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[FreeCourseSite.com] Udemy - Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023

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种子名称： [FreeCourseSite.com] Udemy - Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023
文件类型： 视频
文件数目： 343个文件
文件大小： 10.27 GB
收录时间： 2023-11-1 06:08
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[FreeCourseSite.com] Udemy - Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023.torrent
  • 01 - Welcome to the course! Here we will help you get started in the best conditions/002 Machine Learning Demo - Get Excited!.mp450.77MB
  • 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.mp425.62MB
  • 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).mp433.59MB
  • 02 - -------------------- Part 1 Data Preprocessing --------------------/002 The Machine Learning process.mp47.98MB
  • 02 - -------------------- Part 1 Data Preprocessing --------------------/003 Splitting the data into a Training and Test set.mp45.36MB
  • 02 - -------------------- Part 1 Data Preprocessing --------------------/004 Feature Scaling.mp414.02MB
  • 03 - Data Preprocessing in Python/001 Getting Started - Step 1.mp410.78MB
  • 03 - Data Preprocessing in Python/002 Getting Started - Step 2.mp435.12MB
  • 03 - Data Preprocessing in Python/003 Importing the Libraries.mp47.45MB
  • 03 - Data Preprocessing in Python/004 Importing the Dataset - Step 1.mp412.55MB
  • 03 - Data Preprocessing in Python/005 Importing the Dataset - Step 2.mp49.8MB
  • 03 - Data Preprocessing in Python/006 Importing the Dataset - Step 3.mp413.92MB
  • 03 - Data Preprocessing in Python/008 Taking care of Missing Data - Step 1.mp416.08MB
  • 03 - Data Preprocessing in Python/009 Taking care of Missing Data - Step 2.mp429.43MB
  • 03 - Data Preprocessing in Python/010 Encoding Categorical Data - Step 1.mp413.44MB
  • 03 - Data Preprocessing in Python/011 Encoding Categorical Data - Step 2.mp419.73MB
  • 03 - Data Preprocessing in Python/012 Encoding Categorical Data - Step 3.mp414.11MB
  • 03 - Data Preprocessing in Python/013 Splitting the dataset into the Training set and Test set - Step 1.mp410.31MB
  • 03 - Data Preprocessing in Python/014 Splitting the dataset into the Training set and Test set - Step 2.mp413.67MB
  • 03 - Data Preprocessing in Python/015 Splitting the dataset into the Training set and Test set - Step 3.mp411.64MB
  • 03 - Data Preprocessing in Python/016 Feature Scaling - Step 1.mp413.07MB
  • 03 - Data Preprocessing in Python/017 Feature Scaling - Step 2.mp411.71MB
  • 03 - Data Preprocessing in Python/018 Feature Scaling - Step 3.mp411.23MB
  • 03 - Data Preprocessing in Python/019 Feature Scaling - Step 4.mp416.87MB
  • 04 - Data Preprocessing in R/001 Getting Started.mp44.06MB
  • 04 - Data Preprocessing in R/002 Dataset Description.mp46.42MB
  • 04 - Data Preprocessing in R/003 Importing the Dataset.mp46.91MB
  • 04 - Data Preprocessing in R/004 Taking care of Missing Data.mp421.44MB
  • 04 - Data Preprocessing in R/005 Encoding Categorical Data.mp463.89MB
  • 04 - Data Preprocessing in R/006 Splitting the dataset into the Training set and Test set - Step 1.mp416.6MB
  • 04 - Data Preprocessing in R/007 Splitting the dataset into the Training set and Test set - Step 2.mp419.66MB
  • 04 - Data Preprocessing in R/008 Feature Scaling - Step 1.mp422.81MB
  • 04 - Data Preprocessing in R/009 Feature Scaling - Step 2.mp436.32MB
  • 04 - Data Preprocessing in R/010 Data Preprocessing Template.mp422.6MB
  • 06 - Simple Linear Regression/001 Simple Linear Regression Intuition.mp44.97MB
