[GigaCourse.Com] Udemy - Machine Learning A-Z™ AI, Python & R + ChatGPT Bonus [2023]

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[GigaCourse.Com] Udemy - Machine Learning A-Z™ AI, Python & R + ChatGPT Bonus [2023]

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最近下载：2024-11-09

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文件列表

  • 41 - Kernel PCA/002 Kernel PCA in R.mp4 239.9 MB
  • 37 - Convolutional Neural Networks/001 dataset.zip 232.0 MB
  • 29 - Apriori/008 Apriori in R - Step 3.mp4 169.5 MB
  • 20 - Naive Bayes/001 Bayes Theorem.mp4 152.7 MB
  • 36 - Artificial Neural Networks/015 ANN in R - Step 1.mp4 139.2 MB
  • 29 - Apriori/005 Apriori in Python - Step 4.mp4 122.4 MB
  • 36 - Artificial Neural Networks/017 ANN in R - Step 3.mp4 121.3 MB
  • 43 - Model Selection/002 Grid Search in Python.mp4 120.0 MB
  • 37 - Convolutional Neural Networks/015 CNN in Python - FINAL DEMO!.mp4 117.5 MB
  • 35 - -------------------- Part 8 Deep Learning --------------------/002 What is Deep Learning.mp4 107.9 MB
  • 39 - Principal Component Analysis (PCA)/004 PCA in R - Step 1.mp4 105.5 MB
  • 37 - Convolutional Neural Networks/011 CNN in Python - Step 2.mp4 104.9 MB
  • 32 - Upper Confidence Bound (UCB)/012 Upper Confidence Bound in R - Step 3.mp4 103.8 MB
  • 29 - Apriori/007 Apriori in R - Step 2.mp4 101.3 MB
  • 32 - Upper Confidence Bound (UCB)/001 The Multi-Armed Bandit Problem.mp4 101.1 MB
  • 40 - Linear Discriminant Analysis (LDA)/003 LDA in R.mp4 98.2 MB
  • 37 - Convolutional Neural Networks/005 Step 2 - Pooling.mp4 91.7 MB
  • 39 - Principal Component Analysis (PCA)/002 PCA in Python - Step 1.mp4 90.2 MB
  • 37 - Convolutional Neural Networks/014 CNN in Python - Step 5.mp4 89.0 MB
  • 36 - Artificial Neural Networks/011 ANN in Python - Step 2.mp4 88.6 MB
  • 44 - XGBoost/001 XGBoost in Python.mp4 88.3 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/004 Classical vs Deep Learning Models.mp4 88.0 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/010 Natural Language Processing in Python - Step 5.mp4 86.5 MB
  • 29 - Apriori/003 Apriori in Python - Step 2.mp4 86.2 MB
  • 32 - Upper Confidence Bound (UCB)/002 Upper Confidence Bound (UCB) Intuition.mp4 83.1 MB
  • 32 - Upper Confidence Bound (UCB)/011 Upper Confidence Bound in R - Step 2.mp4 79.9 MB
  • 40 - Linear Discriminant Analysis (LDA)/002 LDA in Python.mp4 79.1 MB
  • 36 - Artificial Neural Networks/014 ANN in Python - Step 5.mp4 78.9 MB
  • 29 - Apriori/006 Apriori in R - Step 1.mp4 77.5 MB
  • 37 - Convolutional Neural Networks/002 What are convolutional neural networks.mp4 74.5 MB
  • 44 - XGBoost/003 XGBoost in R.mp4 72.7 MB
  • 36 - Artificial Neural Networks/004 How do Neural Networks work.mp4 70.5 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/024 Natural Language Processing in R - Step 10.mp4 69.7 MB
  • 37 - Convolutional Neural Networks/003 Step 1 - Convolution Operation.mp4 68.8 MB
  • 39 - Principal Component Analysis (PCA)/006 PCA in R - Step 3.mp4 68.5 MB
  • 30 - Eclat/003 Eclat in R.mp4 68.5 MB
  • 07 - Multiple Linear Regression/023 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 67.7 MB
  • 10 - Decision Tree Regression/008 Decision Tree Regression in R - Step 2.mp4 67.5 MB
  • 37 - Convolutional Neural Networks/012 CNN in Python - Step 3.mp4 67.4 MB
  • 04 - Data Preprocessing in R/005 Encoding Categorical Data.mp4 67.0 MB
  • 43 - Model Selection/001 k-Fold Cross Validation in Python.mp4 65.1 MB
  • 33 - Thompson Sampling/008 Thompson Sampling in R - Step 1.mp4 62.2 MB
  • 37 - Convolutional Neural Networks/007 Step 4 - Full Connection.mp4 61.4 MB
  • 29 - Apriori/002 Apriori in Python - Step 1.mp4 61.2 MB
  • 21 - Decision Tree Classification/004 Decision Tree Classification in R - Step 1.mp4 60.6 MB
  • 20 - Naive Bayes/002 Naive Bayes Intuition.mp4 60.2 MB
  • 41 - Kernel PCA/001 Kernel PCA in Python.mp4 59.7 MB
  • 30 - Eclat/002 Eclat in Python.mp4 58.9 MB
  • 29 - Apriori/001 Apriori Intuition.mp4 58.9 MB
  • 19 - Kernel SVM/008 Kernel SVM in R - Step 1.mp4 58.0 MB
  • 36 - Artificial Neural Networks/018 ANN in R - Step 4 (Last step).mp4 57.2 MB
  • 43 - Model Selection/003 k-Fold Cross Validation in R.mp4 55.9 MB
  • 14 - Regression Model Selection in R/002 Interpreting Linear Regression Coefficients.mp4 55.2 MB
  • 20 - Naive Bayes/005 Naive Bayes in Python - Step 1.mp4 55.1 MB
  • 18 - Support Vector Machine (SVM)/005 SVM in R - Step 1.mp4 54.4 MB
  • 18 - Support Vector Machine (SVM)/002 SVM in Python - Step 1.mp4 54.0 MB
  • 36 - Artificial Neural Networks/010 ANN in Python - Step 1.mp4 53.3 MB
  • 01 - Welcome to the course! Here we will help you get started in the best conditions/002 Machine Learning Demo - Get Excited!.mp4 53.2 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/014 Natural Language Processing in R - Step 1.mp4 53.0 MB
  • 43 - Model Selection/004 Grid Search in R.mp4 52.5 MB
  • 33 - Thompson Sampling/001 Thompson Sampling Intuition.mp4 51.1 MB
