007 Step 3 - Implementing KNN Classification in R Adapting the Classifier Template.mp4 |
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| 007 Step 3 - Implementing KNN Classification in R Adapting the Classifier Template.srt |
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| 007 Step 3 - Using Scikit-Learn--'s Predict Method for Linear Regression in Python.mp4 |
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| 007 Step 3 - Using Scikit-Learn--'s Predict Method for Linear Regression in Python.srt |
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| 007 Step 4 - Fully Connected Layers in CNNs Optimizing Feature Combination.mp4 |
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| 007 Step 4 - Fully Connected Layers in CNNs Optimizing Feature Combination.srt |
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| 007 Step 5 - Coding Upper Confidence Bound Optimizing Ad Selection in Python.mp4 |
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| 007 Step 5 - Coding Upper Confidence Bound Optimizing Ad Selection in Python.srt |
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| 007 Stochastic vs Batch Gradient Descent Deep Learning Fundamentals.mp4 |
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| 007 Stochastic vs Batch Gradient Descent Deep Learning Fundamentals.srt |
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| 007 SVM Quiz.html |
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| 008 Coding Exercise 1 Importing and Preprocessing a Dataset for Machine Learning.html |
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| 008 Conclusion of Part 2 - Regression.html |
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| 008 Deep Learning Basics How Convolutional Neural Networks --(CNNs--) Process Images.mp4 |
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| 008 Deep Learning Basics How Convolutional Neural Networks --(CNNs--) Process Images.srt |
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| 008 Deep Learning Fundamentals Training Neural Networks Step-by-Step.mp4 |
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| 008 Deep Learning Fundamentals Training Neural Networks Step-by-Step.srt |
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| 008 Feature Scaling in ML Step 1 Why It--'s Crucial for Data Preprocessing.mp4 |
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| 008 Feature Scaling in ML Step 1 Why It--'s Crucial for Data Preprocessing.srt |
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| 008 K-Nearest Neighbor Quiz.html |
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| 008 Regression-Bonus.zip |
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| 008 Step 1a - Hands-On Data Preprocessing for Multiple Linear Regression in Python.mp4 |
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| 008 Step 1 - Getting Started with Naive Bayes Algorithm in R for Classification.mp4 |
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| 008 Step 1 - Getting Started with Naive Bayes Algorithm in R for Classification.srt |
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| 008 Step 1 - Kernel SVM vs Linear SVM Overcoming Non-Linear Separability in R.mp4 |
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| 008 Step 1 - Kernel SVM vs Linear SVM Overcoming Non-Linear Separability in R.srt |
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| 008 Step 1 - Thompson Sampling vs UCB Optimizing Ad Click-Through Rates in R.mp4 |
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| 008 Step 1 - Thompson Sampling vs UCB Optimizing Ad Click-Through Rates in R.srt |
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| 008 Step 2b K-Means Clustering - Optimizing Features for 2D Visualization.mp4 |
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| 008 Step 2b K-Means Clustering - Optimizing Features for 2D Visualization.srt |
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| 008 Step 2 - Decision Tree Regression Fixing Splits with rpart Control Parameter.mp4 |
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| 008 Step 2 - Decision Tree Regression Fixing Splits with rpart Control Parameter.srt |
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| 008 Step 3a - Building a Hierarchical Clustering Model with Scikit-learn in Python.mp4 |
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| 008 Step 3a - Building a Hierarchical Clustering Model with Scikit-learn in Python.srt |
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| 008 Step 3b - Training Logistic Regression Model Fit Method for Classification.mp4 |
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| 008 Step 3b - Training Logistic Regression Model Fit Method for Classification.srt |
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| 008 Step 3 Optimizing Product Placement - Apriori Algorithm, Lift --& Confidence.mp4 |