  • 06 - Simple Linear Regression/002 Ordinary Least Squares.mp412.73MB
  • 06 - Simple Linear Regression/003 Simple Linear Regression in Python - Step 1a.mp48.69MB
  • 06 - Simple Linear Regression/004 Simple Linear Regression in Python - Step 1b.mp414.54MB
  • 06 - Simple Linear Regression/005 Simple Linear Regression in Python - Step 2a.mp48.46MB
  • 06 - Simple Linear Regression/006 Simple Linear Regression in Python - Step 2b.mp410.85MB
  • 06 - Simple Linear Regression/007 Simple Linear Regression in Python - Step 3.mp421.02MB
  • 06 - Simple Linear Regression/008 Simple Linear Regression in Python - Step 4a.mp417.66MB
  • 06 - Simple Linear Regression/009 Simple Linear Regression in Python - Step 4b.mp419.35MB
  • 06 - Simple Linear Regression/011 Simple Linear Regression in R - Step 1.mp411.3MB
  • 06 - Simple Linear Regression/012 Simple Linear Regression in R - Step 2.mp419.09MB
  • 06 - Simple Linear Regression/013 Simple Linear Regression in R - Step 3.mp414.61MB
  • 06 - Simple Linear Regression/014 Simple Linear Regression in R - Step 4a.mp428.99MB
  • 06 - Simple Linear Regression/015 Simple Linear Regression in R - Step 4b.mp420.68MB
  • 07 - Multiple Linear Regression/001 Dataset + Business Problem Description.mp414.09MB
  • 07 - Multiple Linear Regression/002 Multiple Linear Regression Intuition.mp48.41MB
  • 07 - Multiple Linear Regression/003 Assumptions of Linear Regression.mp424.61MB
  • 07 - Multiple Linear Regression/004 Multiple Linear Regression Intuition - Step 3.mp419.01MB
  • 07 - Multiple Linear Regression/005 Multiple Linear Regression Intuition - Step 4.mp416.46MB
  • 07 - Multiple Linear Regression/006 Understanding the P-Value.mp423.16MB
  • 07 - Multiple Linear Regression/007 Multiple Linear Regression Intuition - Step 5.mp433.4MB
  • 07 - Multiple Linear Regression/008 Multiple Linear Regression in Python - Step 1a.mp418.88MB
  • 07 - Multiple Linear Regression/009 Multiple Linear Regression in Python - Step 1b.mp412.28MB
  • 07 - Multiple Linear Regression/010 Multiple Linear Regression in Python - Step 2a.mp428.52MB
  • 07 - Multiple Linear Regression/011 Multiple Linear Regression in Python - Step 2b.mp438.08MB
  • 07 - Multiple Linear Regression/012 Multiple Linear Regression in Python - Step 3a.mp414.75MB
  • 07 - Multiple Linear Regression/013 Multiple Linear Regression in Python - Step 3b.mp414.65MB
  • 07 - Multiple Linear Regression/014 Multiple Linear Regression in Python - Step 4a.mp438.97MB
  • 07 - Multiple Linear Regression/015 Multiple Linear Regression in Python - Step 4b.mp414.17MB
  • 07 - Multiple Linear Regression/018 Multiple Linear Regression in R - Step 1a.mp410.6MB
  • 07 - Multiple Linear Regression/019 Multiple Linear Regression in R - Step 1b.mp414.67MB
  • 07 - Multiple Linear Regression/020 Multiple Linear Regression in R - Step 2a.mp426.69MB
  • 07 - Multiple Linear Regression/021 Multiple Linear Regression in R - Step 2b.mp417.74MB
  • 07 - Multiple Linear Regression/022 Multiple Linear Regression in R - Step 3.mp414.31MB
  • 07 - Multiple Linear Regression/023 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp464.53MB
  • 07 - Multiple Linear Regression/024 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp432.47MB
  • 08 - Polynomial Regression/001 Polynomial Regression Intuition.mp48.59MB
  • 08 - Polynomial Regression/002 Polynomial Regression in Python - Step 1a.mp47.46MB
  • 08 - Polynomial Regression/003 Polynomial Regression in Python - Step 1b.mp419.74MB
  • 08 - Polynomial Regression/004 Polynomial Regression in Python - Step 2a.mp416.46MB
  • 08 - Polynomial Regression/005 Polynomial Regression in Python - Step 2b.mp417.97MB