  • 39 - Principal Component Analysis (PCA)/005 PCA in R - Step 2.mp4 48.8 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/011 Natural Language Processing in Python - Step 6.mp4 47.3 MB
  • 32 - Upper Confidence Bound (UCB)/003 Upper Confidence Bound in Python - Step 1.mp4 46.8 MB
  • 36 - Artificial Neural Networks/002 The Neuron.mp4 46.2 MB
  • 22 - Random Forest Classification/006 Random Forest Classification in R - Step 3.mp4 46.1 MB
  • 36 - Artificial Neural Networks/009 Business Problem Description.mp4 45.8 MB
  • 36 - Artificial Neural Networks/005 How do Neural Networks learn.mp4 45.4 MB
  • 21 - Decision Tree Classification/005 Decision Tree Classification in R - Step 2.mp4 44.9 MB
  • 18 - Support Vector Machine (SVM)/006 SVM in R - Step 2.mp4 44.7 MB
  • 37 - Convolutional Neural Networks/009 Softmax & Cross-Entropy.mp4 44.2 MB
  • 20 - Naive Bayes/006 Naive Bayes in Python - Step 2.mp4 44.1 MB
  • 32 - Upper Confidence Bound (UCB)/006 Upper Confidence Bound in Python - Step 4.mp4 43.7 MB
  • 22 - Random Forest Classification/001 Random Forest Classification Intuition.mp4 43.6 MB
  • 17 - K-Nearest Neighbors (K-NN)/005 K-NN in R - Step 1.mp4 42.5 MB
  • 33 - Thompson Sampling/005 Thompson Sampling in Python - Step 3.mp4 42.2 MB
  • 29 - Apriori/004 Apriori in Python - Step 3.mp4 41.4 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/023 Natural Language Processing in R - Step 9.mp4 41.2 MB
  • 22 - Random Forest Classification/005 Random Forest Classification in R - Step 2.mp4 40.9 MB
  • 07 - Multiple Linear Regression/014 Multiple Linear Regression in Python - Step 4a.mp4 40.9 MB
  • 36 - Artificial Neural Networks/012 ANN in Python - Step 3.mp4 40.4 MB
  • 07 - Multiple Linear Regression/011 Multiple Linear Regression in Python - Step 2b.mp4 39.9 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/005 Bag-Of-Words Model.mp4 39.8 MB
  • 21 - Decision Tree Classification/002 Decision Tree Classification in Python - Step 1.mp4 39.7 MB
  • 18 - Support Vector Machine (SVM)/003 SVM in Python - Step 2.mp4 39.5 MB
  • 19 - Kernel SVM/010 Kernel SVM in R - Step 3.mp4 39.2 MB
  • 16 - Logistic Regression/026 Logistic Regression in R - Step 5c.mp4 39.2 MB
  • 19 - Kernel SVM/006 Kernel SVM in Python - Step 1.mp4 38.7 MB
  • 09 - Support Vector Regression (SVR)/001 SVR Intuition (Updated!).mp4 38.6 MB
  • 04 - Data Preprocessing in R/009 Feature Scaling - Step 2.mp4 38.1 MB
  • 27 - Hierarchical Clustering/001 Hierarchical Clustering Intuition.mp4 38.0 MB
  • 17 - K-Nearest Neighbors (K-NN)/007 K-NN in R - Step 3.mp4 37.5 MB
  • 11 - Random Forest Regression/001 Random Forest Regression Intuition.mp4 37.5 MB
  • 26 - K-Means Clustering/014 K-Means Clustering in Python - Step 5b.mp4 37.4 MB
  • 19 - Kernel SVM/007 Kernel SVM in Python - Step 2.mp4 37.2 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/009 Natural Language Processing in Python - Step 4.mp4 37.0 MB
  • 03 - Data Preprocessing in Python/002 Getting Started - Step 2.mp4 36.8 MB
  • 17 - K-Nearest Neighbors (K-NN)/002 K-NN in Python - Step 1.mp4 36.7 MB
  • 22 - Random Forest Classification/002 Random Forest Classification in Python - Step 1.mp4 36.6 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/007 Natural Language Processing in Python - Step 2.mp4 36.5 MB
  • 17 - K-Nearest Neighbors (K-NN)/004 K-NN in Python - Step 3.mp4 36.0 MB
  • 33 - Thompson Sampling/004 Thompson Sampling in Python - Step 2.mp4 35.9 MB
  • 32 - Upper Confidence Bound (UCB)/010 Upper Confidence Bound in R - Step 1.mp4 35.6 MB
  • 21 - Decision Tree Classification/003 Decision Tree Classification in Python - Step 2.mp4 35.3 MB
  • 01 - Welcome to the course! Here we will help you get started in the best conditions/005 Installing R and R Studio (Mac, Linux & Windows).mp4 35.2 MB
  • 17 - K-Nearest Neighbors (K-NN)/003 K-NN in Python - Step 2.mp4 35.2 MB
  • 19 - Kernel SVM/003 The Kernel Trick.mp4 35.2 MB
  • 07 - Multiple Linear Regression/007 Multiple Linear Regression Intuition - Step 5.mp4 35.0 MB
  • 23 - Classification Model Selection in Python/004 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 2.mp4 34.6 MB
  • 22 - Random Forest Classification/003 Random Forest Classification in Python - Step 2.mp4 34.4 MB
  • 16 - Logistic Regression/007 Logistic Regression in Python - Step 2b.mp4 34.4 MB
  • 16 - Logistic Regression/022 Logistic Regression in R - Step 4.mp4 34.2 MB
  • 07 - Multiple Linear Regression/024 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 34.0 MB
  • 46 - Annex Logistic Regression (Long Explanation)/001 Logistic Regression Intuition.mp4 34.0 MB
  • 36 - Artificial Neural Networks/013 ANN in Python - Step 4.mp4 33.4 MB
  • 19 - Kernel SVM/002 Mapping to a higher dimension.mp4 33.4 MB
  • 37 - Convolutional Neural Networks/010 CNN in Python - Step 1.mp4 33.4 MB
  • 27 - Hierarchical Clustering/012 Hierarchical Clustering in R - Step 3.mp4 32.9 MB
  • 03 - Data Preprocessing in Python/009 Taking care of Missing Data - Step 2.mp4 30.9 MB