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| 008 Step 3 Optimizing Product Placement - Apriori Algorithm, Lift --& Confidence.srt |
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| 008 Step 3 SVM Regression Creating --& Training SVR Model with RBF Kernel in Python.mp4 |
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| 008 Step 3 SVM Regression Creating --& Training SVR Model with RBF Kernel in Python.srt |
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| 008 Step 3 - Text Cleaning for NLP Remove Punctuation and Convert to Lowercase.mp4 |
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| 008 Step 3 - Text Cleaning for NLP Remove Punctuation and Convert to Lowercase.srt |
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| 008 Step 4a - Linear Regression Plotting Real vs Predicted Salaries Visualization.mp4 |
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| 008 Step 4a - Linear Regression Plotting Real vs Predicted Salaries Visualization.srt |
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| 008 Step 4a Predicting Salaries - Linear Regression in Python --(Array Input Guide--).mp4 |
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| 008 Step 6 - Reinforcement Learning Finalizing UCB Algorithm in Python.mp4 |
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| 008 Step 6 - Reinforcement Learning Finalizing UCB Algorithm in Python.srt |
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| 009 Apriori Quiz.html |
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| 009 Bank Customer Churn Prediction Machine Learning Model with TensorFlow.mp4 |
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| 009 Bank Customer Churn Prediction Machine Learning Model with TensorFlow.srt |
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| 009 Deep Learning Essentials Understanding Softmax and Cross-Entropy in CNNs.mp4 |
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| 009 Deep Learning Essentials Understanding Softmax and Cross-Entropy in CNNs.srt |
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| 009 How to Scale Numeric Features in R for Machine Learning Preprocessing - Step 2.mp4 |
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| 009 How to Scale Numeric Features in R for Machine Learning Preprocessing - Step 2.srt |
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| 009 Step 1b - Hands-On Guide Implementing Multiple Linear Regression in Python.mp4 |
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| 009 Step 1 - Using Scikit-Learn to Replace Missing Values in Machine Learning.mp4 |
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| 009 Step 1 - Using Scikit-Learn to Replace Missing Values in Machine Learning.srt |
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| 009 Step 2 - Building a Gaussian Kernel SVM Classifier for Advanced Machine Learning.mp4 |
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| 009 Step 2 - Building a Gaussian Kernel SVM Classifier for Advanced Machine Learning.srt |
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| 009 Step 2 - Reinforcement Learning Thompson Sampling Outperforms UCB Algorithm.mp4 |
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| 009 Step 2 - Troubleshooting Naive Bayes Classification Empty Prediction Vectors.mp4 |
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| 009 Step 3a - Implementing the Elbow Method for K-Means Clustering in Python.mp4 |
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| 009 Step 3b - Comparing 3 vs 5 Clusters in Hierarchical Clustering Python Example.mp4 |
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| 009 Step 3 Non-Continuous Regression - Decision Tree Visualization Challenges.mp4 |
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| 009 Step 3 Non-Continuous Regression - Decision Tree Visualization Challenges.srt |
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| 009 Step 4a - Formatting Single Observation Input for Logistic Regression Predict.mp4 |
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| 009 Step 4a - Formatting Single Observation Input for Logistic Regression Predict.srt |
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| 009 Step 4b - Evaluating Linear Regression Model Performance on Test Data.mp4 |
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| 009 Step 4b - Evaluating Linear Regression Model Performance on Test Data.srt |
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| 009 Step 4b Python Polynomial Regression - Predicting Salaries Accurately.mp4 |
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| 009 Step 4b Python Polynomial Regression - Predicting Salaries Accurately.srt |
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| 009 Step 4 - SVR Model Prediction Handling Scaled Data and Inverse Transformation.mp4 |
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| 009 Step 4 - SVR Model Prediction Handling Scaled Data and Inverse Transformation.srt |