  • 08 - Polynomial Regression/006 Polynomial Regression in Python - Step 3a.mp419.74MB
  • 08 - Polynomial Regression/007 Polynomial Regression in Python - Step 3b.mp418.29MB
  • 08 - Polynomial Regression/008 Polynomial Regression in Python - Step 4a.mp411.15MB
  • 08 - Polynomial Regression/009 Polynomial Regression in Python - Step 4b.mp49.03MB
  • 08 - Polynomial Regression/010 Polynomial Regression in R - Step 1a.mp416.84MB
  • 08 - Polynomial Regression/011 Polynomial Regression in R - Step 1b.mp413.7MB
  • 08 - Polynomial Regression/012 Polynomial Regression in R - Step 2a.mp414.45MB
  • 08 - Polynomial Regression/013 Polynomial Regression in R - Step 2b.mp423.87MB
  • 08 - Polynomial Regression/014 Polynomial Regression in R - Step 3a.mp420.72MB
  • 08 - Polynomial Regression/015 Polynomial Regression in R - Step 3b.mp419.55MB
  • 08 - Polynomial Regression/016 Polynomial Regression in R - Step 3c.mp416.17MB
  • 08 - Polynomial Regression/017 Polynomial Regression in R - Step 4a.mp414.99MB
  • 08 - Polynomial Regression/018 Polynomial Regression in R - Step 4b.mp414.25MB
  • 08 - Polynomial Regression/019 R Regression Template - Step 1.mp420.57MB
  • 08 - Polynomial Regression/020 R Regression Template - Step 2.mp413.55MB
  • 09 - Support Vector Regression (SVR)/001 SVR Intuition (Updated!).mp436.83MB
  • 09 - Support Vector Regression (SVR)/002 Heads-up on non-linear SVR.mp419.76MB
  • 09 - Support Vector Regression (SVR)/003 SVR in Python - Step 1a.mp412.06MB
  • 09 - Support Vector Regression (SVR)/004 SVR in Python - Step 1b.mp49.49MB
  • 09 - Support Vector Regression (SVR)/005 SVR in Python - Step 2a.mp417.15MB
  • 09 - Support Vector Regression (SVR)/006 SVR in Python - Step 2b.mp414.96MB
  • 09 - Support Vector Regression (SVR)/007 SVR in Python - Step 2c.mp48.54MB
  • 09 - Support Vector Regression (SVR)/008 SVR in Python - Step 3.mp426.92MB
  • 09 - Support Vector Regression (SVR)/009 SVR in Python - Step 4.mp410.87MB
  • 09 - Support Vector Regression (SVR)/010 SVR in Python - Step 5a.mp411.47MB
  • 09 - Support Vector Regression (SVR)/011 SVR in Python - Step 5b.mp424.71MB
  • 09 - Support Vector Regression (SVR)/012 SVR in R - Step 1.mp417.23MB
  • 09 - Support Vector Regression (SVR)/013 SVR in R - Step 2.mp413.44MB
  • 10 - Decision Tree Regression/001 Decision Tree Regression Intuition.mp423.24MB
  • 10 - Decision Tree Regression/002 Decision Tree Regression in Python - Step 1a.mp49.32MB
  • 10 - Decision Tree Regression/003 Decision Tree Regression in Python - Step 1b.mp411.06MB
  • 10 - Decision Tree Regression/004 Decision Tree Regression in Python - Step 2.mp412.15MB
  • 10 - Decision Tree Regression/005 Decision Tree Regression in Python - Step 3.mp48.38MB
  • 10 - Decision Tree Regression/006 Decision Tree Regression in Python - Step 4.mp411.67MB
  • 10 - Decision Tree Regression/007 Decision Tree Regression in R - Step 1.mp416.65MB
  • 10 - Decision Tree Regression/008 Decision Tree Regression in R - Step 2.mp464.4MB
  • 10 - Decision Tree Regression/009 Decision Tree Regression in R - Step 3.mp410.29MB
  • 10 - Decision Tree Regression/010 Decision Tree Regression in R - Step 4.mp411.67MB
  • 11 - Random Forest Regression/001 Random Forest Regression Intuition.mp435.79MB
  • 11 - Random Forest Regression/002 Random Forest Regression in Python - Step 1.mp417.5MB
  • 11 - Random Forest Regression/003 Random Forest Regression in Python - Step 2.mp429.17MB
  • 11 - Random Forest Regression/004 Random Forest Regression in R - Step 1.mp420.1MB
  • 11 - Random Forest Regression/005 Random Forest Regression in R - Step 2.mp421.6MB