  • 11 - Random Forest Regression/003 Random Forest Regression in Python - Step 2.mp4 30.6 MB
  • 16 - Logistic Regression/006 Logistic Regression in Python - Step 2a.mp4 30.5 MB
  • 06 - Simple Linear Regression/014 Simple Linear Regression in R - Step 4a.mp4 30.4 MB
  • 13 - Regression Model Selection in Python/007 THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! - STEP 2.mp4 30.2 MB
  • 23 - Classification Model Selection in Python/002 Confusion Matrix & Accuracy Ratios.mp4 30.1 MB
  • 16 - Logistic Regression/024 Logistic Regression in R - Step 5a.mp4 30.0 MB
  • 07 - Multiple Linear Regression/010 Multiple Linear Regression in Python - Step 2a.mp4 29.9 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/008 Natural Language Processing in Python - Step 3.mp4 29.6 MB
  • 14 - Regression Model Selection in R/001 Evaluating Regression Models Performance - Homework's Final Part.mp4 29.1 MB
  • 26 - K-Means Clustering/017 K-Means Clustering in R - Step 2.mp4 29.0 MB
  • 19 - Kernel SVM/005 Non-Linear Kernel SVR (Advanced).mp4 28.8 MB
  • 20 - Naive Bayes/010 Naive Bayes in R - Step 3.mp4 28.5 MB
  • 16 - Logistic Regression/021 Logistic Regression in R - Step 3.mp4 28.3 MB
  • 09 - Support Vector Regression (SVR)/008 SVR in Python - Step 3.mp4 28.2 MB
  • 36 - Artificial Neural Networks/007 Stochastic Gradient Descent.mp4 28.1 MB
  • 07 - Multiple Linear Regression/020 Multiple Linear Regression in R - Step 2a.mp4 28.0 MB
  • 26 - K-Means Clustering/015 K-Means Clustering in Python - Step 5c.mp4 28.0 MB
  • 27 - Hierarchical Clustering/007 Hierarchical Clustering in Python - Step 2c.mp4 27.8 MB
  • 36 - Artificial Neural Networks/006 Gradient Descent.mp4 26.9 MB
  • 01 - Welcome to the course! Here we will help you get started in the best conditions/004 How to use the ML A-Z folder & Google Colab.mp4 26.9 MB
  • 16 - Logistic Regression/028 R Classification Template.mp4 26.7 MB
  • 27 - Hierarchical Clustering/003 Hierarchical Clustering Using Dendrograms.mp4 26.4 MB
  • 36 - Artificial Neural Networks/016 ANN in R - Step 2.mp4 26.2 MB
  • 13 - Regression Model Selection in Python/005 Preparation of the Regression Code Templates - Step 4.mp4 26.1 MB
  • 16 - Logistic Regression/002 Logistic Regression Intuition.mp4 26.0 MB
  • 09 - Support Vector Regression (SVR)/011 SVR in Python - Step 5b.mp4 25.9 MB
  • 16 - Logistic Regression/025 Logistic Regression in R - Step 5b.mp4 25.9 MB
  • 07 - Multiple Linear Regression/003 Assumptions of Linear Regression.mp4 25.8 MB
  • 30 - Eclat/001 Eclat Intuition.mp4 25.4 MB
  • 22 - Random Forest Classification/004 Random Forest Classification in R - Step 1.mp4 25.2 MB
  • 16 - Logistic Regression/016 Logistic Regression in Python - Step 7b.mp4 25.2 MB
  • 08 - Polynomial Regression/013 Polynomial Regression in R - Step 2b.mp4 25.0 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/016 Natural Language Processing in R - Step 2.mp4 24.9 MB
  • 10 - Decision Tree Regression/001 Decision Tree Regression Intuition.mp4 24.4 MB
  • 07 - Multiple Linear Regression/006 Understanding the P-Value.mp4 24.3 MB
  • 04 - Data Preprocessing in R/008 Feature Scaling - Step 1.mp4 23.9 MB
  • 13 - Regression Model Selection in Python/006 THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! - STEP 1.mp4 23.9 MB
  • 20 - Naive Bayes/009 Naive Bayes in R - Step 2.mp4 23.8 MB
  • 37 - Convolutional Neural Networks/013 CNN in Python - Step 4.mp4 23.8 MB
  • 04 - Data Preprocessing in R/010 Data Preprocessing Template.mp4 23.7 MB
  • 27 - Hierarchical Clustering/006 Hierarchical Clustering in Python - Step 2b.mp4 23.4 MB
  • 13 - Regression Model Selection in Python/003 Preparation of the Regression Code Templates - Step 2.mp4 23.0 MB
  • 21 - Decision Tree Classification/006 Decision Tree Classification in R - Step 3.mp4 22.8 MB
  • 23 - Classification Model Selection in Python/005 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 3.mp4 22.7 MB
  • 11 - Random Forest Regression/005 Random Forest Regression in R - Step 2.mp4 22.7 MB
  • 04 - Data Preprocessing in R/004 Taking care of Missing Data.mp4 22.5 MB
  • 23 - Classification Model Selection in Python/003 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 1.mp4 22.1 MB
  • 06 - Simple Linear Regression/007 Simple Linear Regression in Python - Step 3.mp4 22.0 MB
  • 39 - Principal Component Analysis (PCA)/001 Principal Component Analysis (PCA) Intuition.mp4 22.0 MB
  • 39 - Principal Component Analysis (PCA)/003 PCA in Python - Step 2.mp4 21.8 MB
  • 08 - Polynomial Regression/014 Polynomial Regression in R - Step 3a.mp4 21.7 MB
  • 33 - Thompson Sampling/006 Thompson Sampling in Python - Step 4.mp4 21.7 MB
  • 06 - Simple Linear Regression/015 Simple Linear Regression in R - Step 4b.mp4 21.7 MB
  • 37 - Convolutional Neural Networks/004 Step 1(b) - ReLU Layer.mp4 21.6 MB
  • 08 - Polynomial Regression/019 R Regression Template - Step 1.mp4 21.6 MB
  • 16 - Logistic Regression/015 Logistic Regression in Python - Step 7a.mp4 21.5 MB