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| 009 Step 4 - Text Preprocessing Stemming and Stop Word Removal for NLP in Python.mp4 |
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| 009 Step 4 - Text Preprocessing Stemming and Stop Word Removal for NLP in Python.srt |
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| 009 Step 7 - Visualizing UCB Algorithm Results Histogram Analysis in Python.mp4 |
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| 009 Step 7 - Visualizing UCB Algorithm Results Histogram Analysis in Python.srt |
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| 010 dataset.zip |
221.65Мб |
| 010 Essential Steps in Data Preprocessing Preparing Your Dataset for ML Models.mp4 |
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| 010 Essential Steps in Data Preprocessing Preparing Your Dataset for ML Models.srt |
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| 010 Make sure you have your dataset ready.html |
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| 010 Simple Linear Regression in Python - Additional Lecture.html |
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| 010 Step 1a - Implementing Polynomial Regression in R HR Salary Analysis Case Study.mp4 |
11.54Мб |
| 010 Step 1a - Implementing Polynomial Regression in R HR Salary Analysis Case Study.srt |
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| 010 Step 1 ANN in Python Predicting Customer Churn with TensorFlow.mp4 |
31.85Мб |
| 010 Step 1 ANN in Python Predicting Customer Churn with TensorFlow.srt |
17.85Кб |
| 010 Step 1 - Exploring Upper Confidence Bound in R Multi-Armed Bandit Problems.mp4 |
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| 010 Step 1 - Exploring Upper Confidence Bound in R Multi-Armed Bandit Problems.srt |
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| 010 Step 1 - R Data Import for Clustering Annual Income --& Spending Score Analysis.mp4 |
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| 010 Step 1 - R Data Import for Clustering Annual Income --& Spending Score Analysis.srt |
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| 010 Step 2a - Hands-on Multiple Linear Regression Preparing Data in Python.mp4 |
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| 010 Step 2a - Hands-on Multiple Linear Regression Preparing Data in Python.srt |
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| 010 Step 2 - Imputing Missing Data in Python SimpleImputer and Numerical Columns.mp4 |
18.41Мб |
| 010 Step 2 - Imputing Missing Data in Python SimpleImputer and Numerical Columns.srt |
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| 010 Step 3b - Optimizing K-means Clustering WCSS and Elbow Method Implementation.mp4 |
17.60Мб |
| 010 Step 3b - Optimizing K-means Clustering WCSS and Elbow Method Implementation.srt |
9.63Кб |
| 010 Step 3 Visualizing Kernel SVM - Non-Linear Classification in Machine Learning.mp4 |
15.94Мб |
| 010 Step 3 Visualizing Kernel SVM - Non-Linear Classification in Machine Learning.srt |
8.77Кб |
| 010 Step 3 - Visualizing Naive Bayes Results Creating Confusion Matrix and Graphs.mp4 |
11.34Мб |
| 010 Step 3 - Visualizing Naive Bayes Results Creating Confusion Matrix and Graphs.srt |
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| 010 Step 4b Predicted vs. Real Purchase Decisions in Logistic Regression.mp4 |
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| 010 Step 4b Predicted vs. Real Purchase Decisions in Logistic Regression.srt |
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| 010 Step 4 - Visualizing Decision Tree Understanding Intervals and Predictions.mp4 |
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| 010 Step 4 - Visualizing Decision Tree Understanding Intervals and Predictions.srt |
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| 010 Step 5a - How to Plot Support Vector Regression --(SVR--) Models Step-by-Step Guide.mp4 |
11.43Мб |
| 010 Step 5a - How to Plot Support Vector Regression --(SVR--) Models Step-by-Step Guide.srt |
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| 010 Step 5 - Tokenization and Feature Extraction for Naive Bayes Sentiment Analysis.mp4 |
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| 010 Step 5 - Tokenization and Feature Extraction for Naive Bayes Sentiment Analysis.srt |
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| 010 Thompson Sampling Quiz.html |
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| 011 Coding Exercise 2 Handling Missing Data in a Dataset for Machine Learning.html |
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| 011 Data Preprocessing Quiz.html |
20.93Кб |
| 011 Decision Tree Regression Quiz.html |
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| 011 Kernel SVM Quiz.html |
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| 011 Naive Bayes Quiz.html |