  • 11 - Random Forest Regression/006 Random Forest Regression in R - Step 3.mp416.71MB
  • 12 - Evaluating Regression Models Performance/001 R-Squared Intuition.mp416.54MB
  • 12 - Evaluating Regression Models Performance/002 Adjusted R-Squared Intuition.mp411.57MB
  • 13 - Regression Model Selection in Python/002 Preparation of the Regression Code Templates - Step 1.mp410.4MB
  • 13 - Regression Model Selection in Python/003 Preparation of the Regression Code Templates - Step 2.mp421.93MB
  • 13 - Regression Model Selection in Python/004 Preparation of the Regression Code Templates - Step 3.mp413.75MB
  • 13 - Regression Model Selection in Python/005 Preparation of the Regression Code Templates - Step 4.mp424.93MB
  • 13 - Regression Model Selection in Python/006 THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! - STEP 1.mp422.75MB
  • 13 - Regression Model Selection in Python/007 THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! - STEP 2.mp428.83MB
  • 14 - Regression Model Selection in R/001 Evaluating Regression Models Performance - Homework's Final Part.mp427.71MB
  • 14 - Regression Model Selection in R/002 Interpreting Linear Regression Coefficients.mp452.6MB
  • 16 - Logistic Regression/001 What is Classification.mp45.58MB
  • 16 - Logistic Regression/002 Logistic Regression Intuition.mp424.75MB
  • 16 - Logistic Regression/003 Maximum Likelihood.mp47.11MB
  • 16 - Logistic Regression/004 Logistic Regression in Python - Step 1a.mp411.9MB
  • 16 - Logistic Regression/005 Logistic Regression in Python - Step 1b.mp49.27MB
  • 16 - Logistic Regression/006 Logistic Regression in Python - Step 2a.mp429.06MB
  • 16 - Logistic Regression/007 Logistic Regression in Python - Step 2b.mp432.82MB
  • 16 - Logistic Regression/008 Logistic Regression in Python - Step 3a.mp420.41MB
  • 16 - Logistic Regression/009 Logistic Regression in Python - Step 3b.mp47.77MB
  • 16 - Logistic Regression/010 Logistic Regression in Python - Step 4a.mp417.88MB
  • 16 - Logistic Regression/011 Logistic Regression in Python - Step 4b.mp44.48MB
  • 16 - Logistic Regression/012 Logistic Regression in Python - Step 5.mp418.25MB
  • 16 - Logistic Regression/013 Logistic Regression in Python - Step 6a.mp413.77MB
  • 16 - Logistic Regression/014 Logistic Regression in Python - Step 6b.mp412.07MB
  • 16 - Logistic Regression/015 Logistic Regression in Python - Step 7a.mp420.49MB
  • 16 - Logistic Regression/016 Logistic Regression in Python - Step 7b.mp424.02MB
  • 16 - Logistic Regression/017 Logistic Regression in Python - Step 7c.mp420.16MB
  • 16 - Logistic Regression/019 Logistic Regression in R - Step 1.mp419.25MB
  • 16 - Logistic Regression/020 Logistic Regression in R - Step 2.mp412.9MB
  • 16 - Logistic Regression/021 Logistic Regression in R - Step 3.mp427.03MB
  • 16 - Logistic Regression/022 Logistic Regression in R - Step 4.mp432.59MB
  • 16 - Logistic Regression/024 Logistic Regression in R - Step 5a.mp428.65MB
  • 16 - Logistic Regression/025 Logistic Regression in R - Step 5b.mp424.7MB
  • 16 - Logistic Regression/026 Logistic Regression in R - Step 5c.mp437.4MB
  • 16 - Logistic Regression/028 R Classification Template.mp425.48MB
  • 17 - K-Nearest Neighbors (K-NN)/001 K-Nearest Neighbor Intuition.mp410.46MB
  • 17 - K-Nearest Neighbors (K-NN)/002 K-NN in Python - Step 1.mp435.04MB
  • 17 - K-Nearest Neighbors (K-NN)/003 K-NN in Python - Step 2.mp433.58MB
  • 17 - K-Nearest Neighbors (K-NN)/004 K-NN in Python - Step 3.mp434.31MB
  • 17 - K-Nearest Neighbors (K-NN)/005 K-NN in R - Step 1.mp440.57MB
  • 17 - K-Nearest Neighbors (K-NN)/006 K-NN in R - Step 2.mp417.88MB