  • 32 - Upper Confidence Bound (UCB)/009 Upper Confidence Bound in Python - Step 7.mp4 21.5 MB
  • 27 - Hierarchical Clustering/004 Hierarchical Clustering in Python - Step 1.mp4 21.4 MB
  • 16 - Logistic Regression/008 Logistic Regression in Python - Step 3a.mp4 21.4 MB
  • 16 - Logistic Regression/017 Logistic Regression in Python - Step 7c.mp4 21.1 MB
  • 18 - Support Vector Machine (SVM)/001 SVM Intuition.mp4 21.1 MB
  • 11 - Random Forest Regression/004 Random Forest Regression in R - Step 1.mp4 21.1 MB
  • 09 - Support Vector Regression (SVR)/002 Heads-up on non-linear SVR.mp4 20.7 MB
  • 08 - Polynomial Regression/003 Polynomial Regression in Python - Step 1b.mp4 20.7 MB
  • 08 - Polynomial Regression/006 Polynomial Regression in Python - Step 3a.mp4 20.7 MB
  • 03 - Data Preprocessing in Python/011 Encoding Categorical Data - Step 2.mp4 20.7 MB
  • 24 - Evaluating Classification Models Performance/001 False Positives & False Negatives.mp4 20.6 MB
  • 04 - Data Preprocessing in R/007 Splitting the dataset into the Training set and Test set - Step 2.mp4 20.6 MB
  • 08 - Polynomial Regression/015 Polynomial Regression in R - Step 3b.mp4 20.5 MB
  • 27 - Hierarchical Clustering/013 Hierarchical Clustering in R - Step 4.mp4 20.3 MB
  • 06 - Simple Linear Regression/009 Simple Linear Regression in Python - Step 4b.mp4 20.3 MB
  • 16 - Logistic Regression/019 Logistic Regression in R - Step 1.mp4 20.2 MB
  • 06 - Simple Linear Regression/012 Simple Linear Regression in R - Step 2.mp4 20.0 MB
  • 32 - Upper Confidence Bound (UCB)/005 Upper Confidence Bound in Python - Step 3.mp4 20.0 MB
  • 32 - Upper Confidence Bound (UCB)/008 Upper Confidence Bound in Python - Step 6.mp4 20.0 MB
  • 07 - Multiple Linear Regression/004 Multiple Linear Regression Intuition - Step 3.mp4 19.9 MB
  • 24 - Evaluating Classification Models Performance/003 CAP Curve.mp4 19.9 MB
  • 07 - Multiple Linear Regression/008 Multiple Linear Regression in Python - Step 1a.mp4 19.8 MB
  • 19 - Kernel SVM/009 Kernel SVM in R - Step 2.mp4 19.7 MB
  • 26 - K-Means Clustering/004 K-Means++.mp4 19.6 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/017 Natural Language Processing in R - Step 3.mp4 19.5 MB
  • 08 - Polynomial Regression/007 Polynomial Regression in Python - Step 3b.mp4 19.2 MB
  • 20 - Naive Bayes/008 Naive Bayes in R - Step 1.mp4 19.2 MB
  • 16 - Logistic Regression/012 Logistic Regression in Python - Step 5.mp4 19.1 MB
  • 08 - Polynomial Regression/005 Polynomial Regression in Python - Step 2b.mp4 18.8 MB
  • 16 - Logistic Regression/010 Logistic Regression in Python - Step 4a.mp4 18.7 MB
  • 17 - K-Nearest Neighbors (K-NN)/006 K-NN in R - Step 2.mp4 18.7 MB
  • 21 - Decision Tree Classification/001 Decision Tree Classification Intuition.mp4 18.6 MB
  • 07 - Multiple Linear Regression/021 Multiple Linear Regression in R - Step 2b.mp4 18.6 MB
  • 06 - Simple Linear Regression/008 Simple Linear Regression in Python - Step 4a.mp4 18.5 MB
  • 11 - Random Forest Regression/002 Random Forest Regression in Python - Step 1.mp4 18.3 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/020 Natural Language Processing in R - Step 6.mp4 18.2 MB
  • 36 - Artificial Neural Networks/003 The Activation Function.mp4 18.1 MB
  • 33 - Thompson Sampling/002 Algorithm Comparison UCB vs Thompson Sampling.mp4 18.1 MB
  • 09 - Support Vector Regression (SVR)/012 SVR in R - Step 1.mp4 18.1 MB
  • 09 - Support Vector Regression (SVR)/005 SVR in Python - Step 2a.mp4 18.0 MB
  • 27 - Hierarchical Clustering/008 Hierarchical Clustering in Python - Step 3a.mp4 17.9 MB
  • 03 - Data Preprocessing in Python/019 Feature Scaling - Step 4.mp4 17.7 MB
  • 08 - Polynomial Regression/010 Polynomial Regression in R - Step 1a.mp4 17.7 MB
  • 32 - Upper Confidence Bound (UCB)/007 Upper Confidence Bound in Python - Step 5.mp4 17.6 MB
  • 11 - Random Forest Regression/006 Random Forest Regression in R - Step 3.mp4 17.5 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/022 Natural Language Processing in R - Step 8.mp4 17.5 MB
  • 10 - Decision Tree Regression/007 Decision Tree Regression in R - Step 1.mp4 17.5 MB
  • 04 - Data Preprocessing in R/006 Splitting the dataset into the Training set and Test set - Step 1.mp4 17.4 MB
  • 12 - Evaluating Regression Models Performance/001 R-Squared Intuition.mp4 17.3 MB
  • 08 - Polynomial Regression/004 Polynomial Regression in Python - Step 2a.mp4 17.3 MB
  • 07 - Multiple Linear Regression/005 Multiple Linear Regression Intuition - Step 4.mp4 17.3 MB
  • 26 - K-Means Clustering/012 K-Means Clustering in Python - Step 4.mp4 17.3 MB
  • 27 - Hierarchical Clustering/002 Hierarchical Clustering How Dendrograms Work.mp4 17.2 MB
  • 08 - Polynomial Regression/016 Polynomial Regression in R - Step 3c.mp4 17.0 MB
  • 20 - Naive Bayes/004 Naive Bayes Intuition (Extras).mp4 16.9 MB
  • 03 - Data Preprocessing in Python/008 Taking care of Missing Data - Step 1.mp4 16.9 MB
  • 26 - K-Means Clustering/006 K-Means Clustering in Python - Step 1b.mp4 16.4 MB