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| 011 Step 1b - ML Fundamentals Preparing Data for Polynomial Regression.mp4 |
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| 011 Step 1b - ML Fundamentals Preparing Data for Polynomial Regression.srt |
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| 011 Step 1 - Data Preprocessing in R Preparing for Linear Regression Modeling.mp4 |
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| 011 Step 1 - Data Preprocessing in R Preparing for Linear Regression Modeling.srt |
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| 011 Step 1 Intro to CNNs for Image Classification.mp4 |
35.66Мб |
| 011 Step 1 Intro to CNNs for Image Classification.srt |
19.19Кб |
| 011 Step 2b - Multiple Linear Regression in Python Preparing Your Dataset.mp4 |
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| 011 Step 2b - Multiple Linear Regression in Python Preparing Your Dataset.srt |
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| 011 Step 2 - TensorFlow 2.0 Tutorial Preprocessing Data for Customer Churn Model.mp4 |
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| 011 Step 2 - TensorFlow 2.0 Tutorial Preprocessing Data for Customer Churn Model.srt |
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| 011 Step 2 - UCB Algorithm in R Calculating Average Reward --& Confidence Interval.mp4 |
49.32Мб |
| 011 Step 2 - UCB Algorithm in R Calculating Average Reward --& Confidence Interval.srt |
30.38Кб |
| 011 Step 2 Using H.clust in R - Building --& Interpreting Dendrograms for Clustering.mp4 |
16.21Мб |
| 011 Step 2 Using H.clust in R - Building --& Interpreting Dendrograms for Clustering.srt |
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| 011 Step 3c - Plotting the Elbow Method Graph for K-Means Clustering in Python.mp4 |
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| 011 Step 3c - Plotting the Elbow Method Graph for K-Means Clustering in Python.srt |
6.34Кб |
| 011 Step 5b - SVR Scaling --& Inverse Transformation in Python.mp4 |
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| 011 Step 5b - SVR Scaling --& Inverse Transformation in Python.srt |
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| 011 Step 5 - Comparing Predicted vs Real Results Python Logistic Regression Guide.mp4 |
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| 011 Step 5 - Comparing Predicted vs Real Results Python Logistic Regression Guide.srt |
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| 011 Step 6 - Training and Evaluating a Naive Bayes Classifier for Sentiment Analysis.mp4 |
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| 012 Natural Language Processing in Python - EXTRA.html |
3.33Кб |
| 012 Step 1 - One-Hot Encoding Transforming Categorical Features for ML Algorithms.mp4 |
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| 012 Step 1 - One-Hot Encoding Transforming Categorical Features for ML Algorithms.srt |
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| 012 Step 1 - SVR Tutorial Creating a Support Vector Machine Regressor in R.mp4 |
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| 012 Step 1 - SVR Tutorial Creating a Support Vector Machine Regressor in R.srt |
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| 012 Step 2a - Building Linear --& Polynomial Regression Models in R A Comparison.mp4 |
14.97Мб |
| 012 Step 2a - Building Linear --& Polynomial Regression Models in R A Comparison.srt |
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| 012 Step 2 - Fitting Simple Linear Regression in R LM Function and Model Summary.mp4 |
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| 012 Step 2 - Fitting Simple Linear Regression in R LM Function and Model Summary.srt |
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| 012 Step 2 - Keras ImageDataGenerator Prevent Overfitting in CNN Models.mp4 |
54.71Мб |
| 012 Step 2 - Keras ImageDataGenerator Prevent Overfitting in CNN Models.srt |
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| 012 Step 3a - Scikit-learn for Multiple Linear Regression Efficient Model Building.mp4 |
18.09Мб |
| 012 Step 3a - Scikit-learn for Multiple Linear Regression Efficient Model Building.srt |
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| 012 Step 3 - Designing ANN Sequential Model --& Dense Layers for Deep Learning.mp4 |
44.50Мб |
| 012 Step 3 - Designing ANN Sequential Model --& Dense Layers for Deep Learning.srt |
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| 012 Step 3 - Implementing Hierarchical Clustering Using Cat Tree Method in R.mp4 |
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| 012 Step 3 - Implementing Hierarchical Clustering Using Cat Tree Method in R.srt |
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| 012 Step 3 Optimizing Ad Selection - UCB --& Multi-Armed Bandit Algorithm Explained.mp4 |
55.68Мб |