  • 17 - K-Nearest Neighbors (K-NN)/007 K-NN in R - Step 3.mp435.8MB
  • 18 - Support Vector Machine (SVM)/001 SVM Intuition.mp420.12MB
  • 18 - Support Vector Machine (SVM)/002 SVM in Python - Step 1.mp451.51MB
  • 18 - Support Vector Machine (SVM)/003 SVM in Python - Step 2.mp437.68MB
  • 18 - Support Vector Machine (SVM)/004 SVM in Python - Step 3.mp411.75MB
  • 18 - Support Vector Machine (SVM)/005 SVM in R - Step 1.mp451.91MB
  • 18 - Support Vector Machine (SVM)/006 SVM in R - Step 2.mp442.64MB
  • 19 - Kernel SVM/001 Kernel SVM Intuition.mp46.89MB
  • 19 - Kernel SVM/002 Mapping to a higher dimension.mp431.86MB
  • 19 - Kernel SVM/003 The Kernel Trick.mp433.55MB
  • 19 - Kernel SVM/004 Types of Kernel Functions.mp410.53MB
  • 19 - Kernel SVM/005 Non-Linear Kernel SVR (Advanced).mp427.49MB
  • 19 - Kernel SVM/006 Kernel SVM in Python - Step 1.mp436.88MB
  • 19 - Kernel SVM/007 Kernel SVM in Python - Step 2.mp435.48MB
  • 19 - Kernel SVM/008 Kernel SVM in R - Step 1.mp455.33MB
  • 19 - Kernel SVM/009 Kernel SVM in R - Step 2.mp418.8MB
  • 19 - Kernel SVM/010 Kernel SVM in R - Step 3.mp437.42MB
  • 20 - Naive Bayes/001 Bayes Theorem.mp4145.6MB
  • 20 - Naive Bayes/002 Naive Bayes Intuition.mp457.39MB
  • 20 - Naive Bayes/003 Naive Bayes Intuition (Challenge Reveal).mp411.81MB
  • 20 - Naive Bayes/004 Naive Bayes Intuition (Extras).mp416.11MB
  • 20 - Naive Bayes/005 Naive Bayes in Python - Step 1.mp452.57MB
  • 20 - Naive Bayes/006 Naive Bayes in Python - Step 2.mp442.02MB
  • 20 - Naive Bayes/007 Naive Bayes in Python - Step 3.mp46.67MB
  • 20 - Naive Bayes/008 Naive Bayes in R - Step 1.mp418.27MB
  • 20 - Naive Bayes/009 Naive Bayes in R - Step 2.mp422.68MB
  • 20 - Naive Bayes/010 Naive Bayes in R - Step 3.mp427.22MB
  • 21 - Decision Tree Classification/001 Decision Tree Classification Intuition.mp417.77MB
  • 21 - Decision Tree Classification/002 Decision Tree Classification in Python - Step 1.mp437.83MB
  • 21 - Decision Tree Classification/003 Decision Tree Classification in Python - Step 2.mp433.69MB
  • 21 - Decision Tree Classification/004 Decision Tree Classification in R - Step 1.mp457.77MB
  • 21 - Decision Tree Classification/005 Decision Tree Classification in R - Step 2.mp442.85MB
  • 21 - Decision Tree Classification/006 Decision Tree Classification in R - Step 3.mp421.72MB
  • 22 - Random Forest Classification/001 Random Forest Classification Intuition.mp441.56MB
  • 22 - Random Forest Classification/002 Random Forest Classification in Python - Step 1.mp434.92MB
  • 22 - Random Forest Classification/003 Random Forest Classification in Python - Step 2.mp432.83MB
  • 22 - Random Forest Classification/004 Random Forest Classification in R - Step 1.mp424.03MB
  • 22 - Random Forest Classification/005 Random Forest Classification in R - Step 2.mp438.98MB
  • 22 - Random Forest Classification/006 Random Forest Classification in R - Step 3.mp444MB
  • 23 - Classification Model Selection in Python/002 Confusion Matrix & Accuracy Ratios.mp428.7MB
  • 23 - Classification Model Selection in Python/003 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 1.mp421.03MB
  • 23 - Classification Model Selection in Python/004 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 2.mp433MB
  • 23 - Classification Model Selection in Python/005 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 3.mp421.68MB
  • 23 - Classification Model Selection in Python/006 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 4.mp48.1MB
  • 24 - Evaluating Classification Models Performance/001 False Positives & False Negatives.mp419.69MB