  • 26 - K-Means Clustering/001 What is Clustering (Supervised vs Unsupervised Learning).mp4 16.2 MB
  • 27 - Hierarchical Clustering/009 Hierarchical Clustering in Python - Step 3b.mp4 15.9 MB
  • 26 - K-Means Clustering/016 K-Means Clustering in R - Step 1.mp4 15.9 MB
  • 26 - K-Means Clustering/013 K-Means Clustering in Python - Step 5a.mp4 15.8 MB
  • 40 - Linear Discriminant Analysis (LDA)/001 Linear Discriminant Analysis (LDA) Intuition.mp4 15.8 MB
  • 08 - Polynomial Regression/017 Polynomial Regression in R - Step 4a.mp4 15.7 MB
  • 09 - Support Vector Regression (SVR)/006 SVR in Python - Step 2b.mp4 15.7 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/006 Natural Language Processing in Python - Step 1.mp4 15.6 MB
  • 07 - Multiple Linear Regression/012 Multiple Linear Regression in Python - Step 3a.mp4 15.5 MB
  • 07 - Multiple Linear Regression/019 Multiple Linear Regression in R - Step 1b.mp4 15.4 MB
  • 07 - Multiple Linear Regression/013 Multiple Linear Regression in Python - Step 3b.mp4 15.4 MB
  • 06 - Simple Linear Regression/013 Simple Linear Regression in R - Step 3.mp4 15.3 MB
  • 06 - Simple Linear Regression/004 Simple Linear Regression in Python - Step 1b.mp4 15.2 MB
  • 08 - Polynomial Regression/012 Polynomial Regression in R - Step 2a.mp4 15.2 MB
  • 24 - Evaluating Classification Models Performance/004 CAP Curve Analysis.mp4 15.1 MB
  • 07 - Multiple Linear Regression/022 Multiple Linear Regression in R - Step 3.mp4 15.0 MB
  • 08 - Polynomial Regression/018 Polynomial Regression in R - Step 4b.mp4 14.9 MB
  • 07 - Multiple Linear Regression/015 Multiple Linear Regression in Python - Step 4b.mp4 14.9 MB
  • 03 - Data Preprocessing in Python/012 Encoding Categorical Data - Step 3.mp4 14.8 MB
  • 07 - Multiple Linear Regression/001 Dataset + Business Problem Description.mp4 14.8 MB
  • 02 - -------------------- Part 1 Data Preprocessing --------------------/004 Feature Scaling.mp4 14.7 MB
  • 36 - Artificial Neural Networks/008 Backpropagation.mp4 14.7 MB
  • 03 - Data Preprocessing in Python/006 Importing the Dataset - Step 3.mp4 14.6 MB
  • 27 - Hierarchical Clustering/014 Hierarchical Clustering in R - Step 5.mp4 14.5 MB
  • 16 - Logistic Regression/013 Logistic Regression in Python - Step 6a.mp4 14.4 MB
  • 13 - Regression Model Selection in Python/004 Preparation of the Regression Code Templates - Step 3.mp4 14.4 MB
  • 08 - Polynomial Regression/011 Polynomial Regression in R - Step 1b.mp4 14.4 MB
  • 03 - Data Preprocessing in Python/014 Splitting the dataset into the Training set and Test set - Step 2.mp4 14.3 MB
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  • 34 - -------------------- Part 7 Natural Language Processing --------------------/021 Natural Language Processing in R - Step 7.mp4 11.1 MB
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  • 34 - -------------------- Part 7 Natural Language Processing --------------------/018 Natural Language Processing in R - Step 4.mp4 9.2 MB
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  • 08 - Polynomial Regression/001 Polynomial Regression Intuition.mp4 9.0 MB
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  • 07 - Multiple Linear Regression/002 Multiple Linear Regression Intuition.mp4 8.8 MB
  • 10 - Decision Tree Regression/005 Decision Tree Regression in Python - Step 3.mp4 8.8 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/003 Types of Natural Language Processing.mp4 8.5 MB
  • 23 - Classification Model Selection in Python/006 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 4.mp4 8.5 MB
  • 02 - -------------------- Part 1 Data Preprocessing --------------------/002 The Machine Learning process.mp4 8.4 MB
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  • 26 - K-Means Clustering/003 The Elbow Method.mp4 7.9 MB
  • 08 - Polynomial Regression/002 Polynomial Regression in Python - Step 1a.mp4 7.8 MB
  • 03 - Data Preprocessing in Python/003 Importing the Libraries.mp4 7.8 MB
  • 16 - Logistic Regression/003 Maximum Likelihood.mp4 7.5 MB
  • 04 - Data Preprocessing in R/003 Importing the Dataset.mp4 7.2 MB
  • 19 - Kernel SVM/001 Kernel SVM Intuition.mp4 7.2 MB
  • 20 - Naive Bayes/007 Naive Bayes in Python - Step 3.mp4 7.0 MB
  • 04 - Data Preprocessing in R/002 Dataset Description.mp4 6.7 MB
  • 37 - Convolutional Neural Networks/001 Plan of attack.mp4 6.5 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/019 Natural Language Processing in R - Step 5.mp4 6.5 MB
  • 16 - Logistic Regression/001 What is Classification.mp4 5.9 MB
  • 02 - -------------------- Part 1 Data Preprocessing --------------------/003 Splitting the data into a Training and Test set.mp4 5.6 MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/002 NLP Intuition.mp4 5.4 MB
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  • 36 - Artificial Neural Networks/001 Plan of attack.mp4 5.0 MB
  • 16 - Logistic Regression/011 Logistic Regression in Python - Step 4b.mp4 4.7 MB
  • 24 - Evaluating Classification Models Performance/002 Accuracy Paradox.mp4 4.4 MB