| 012 Step 3 Optimizing Ad Selection - UCB --& Multi-Armed Bandit Algorithm Explained.srt |
28.74Кб |
| 012 Step 4 - Creating a Dependent Variable from K-Means Clustering Results in Python.mp4 |
18.45Мб |
| 012 Step 4 - Creating a Dependent Variable from K-Means Clustering Results in Python.srt |
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| 012 Step 6a - Implementing Confusion Matrix and Accuracy Score in Scikit-Learn.mp4 |
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| 012 Step 6a - Implementing Confusion Matrix and Accuracy Score in Scikit-Learn.srt |
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| 013 Homework Challenge.html |
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| 013 Step 2b - Building a Polynomial Regression Model Adding Squared --& Cubed Terms.mp4 |
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| 013 Step 2b - Building a Polynomial Regression Model Adding Squared --& Cubed Terms.srt |
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| 013 Step 2 - Handling Categorical Data One-Hot Encoding with ColumnTransformer.mp4 |
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| 013 Step 2 - Handling Categorical Data One-Hot Encoding with ColumnTransformer.srt |
10.06Кб |
| 013 Step 2 - Support Vector Regression Building a Predictive Model in Python.mp4 |
12.16Мб |
| 013 Step 2 - Support Vector Regression Building a Predictive Model in Python.srt |
8.62Кб |
| 013 Step 3b - Scikit-Learn Building --& Training Multiple Linear Regression Models.mp4 |
14.20Мб |
| 013 Step 3b - Scikit-Learn Building --& Training Multiple Linear Regression Models.srt |
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| 013 Step 3 - How to Use predict--(--) Function in R for Linear Regression Analysis.mp4 |
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| 013 Step 3 - How to Use predict--(--) Function in R for Linear Regression Analysis.srt |
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| 013 Step 3 - TensorFlow CNN Convolution to Output Layer for Vision Tasks.mp4 |
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| 013 Step 3 - TensorFlow CNN Convolution to Output Layer for Vision Tasks.srt |
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| 013 Step 4 - Cluster Plot Method Visualizing Hierarchical Clustering Results in R.mp4 |
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| 013 Step 4 - Cluster Plot Method Visualizing Hierarchical Clustering Results in R.srt |
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| 013 Step 4 - Train Neural Network Compile --& Fit for Customer Churn Prediction.mp4 |
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| 013 Step 4 - Train Neural Network Compile --& Fit for Customer Churn Prediction.srt |
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| 013 Step 4 - UCB Algorithm Performance Analyzing Ad Selection with Histograms.mp4 |
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| 013 Step 4 - UCB Algorithm Performance Analyzing Ad Selection with Histograms.srt |
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| 013 Step 5a Visualizing K-Means Clusters of Customer Data with Python Scatter.mp4 |
18.39Мб |
| 013 Step 5a Visualizing K-Means Clusters of Customer Data with Python Scatter.srt |
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| 013 Step 6b Evaluating Classification Models - Confusion Matrix --& Accuracy Metrics.mp4 |
10.61Мб |
| 013 Step 6b Evaluating Classification Models - Confusion Matrix --& Accuracy Metrics.srt |
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| 014 Step 1 - Text Classification Using Bag-of-Words and Random Forest in R.mp4 |
51.09Мб |
| 014 Step 1 - Text Classification Using Bag-of-Words and Random Forest in R.srt |
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| 014 Step 3a Visualizing Regression Results - Creating Scatter Plots with ggplot2 in.mp4 |
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| 014 Step 3a Visualizing Regression Results - Creating Scatter Plots with ggplot2 in.srt |
8.09Кб |
| 014 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.mp4 |
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| 014 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.srt |
7.70Кб |
| 014 Step 4a Comparing Real vs Predicted Profits in Linear Regression - Hands-on Gui.mp4 |
17.40Мб |
| 014 Step 4a Comparing Real vs Predicted Profits in Linear Regression - Hands-on Gui.srt |
9.85Кб |
| 014 Step 4a - Plotting Linear Regression Data in R ggplot2 Step-by-Step Guide.mp4 |
18.18Мб |
| 014 Step 4a - Plotting Linear Regression Data in R ggplot2 Step-by-Step Guide.srt |
9.50Кб |
| 014 Step 4 CNN Training - Epochs, Loss Function --& Metrics in TensorFlow.mp4 |
22.76Мб |
| 014 Step 4 CNN Training - Epochs, Loss Function --& Metrics in TensorFlow.srt |
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| 014 Step 5b - Visualizing K-Means Clusters Plotting Customer Segments in Python.mp4 |