  • 24 - Evaluating Classification Models Performance/002 Accuracy Paradox.mp44.21MB
  • 24 - Evaluating Classification Models Performance/003 CAP Curve.mp418.98MB
  • 24 - Evaluating Classification Models Performance/004 CAP Curve Analysis.mp414.41MB
  • 26 - K-Means Clustering/001 What is Clustering (Supervised vs Unsupervised Learning).mp415.45MB
  • 26 - K-Means Clustering/002 K-Means Clustering Intuition.mp44.09MB
  • 26 - K-Means Clustering/003 The Elbow Method.mp47.5MB
  • 26 - K-Means Clustering/004 K-Means++.mp418.73MB
  • 26 - K-Means Clustering/005 K-Means Clustering in Python - Step 1a.mp410.62MB
  • 26 - K-Means Clustering/006 K-Means Clustering in Python - Step 1b.mp415.62MB
  • 26 - K-Means Clustering/007 K-Means Clustering in Python - Step 2a.mp413.51MB
  • 26 - K-Means Clustering/008 K-Means Clustering in Python - Step 2b.mp412.8MB
  • 26 - K-Means Clustering/009 K-Means Clustering in Python - Step 3a.mp413.15MB
  • 26 - K-Means Clustering/010 K-Means Clustering in Python - Step 3b.mp413.3MB
  • 26 - K-Means Clustering/011 K-Means Clustering in Python - Step 3c.mp49.53MB
  • 26 - K-Means Clustering/012 K-Means Clustering in Python - Step 4.mp416.46MB
  • 26 - K-Means Clustering/013 K-Means Clustering in Python - Step 5a.mp415.09MB
  • 26 - K-Means Clustering/014 K-Means Clustering in Python - Step 5b.mp435.71MB
  • 26 - K-Means Clustering/015 K-Means Clustering in Python - Step 5c.mp426.67MB
  • 26 - K-Means Clustering/016 K-Means Clustering in R - Step 1.mp415.13MB
  • 26 - K-Means Clustering/017 K-Means Clustering in R - Step 2.mp427.66MB
  • 27 - Hierarchical Clustering/001 Hierarchical Clustering Intuition.mp436.21MB
  • 27 - Hierarchical Clustering/002 Hierarchical Clustering How Dendrograms Work.mp416.44MB
  • 27 - Hierarchical Clustering/003 Hierarchical Clustering Using Dendrograms.mp425.19MB
  • 27 - Hierarchical Clustering/004 Hierarchical Clustering in Python - Step 1.mp420.44MB
  • 27 - Hierarchical Clustering/005 Hierarchical Clustering in Python - Step 2a.mp410.88MB
  • 27 - Hierarchical Clustering/006 Hierarchical Clustering in Python - Step 2b.mp422.28MB
  • 27 - Hierarchical Clustering/007 Hierarchical Clustering in Python - Step 2c.mp426.52MB
  • 27 - Hierarchical Clustering/008 Hierarchical Clustering in Python - Step 3a.mp417.12MB
  • 27 - Hierarchical Clustering/009 Hierarchical Clustering in Python - Step 3b.mp415.16MB
  • 27 - Hierarchical Clustering/010 Hierarchical Clustering in R - Step 1.mp47.75MB
  • 27 - Hierarchical Clustering/011 Hierarchical Clustering in R - Step 2.mp412.95MB
  • 27 - Hierarchical Clustering/012 Hierarchical Clustering in R - Step 3.mp431.33MB
  • 27 - Hierarchical Clustering/013 Hierarchical Clustering in R - Step 4.mp419.36MB
  • 27 - Hierarchical Clustering/014 Hierarchical Clustering in R - Step 5.mp413.83MB
  • 29 - Apriori/001 Apriori Intuition.mp456.18MB
  • 29 - Apriori/002 Apriori in Python - Step 1.mp458.33MB
  • 29 - Apriori/003 Apriori in Python - Step 2.mp482.25MB
  • 29 - Apriori/004 Apriori in Python - Step 3.mp439.45MB
  • 29 - Apriori/005 Apriori in Python - Step 4.mp4116.74MB
  • 29 - Apriori/006 Apriori in R - Step 1.mp473.9MB
  • 29 - Apriori/007 Apriori in R - Step 2.mp496.58MB
  • 29 - Apriori/008 Apriori in R - Step 3.mp4161.67MB
  • 30 - Eclat/001 Eclat Intuition.mp424.27MB
  • 30 - Eclat/002 Eclat in Python.mp456.2MB
  • 30 - Eclat/003 Eclat in R.mp465.3MB
  • 32 - Upper Confidence Bound (UCB)/001 The Multi-Armed Bandit Problem.mp496.44MB
  • 32 - Upper Confidence Bound (UCB)/002 Upper Confidence Bound (UCB) Intuition.mp479.23MB
  • 32 - Upper Confidence Bound (UCB)/003 Upper Confidence Bound in Python - Step 1.mp444.65MB