  • 26 - K-Means Clustering/002 K-Means Clustering Intuition.mp4 4.3 MB
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  • 37 - Convolutional Neural Networks/006 Step 3 - Flattening.mp4 3.3 MB
  • 13 - Regression Model Selection in Python/008 Regression-Bonus.zip 373.2 kB
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  • 13 - Regression Model Selection in Python/001 Machine-Learning-A-Z-Model-Selection.zip 165.8 kB
  • 23 - Classification Model Selection in Python/001 Machine-Learning-A-Z-Model-Selection.zip 163.8 kB
  • 30 - Eclat/003 Eclat.zip 49.7 kB
  • 37 - Convolutional Neural Networks/015 CNN in Python - FINAL DEMO!_en.srt 39.5 kB
  • 43 - Model Selection/002 Grid Search in Python_en.srt 39.4 kB
  • 41 - Kernel PCA/002 Kernel PCA in R_en.srt 38.2 kB
  • 37 - Convolutional Neural Networks/009 Softmax & Cross-Entropy_en.srt 38.0 kB
  • 29 - Apriori/005 Apriori in Python - Step 4_en.srt 36.6 kB
  • 40 - Linear Discriminant Analysis (LDA)/003 LDA in R_en.srt 36.4 kB
  • 37 - Convolutional Neural Networks/007 Step 4 - Full Connection_en.srt 36.3 kB
  • 43 - Model Selection/003 k-Fold Cross Validation in R_en.srt 36.2 kB
  • 33 - Thompson Sampling/001 Thompson Sampling Intuition_en.srt 35.8 kB
  • 33 - Thompson Sampling/008 Thompson Sampling in R - Step 1_en.srt 35.3 kB
  • 36 - Artificial Neural Networks/011 ANN in Python - Step 2_en.srt 35.0 kB
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  • 20 - Naive Bayes/001 Bayes Theorem_en.srt 33.9 kB
  • 43 - Model Selection/001 k-Fold Cross Validation in Python_en.srt 33.4 kB
  • 36 - Artificial Neural Networks/015 ANN in R - Step 1_en.srt 33.3 kB
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  • 34 - -------------------- Part 7 Natural Language Processing --------------------/024 Natural Language Processing in R - Step 10_en.srt 32.5 kB
  • 29 - Apriori/001 Apriori Intuition_en.srt 32.5 kB
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  • 46 - Annex Logistic Regression (Long Explanation)/001 Logistic Regression Intuition_en.srt 32.2 kB
  • 36 - Artificial Neural Networks/002 The Neuron_en.srt 31.9 kB
  • 29 - Apriori/003 Apriori in Python - Step 2_en.srt 31.9 kB
  • 44 - XGBoost/003 XGBoost in R_en.srt 31.8 kB
  • 32 - Upper Confidence Bound (UCB)/012 Upper Confidence Bound in R - Step 3_en.srt 31.4 kB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/005 Bag-Of-Words Model_en.srt 31.0 kB
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  • 39 - Principal Component Analysis (PCA)/002 PCA in Python - Step 1_en.srt 30.8 kB
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  • 29 - Apriori/008 Apriori in R - Step 3_en.srt 30.7 kB
  • 29 - Apriori/006 Apriori in R - Step 1_en.srt 30.6 kB
  • 37 - Convolutional Neural Networks/003 Step 1 - Convolution Operation_en.srt 30.1 kB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/014 Natural Language Processing in R - Step 1_en.srt 30.1 kB
  • 24 - Evaluating Classification Models Performance/005 Classification-Pros-Cons.pdf 30.0 kB
  • 37 - Convolutional Neural Networks/002 What are convolutional neural networks_en.srt 29.4 kB
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  • 32 - Upper Confidence Bound (UCB)/001 The Multi-Armed Bandit Problem_en.srt 27.8 kB
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  • 32 - Upper Confidence Bound (UCB)/002 Upper Confidence Bound (UCB) Intuition_en.srt 27.5 kB
  • 36 - Artificial Neural Networks/012 ANN in Python - Step 3_en.srt 27.5 kB
  • 37 - Convolutional Neural Networks/014 CNN in Python - Step 5_en.srt 27.2 kB
  • 07 - Multiple Linear Regression/023 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !_en.srt 27.1 kB
  • 40 - Linear Discriminant Analysis (LDA)/002 LDA in Python_en.srt 27.0 kB
  • 32 - Upper Confidence Bound (UCB)/010 Upper Confidence Bound in R - Step 1_en.srt 26.7 kB
  • 43 - Model Selection/004 Grid Search in R_en.srt 26.6 kB
  • 27 - Hierarchical Clustering/015 Clustering-Pros-Cons.pdf 26.4 kB
  • 32 - Upper Confidence Bound (UCB)/003 Upper Confidence Bound in Python - Step 1_en.srt 26.2 kB
  • 44 - XGBoost/001 XGBoost in Python_en.srt 26.0 kB
  • 36 - Artificial Neural Networks/018 ANN in R - Step 4 (Last step)_en.srt 25.6 kB
  • 36 - Artificial Neural Networks/004 How do Neural Networks work_en.srt 25.2 kB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/008 Natural Language Processing in Python - Step 3_en.srt 25.0 kB
  • 33 - Thompson Sampling/005 Thompson Sampling in Python - Step 3_en.srt 25.0 kB
  • 39 - Principal Component Analysis (PCA)/006 PCA in R - Step 3_en.srt 24.9 kB
  • 35 - -------------------- Part 8 Deep Learning --------------------/002 What is Deep Learning_en.srt 24.9 kB
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  • 29 - Apriori/004 Apriori in Python - Step 3_en.srt 23.9 kB
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  • 30 - Eclat/002 Eclat in Python_en.srt 23.1 kB
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  • 29 - Apriori/007 Apriori in R - Step 2_en.srt 22.7 kB