15.22Мб |
| 014 Step 5b - Visualizing K-Means Clusters Plotting Customer Segments in Python.srt |
8.14Кб |
| 014 Step 5 - Hierarchical Clustering in R Understanding Customer Spending Patterns.mp4 |
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| 014 Step 5 - Hierarchical Clustering in R Understanding Customer Spending Patterns.srt |
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| 014 Step 5 - Implementing ANN for Churn Prediction From Model to Confusion Matrix.mp4 |
50.54Мб |
| 014 Step 5 - Implementing ANN for Churn Prediction From Model to Confusion Matrix.srt |
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| 014 Step 7a - Visualizing Logistic Regression Decision Boundaries in Python 2D Plot.mp4 |
18.16Мб |
| 014 Step 7a - Visualizing Logistic Regression Decision Boundaries in Python 2D Plot.srt |
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| 014 SVR Quiz.html |
20.42Кб |
| 014 Upper Confidence Bound Quiz.html |
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| 015 Coding Exercise 3 Encoding Categorical Data for Machine Learning.html |
22.73Кб |
| 015 Hierarchical Clustering Quiz.html |
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| 015 Step 1 - How to Preprocess Data for Artificial Neural Networks in R.mp4 |
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| 015 Step 1 - How to Preprocess Data for Artificial Neural Networks in R.srt |
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| 015 Step 3b Visualizing Linear Regression - Plotting Predictions vs Observations.mp4 |
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| 015 Step 3b Visualizing Linear Regression - Plotting Predictions vs Observations.srt |
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| 015 Step 4b - Creating a Scatter Plot with Regression Line in R Using ggplot2.mp4 |
17.02Мб |
| 015 Step 4b - Creating a Scatter Plot with Regression Line in R Using ggplot2.srt |
8.51Кб |
| 015 Step 4b - ML in Python Evaluating Multiple Linear Regression Accuracy.mp4 |
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| 015 Step 4b - ML in Python Evaluating Multiple Linear Regression Accuracy.srt |
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| 015 Step 5c - Analyzing Customer Segments Insights from K-means Clustering.mp4 |
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| 015 Step 5c - Analyzing Customer Segments Insights from K-means Clustering.srt |
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| 015 Step 5 - Making Single Predictions with Convolutional Neural Networks in Python.mp4 |
45.99Мб |
| 015 Step 5 - Making Single Predictions with Convolutional Neural Networks in Python.srt |
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| 015 Step 7b - Interpreting Logistic Regression Results Prediction Regions Explained.mp4 |
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| 015 Step 7b - Interpreting Logistic Regression Results Prediction Regions Explained.srt |
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| 015 Warning - Update.html |
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| 016 Clustering-Pros-Cons.pdf |
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| 016 Conclusion of Part 4 - Clustering.html |
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| 016 Hands-on CNN Training Using Jupyter Notebook for Image Classification.mp4 |
72.81Мб |
| 016 Hands-on CNN Training Using Jupyter Notebook for Image Classification.srt |
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| 016 Multiple Linear Regression in Python - Backward Elimination.html |
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| 016 Step 1 - How to Prepare Data for Machine Learning Training vs Test Sets.mp4 |
12.06Мб |
| 016 Step 1 - How to Prepare Data for Machine Learning Training vs Test Sets.srt |
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| 016 Step 1 - K-Means Clustering in R Importing --& Exploring Segmentation Data.mp4 |
18.44Мб |
| 016 Step 1 - K-Means Clustering in R Importing --& Exploring Segmentation Data.srt |
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| 016 Step 2 - How to Install and Initialize H2O for Efficient Deep Learning in R.mp4 |
20.14Мб |
| 016 Step 2 - How to Install and Initialize H2O for Efficient Deep Learning in R.srt |
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| 016 Step 2 - NLP Data Preprocessing in R Importing TSV Files for Sentiment Analysis.mp4 |
26.70Мб |
| 016 Step 2 - NLP Data Preprocessing in R Importing TSV Files for Sentiment Analysis.srt |
14.83Кб |
| 016 Step 3c - Polynomial Regression Curve Fitting for Better Predictions.mp4 |
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| 016 Step 3c - Polynomial Regression Curve Fitting for Better Predictions.srt |
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| 016 Step 4c - Comparing Training vs Test Set Predictions in Linear Regression.mp4 |