  • 07 - Multiple Linear Regression/006 Understanding the P-Value_en.srt 22.5 kB
  • 37 - Convolutional Neural Networks/010 CNN in Python - Step 1_en.srt 21.5 kB
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  • 19 - Kernel SVM/005 Non-Linear Kernel SVR (Advanced)_en.srt 21.0 kB
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  • 41 - Kernel PCA/001 Kernel PCA in Python_en.srt 19.9 kB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/004 Classical vs Deep Learning Models_en.srt 19.9 kB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/011 Natural Language Processing in Python - Step 6_en.srt 19.2 kB
  • 29 - Apriori/002 Apriori in Python - Step 1_en.srt 17.8 kB
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  • 27 - Hierarchical Clustering/003 Hierarchical Clustering Using Dendrograms_en.srt 17.4 kB
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  • 24 - Evaluating Classification Models Performance/003 CAP Curve_en.srt 16.0 kB
  • 36 - Artificial Neural Networks/003 The Activation Function_en.srt 15.9 kB
  • 20 - Naive Bayes/004 Naive Bayes Intuition (Extras)_en.srt 15.7 kB
  • 30 - Eclat/003 Eclat in R_en.srt 15.6 kB
  • 18 - Support Vector Machine (SVM)/001 SVM Intuition_en.srt 15.5 kB
  • 36 - Artificial Neural Networks/007 Stochastic Gradient Descent_en.srt 15.4 kB
  • 09 - Support Vector Regression (SVR)/001 SVR Intuition (Updated!)_en.srt 15.1 kB
  • 19 - Kernel SVM/002 Mapping to a higher dimension_en.srt 14.9 kB
  • 32 - Upper Confidence Bound (UCB)/009 Upper Confidence Bound in Python - Step 7_en.srt 14.8 kB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/006 Natural Language Processing in Python - Step 1_en.srt 14.4 kB
  • 27 - Hierarchical Clustering/001 Hierarchical Clustering Intuition_en.srt 14.4 kB
  • 33 - Thompson Sampling/002 Algorithm Comparison UCB vs Thompson Sampling_en.srt 14.3 kB
  • 27 - Hierarchical Clustering/002 Hierarchical Clustering How Dendrograms Work_en.srt 14.1 kB
  • 37 - Convolutional Neural Networks/013 CNN in Python - Step 4_en.srt 13.9 kB
  • 32 - Upper Confidence Bound (UCB)/005 Upper Confidence Bound in Python - Step 3_en.srt 13.9 kB
  • 32 - Upper Confidence Bound (UCB)/008 Upper Confidence Bound in Python - Step 6_en.srt 13.8 kB
  • 03 - Data Preprocessing in Python/008 Taking care of Missing Data - Step 1_en.srt 13.6 kB
  • 33 - Thompson Sampling/006 Thompson Sampling in Python - Step 4_en.srt 13.4 kB
  • 14 - Regression Model Selection in R/002 Interpreting Linear Regression Coefficients_en.srt 13.2 kB
  • 26 - K-Means Clustering/015 K-Means Clustering in Python - Step 5c_en.srt 13.1 kB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/007 Natural Language Processing in Python - Step 2_en.srt 13.0 kB
  • 14 - Regression Model Selection in R/001 Evaluating Regression Models Performance - Homework's Final Part_en.srt 12.7 kB
  • 09 - Support Vector Regression (SVR)/008 SVR in Python - Step 3_en.srt 12.7 kB
  • 21 - Decision Tree Classification/001 Decision Tree Classification Intuition_en.srt 12.7 kB
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  • 17 - K-Nearest Neighbors (K-NN)/003 K-NN in Python - Step 2_en.srt 12.6 kB
  • 32 - Upper Confidence Bound (UCB)/007 Upper Confidence Bound in Python - Step 5_en.srt 12.5 kB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/017 Natural Language Processing in R - Step 3_en.srt 12.4 kB
  • 21 - Decision Tree Classification/003 Decision Tree Classification in Python - Step 2_en.srt 12.2 kB
  • 02 - -------------------- Part 1 Data Preprocessing --------------------/004 Feature Scaling_en.srt 12.2 kB
  • 23 - Classification Model Selection in Python/003 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 1_en.srt 12.0 kB
  • 01 - Welcome to the course! Here we will help you get started in the best conditions/004 How to use the ML A-Z folder & Google Colab_en.srt 12.0 kB
  • 19 - Kernel SVM/007 Kernel SVM in Python - Step 2_en.srt 12.0 kB
  • 33 - Thompson Sampling/003 Thompson Sampling in Python - Step 1_en.srt 11.9 kB
  • 22 - Random Forest Classification/003 Random Forest Classification in Python - Step 2_en.srt 11.9 kB
  • 22 - Random Forest Classification/004 Random Forest Classification in R - Step 1_en.srt 11.8 kB
  • 23 - Classification Model Selection in Python/004 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 2_en.srt 11.7 kB
  • 07 - Multiple Linear Regression/024 Multiple Linear Regression in R - Backward Elimination - Homework Solution_en.srt 11.7 kB
  • 11 - Random Forest Regression/005 Random Forest Regression in R - Step 2_en.srt 11.7 kB
  • 08 - Polynomial Regression/004 Polynomial Regression in Python - Step 2a_en.srt 11.7 kB
  • 26 - K-Means Clustering/016 K-Means Clustering in R - Step 1_en.srt 11.6 kB
  • 37 - Convolutional Neural Networks/004 Step 1(b) - ReLU Layer_en.srt 11.6 kB
  • 08 - Polynomial Regression/003 Polynomial Regression in Python - Step 1b_en.srt 11.6 kB
  • 18 - Support Vector Machine (SVM)/003 SVM in Python - Step 2_en.srt 11.6 kB
  • 07 - Multiple Linear Regression/012 Multiple Linear Regression in Python - Step 3a_en.srt 11.5 kB
  • 21 - Decision Tree Classification/002 Decision Tree Classification in Python - Step 1_en.srt 11.5 kB
  • 06 - Simple Linear Regression/004 Simple Linear Regression in Python - Step 1b_en.srt 11.5 kB
  • 06 - Simple Linear Regression/003 Simple Linear Regression in Python - Step 1a_en.srt 11.5 kB
  • 26 - K-Means Clustering/017 K-Means Clustering in R - Step 2_en.srt 11.4 kB
  • 27 - Hierarchical Clustering/007 Hierarchical Clustering in Python - Step 2c_en.srt 11.4 kB
  • 03 - Data Preprocessing in Python/019 Feature Scaling - Step 4_en.srt 11.4 kB
  • 17 - K-Nearest Neighbors (K-NN)/004 K-NN in Python - Step 3_en.srt 11.4 kB
  • 06 - Simple Linear Regression/009 Simple Linear Regression in Python - Step 4b_en.srt 11.4 kB
  • 27 - Hierarchical Clustering/004 Hierarchical Clustering in Python - Step 1_en.srt 11.4 kB
  • 16 - Logistic Regression/028 R Classification Template_en.srt 11.3 kB
  • 11 - Random Forest Regression/004 Random Forest Regression in R - Step 1_en.srt 11.3 kB
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  • 11 - Random Forest Regression/002 Random Forest Regression in Python - Step 1_en.srt 11.3 kB
  • 08 - Polynomial Regression/019 R Regression Template - Step 1_en.srt 11.3 kB
  • 03 - Data Preprocessing in Python/006 Importing the Dataset - Step 3_en.srt 11.3 kB
  • 16 - Logistic Regression/025 Logistic Regression in R - Step 5b_en.srt 11.3 kB
  • 13 - Regression Model Selection in Python/003 Preparation of the Regression Code Templates - Step 2_en.srt 11.2 kB
  • 16 - Logistic Regression/012 Logistic Regression in Python - Step 5_en.srt 11.2 kB
  • 23 - Classification Model Selection in Python/005 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 3_en.srt 11.2 kB
  • 22 - Random Forest Classification/005 Random Forest Classification in R - Step 2_en.srt 11.2 kB
  • 08 - Polynomial Regression/006 Polynomial Regression in Python - Step 3a_en.srt 11.2 kB
  • 24 - Evaluating Classification Models Performance/001 False Positives & False Negatives_en.srt 11.2 kB
  • 03 - Data Preprocessing in Python/016 Feature Scaling - Step 1_en.srt 11.1 kB
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  • 04 - Data Preprocessing in R/010 Data Preprocessing Template_en.srt 11.1 kB
  • 07 - Multiple Linear Regression/008 Multiple Linear Regression in Python - Step 1a_en.srt 11.1 kB
  • 07 - Multiple Linear Regression/014 Multiple Linear Regression in Python - Step 4a_en.srt 11.0 kB
  • 22 - Random Forest Classification/006 Random Forest Classification in R - Step 3_en.srt 11.0 kB
  • 26 - K-Means Clustering/010 K-Means Clustering in Python - Step 3b_en.srt 11.0 kB
  • 03 - Data Preprocessing in Python/011 Encoding Categorical Data - Step 2_en.srt 11.0 kB
  • 11 - Random Forest Regression/003 Random Forest Regression in Python - Step 2_en.srt 11.0 kB
  • 19 - Kernel SVM/006 Kernel SVM in Python - Step 1_en.srt 11.0 kB
  • 17 - K-Nearest Neighbors (K-NN)/002 K-NN in Python - Step 1_en.srt 11.0 kB
  • 22 - Random Forest Classification/002 Random Forest Classification in Python - Step 1_en.srt 10.9 kB
  • 08 - Polynomial Regression/005 Polynomial Regression in Python - Step 2b_en.srt 10.9 kB
  • 03 - Data Preprocessing in Python/001 Getting Started - Step 1_en.srt 10.9 kB
  • 06 - Simple Linear Regression/008 Simple Linear Regression in Python - Step 4a_en.srt 10.9 kB
  • 16 - Logistic Regression/007 Logistic Regression in Python - Step 2b_en.srt 10.9 kB
  • 20 - Naive Bayes/005 Naive Bayes in Python - Step 1_en.srt 10.9 kB
  • 09 - Support Vector Regression (SVR)/003 SVR in Python - Step 1a_en.srt 10.9 kB
  • 04 - Data Preprocessing in R/004 Taking care of Missing Data_en.srt 10.9 kB
  • 26 - K-Means Clustering/009 K-Means Clustering in Python - Step 3a_en.srt 10.9 kB
  • 11 - Random Forest Regression/006 Random Forest Regression in R - Step 3_en.srt 10.8 kB
  • 03 - Data Preprocessing in Python/014 Splitting the dataset into the Training set and Test set - Step 2_en.srt 10.8 kB
  • 20 - Naive Bayes/006 Naive Bayes in Python - Step 2_en.srt 10.8 kB
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  • 27 - Hierarchical Clustering/015 Conclusion of Part 4 - Clustering.html 502 Bytes
  • 28 - -------------------- Part 5 Association Rule Learning --------------------/001 Welcome to Part 5 - Association Rule Learning.html 477 Bytes
  • 01 - Welcome to the course! Here we will help you get started in the best conditions/003 Get all the Datasets, Codes and Slides here.html 442 Bytes
  • 0. Websites you may like/[CourseClub.Me].url 122 Bytes
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  • 24 - Evaluating Classification Models Performance/[CourseClub.Me].url 122 Bytes
  • 37 - Convolutional Neural Networks/[CourseClub.Me].url 122 Bytes
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  • 07 - Multiple Linear Regression/003 Download-the-PDF.url 68 Bytes
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