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| 016 Step 4c - Comparing Training vs Test Set Predictions in Linear Regression.srt |
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| 016 Step 7c - Visualizing Logistic Regression Performance on New Data in Python.mp4 |
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| 016 Step 7c - Visualizing Logistic Regression Performance on New Data in Python.srt |
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| 017 Deep Learning Additional Content #2.html |
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| 017 Logistic Regression in Python - Step 7 (Colour-blind friendly image).html |
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| 017 Multiple Linear Regression in Python - EXTRA CONTENT.html |
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| 017 Simple Linear Regression Quiz.html |
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| 017 Step 2 - K-Means Algorithm Implementation in R Fitting and Analyzing Mall Data.mp4 |
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| 017 Step 2 - K-Means Algorithm Implementation in R Fitting and Analyzing Mall Data.srt |
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| 017 Step 2 - Preparing Data Creating Training and Test Sets in Python for ML Models.mp4 |
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| 017 Step 2 - Preparing Data Creating Training and Test Sets in Python for ML Models.srt |
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| 017 Step 3 Building Deep Learning Model - H2O Neural Network Layer Config.mp4 |
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| 017 Step 3 Building Deep Learning Model - H2O Neural Network Layer Config.srt |
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| 017 Step 3 - NLP in R Initialising a Corpus for Sentiment Analysis.mp4 |
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| 017 Step 3 - NLP in R Initialising a Corpus for Sentiment Analysis.srt |
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| 017 Step 4a - How to Make Single Predictions Using Polynomial Regression in R.mp4 |
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| 017 Step 4a - How to Make Single Predictions Using Polynomial Regression in R.srt |
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| 018 CNN Quiz.html |
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| 018 K-Means Clustering Quiz.html |
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| 018 Step 1a - Data Preprocessing for MLR Handling Categorical Data.mp4 |
12.01Мб |
| 018 Step 1a - Data Preprocessing for MLR Handling Categorical Data.srt |
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| 018 Step 1 - Data Preprocessing for Logistic Regression in R Preparing Your Dataset.mp4 |
18.59Мб |
| 018 Step 1 - Data Preprocessing for Logistic Regression in R Preparing Your Dataset.srt |
9.91Кб |
| 018 Step 3 - Splitting Data into Training and Test Sets Best Practices in Python.mp4 |
11.95Мб |
| 018 Step 3 - Splitting Data into Training and Test Sets Best Practices in Python.srt |
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| 018 Step 4b - Predicting Salaries with Polynomial Regression A Practical Example.mp4 |
11.68Мб |
| 018 Step 4b - Predicting Salaries with Polynomial Regression A Practical Example.srt |
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| 018 Step 4 - H2O Deep Learning Making Predictions and Evaluating Model Accuracy.mp4 |
45.35Мб |
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| 018 Step 4 - NLP Data Cleaning Lowercase Transformation in R for Text Analysis.mp4 |
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| 018 Step 4 - NLP Data Cleaning Lowercase Transformation in R for Text Analysis.srt |
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| 019 Coding Exercise 4 Dataset Splitting and Feature Scaling.html |
10.38Кб |
| 019 Deep Learning Additional Content.html |
3.19Кб |
| 019 Step 1b - Preparing Datasets for Multiple Linear Regression in R.mp4 |
12.31Мб |
| 019 Step 1b - Preparing Datasets for Multiple Linear Regression in R.srt |
6.43Кб |
| 019 Step 1 - Building a Reusable Framework for Nonlinear Regression Analysis in R.mp4 |
18.35Мб |
| 019 Step 1 - Building a Reusable Framework for Nonlinear Regression Analysis in R.srt |
10.06Кб |
| 019 Step 2 - How to Create a Logistic Regression Classifier Using R--'s GLM Function.mp4 |
10.02Мб |
| 019 Step 2 - How to Create a Logistic Regression Classifier Using R--'s GLM Function.srt |
5.03Кб |
| 019 Step 5 - Sentiment Analysis Data Cleaning Removing Numbers with TM Map.mp4 |
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| 019 Step 5 - Sentiment Analysis Data Cleaning Removing Numbers with TM Map.srt |
3.64Кб |
| 020 EXTRA CONTENT ANN Case Study.html |
2.73Кб |
| 020 Step 1 - Feature Scaling in ML Why It--'s Crucial for Data Preprocessing.mp4 |
18.34Мб |
| 020 Step 1 - Feature Scaling in ML Why It--'s Crucial for Data Preprocessing.srt |
10.06Кб |
| 020 Step 2a - Multiple Linear Regression in R Building --& Interpreting the Regressor.mp4 |
16.92Мб |
| 020 Step 2a - Multiple Linear Regression in R Building --& Interpreting the Regressor.srt |
8.91Кб |
| 020 Step 2 - Mastering Regression Model Visualization Increasing Data Resolution.mp4 |
16.77Мб |
| 020 Step 2 - Mastering Regression Model Visualization Increasing Data Resolution.srt |
9.21Кб |
| 020 Step 3 - How to Use R for Logistic Regression Prediction Step-by-Step Guide.mp4 |
17.46Мб |
| 020 Step 3 - How to Use R for Logistic Regression Prediction Step-by-Step Guide.srt |
8.37Кб |
| 020 Step 6 - Cleaning Text Data Removing Punctuation for NLP and Classification.mp4 |
18.05Мб |
| 020 Step 6 - Cleaning Text Data Removing Punctuation for NLP and Classification.srt |
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| 021 ANN QUIZ.html |
20.16Кб |
| 021 Polynomial Regression Quiz.html |
20.48Кб |
| 021 Step 2b Statistical Significance - P-values --& Stars in Regression.mp4 |
13.42Мб |
| 021 Step 2b Statistical Significance - P-values --& Stars in Regression.srt |
6.82Кб |
| 021 Step 2 - How to Scale Numeric Features in Python for ML Preprocessing.mp4 |
14.64Мб |
| 021 Step 2 - How to Scale Numeric Features in Python for ML Preprocessing.srt |
7.90Кб |
| 021 Step 4 - How to Assess Model Accuracy Using a Confusion Matrix in R.mp4 |
8.70Мб |
| 021 Step 4 - How to Assess Model Accuracy Using a Confusion Matrix in R.srt |
4.34Кб |
| 021 Step 7 - Simplifying Corpus Using SnowballC Package to Remove Stop Words in R.mp4 |
10.63Мб |
| 021 Step 7 - Simplifying Corpus Using SnowballC Package to Remove Stop Words in R.srt |
5.83Кб |
| 022 Step 3 - How to Use predict--(--) Function in R for Multiple Linear Regression.mp4 |
13.97Мб |
| 022 Step 3 - How to Use predict--(--) Function in R for Multiple Linear Regression.srt |
7.19Кб |
| 022 Step 3 - Implementing Feature Scaling Fit and Transform Methods Explained.mp4 |
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| 022 Step 3 - Implementing Feature Scaling Fit and Transform Methods Explained.srt |
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| 022 Step 8 - Enhancing Text Classification Stemming for Efficient Feature Matrices.mp4 |
16.51Мб |
| 022 Step 8 - Enhancing Text Classification Stemming for Efficient Feature Matrices.srt |
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| 022 Warning - Update.html |
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| 023 Optimizing Multiple Regression Models Backward Elimination Technique in R.mp4 |
54.96Мб |
| 023 Optimizing Multiple Regression Models Backward Elimination Technique in R.srt |
29.83Кб |
| 023 Step 4 - Applying the Same Scaler to Training and Test Sets in Python.mp4 |
18.26Мб |
| 023 Step 4 - Applying the Same Scaler to Training and Test Sets in Python.srt |
10.03Кб |
| 023 Step 5a - Interpreting Logistic Regression Plots Prediction Regions Explained.mp4 |
18.28Мб |
| 023 Step 5a - Interpreting Logistic Regression Plots Prediction Regions Explained.srt |
9.52Кб |
| 023 Step 9 Removing Extra Spaces for NLP Sentiment Analysis Text Cleaning.mp4 |
40.39Мб |
| 023 Step 9 Removing Extra Spaces for NLP Sentiment Analysis Text Cleaning.srt |
22.44Кб |
| 024 Coding exercise 5 Feature scaling for Machine Learning.html |
91.81Кб |
| 024 Mastering Feature Selection Backward Elimination in R for Linear Regression.mp4 |
23.39Мб |
| 024 Mastering Feature Selection Backward Elimination in R for Linear Regression.srt |
14.55Кб |
| 024 Step 10 - Building a Document-Term Matrix for NLP Text Classification.mp4 |
54.87Мб |
| 024 Step 10 - Building a Document-Term Matrix for NLP Text Classification.srt |
30.43Кб |
| 024 Step 5b Logistic Regression - Linear Classifiers --& Prediction Boundaries.mp4 |
18.76Мб |
| 024 Step 5b Logistic Regression - Linear Classifiers --& Prediction Boundaries.srt |
9.88Кб |
| 025 Homework Challenge.html |
3.65Кб |
| 025 Multiple Linear Regression in R - Automatic Backward Elimination.html |
3.00Кб |
| 025 Step 5c - Data Viz in R Colorizing Pixels for Logistic Regression.mp4 |
16.28Мб |
| 025 Step 5c - Data Viz in R Colorizing Pixels for Logistic Regression.srt |
8.18Кб |
| 026 Logistic Regression in R - Step 5 (Colour-blind friendly image).html |
2.96Кб |
| 026 Multiple Linear Regression Quiz.html |
20.64Кб |
| 026 Natural Language Processing Quiz.html |
20.15Кб |
| 027 Optimizing R Scripts for Machine Learning Building a Classification Template.mp4 |
16.55Мб |
| 027 Optimizing R Scripts for Machine Learning Building a Classification Template.srt |
9.26Кб |
| 028 Machine Learning Regression and Classification EXTRA.html |
3.04Кб |
| 029 Logistic Regression Quiz.html |
20.51Кб |
| 030 EXTRA CONTENT Logistic Regression Practical Case Study.html |
2.85Кб |
| external-links.txt |
70б |