  • 32 - Upper Confidence Bound (UCB)/004 Upper Confidence Bound in Python - Step 2.mp49.01MB
  • 32 - Upper Confidence Bound (UCB)/005 Upper Confidence Bound in Python - Step 3.mp419.05MB
  • 32 - Upper Confidence Bound (UCB)/006 Upper Confidence Bound in Python - Step 4.mp441.63MB
  • 32 - Upper Confidence Bound (UCB)/007 Upper Confidence Bound in Python - Step 5.mp416.83MB
  • 32 - Upper Confidence Bound (UCB)/008 Upper Confidence Bound in Python - Step 6.mp419.05MB
  • 32 - Upper Confidence Bound (UCB)/009 Upper Confidence Bound in Python - Step 7.mp420.47MB
  • 32 - Upper Confidence Bound (UCB)/010 Upper Confidence Bound in R - Step 1.mp433.98MB
  • 32 - Upper Confidence Bound (UCB)/011 Upper Confidence Bound in R - Step 2.mp476.2MB
  • 32 - Upper Confidence Bound (UCB)/012 Upper Confidence Bound in R - Step 3.mp498.97MB
  • 32 - Upper Confidence Bound (UCB)/013 Upper Confidence Bound in R - Step 4.mp48.47MB
  • 33 - Thompson Sampling/001 Thompson Sampling Intuition.mp448.7MB
  • 33 - Thompson Sampling/002 Algorithm Comparison UCB vs Thompson Sampling.mp417.24MB
  • 33 - Thompson Sampling/003 Thompson Sampling in Python - Step 1.mp412.93MB
  • 33 - Thompson Sampling/004 Thompson Sampling in Python - Step 2.mp434.28MB
  • 33 - Thompson Sampling/005 Thompson Sampling in Python - Step 3.mp440.29MB
  • 33 - Thompson Sampling/006 Thompson Sampling in Python - Step 4.mp420.69MB
  • 33 - Thompson Sampling/008 Thompson Sampling in R - Step 1.mp459.32MB
  • 33 - Thompson Sampling/009 Thompson Sampling in R - Step 2.mp49.72MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/002 NLP Intuition.mp45.19MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/003 Types of Natural Language Processing.mp48.14MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/004 Classical vs Deep Learning Models.mp483.96MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/005 Bag-Of-Words Model.mp437.97MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/006 Natural Language Processing in Python - Step 1.mp414.9MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/007 Natural Language Processing in Python - Step 2.mp434.79MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/008 Natural Language Processing in Python - Step 3.mp428.2MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/009 Natural Language Processing in Python - Step 4.mp435.31MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/010 Natural Language Processing in Python - Step 5.mp482.52MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/011 Natural Language Processing in Python - Step 6.mp445.08MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/014 Natural Language Processing in R - Step 1.mp450.54MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/016 Natural Language Processing in R - Step 2.mp423.76MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/017 Natural Language Processing in R - Step 3.mp418.61MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/018 Natural Language Processing in R - Step 4.mp48.8MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/019 Natural Language Processing in R - Step 5.mp46.16MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/020 Natural Language Processing in R - Step 6.mp417.39MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/021 Natural Language Processing in R - Step 7.mp410.55MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/022 Natural Language Processing in R - Step 8.mp416.7MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/023 Natural Language Processing in R - Step 9.mp439.29MB
  • 34 - -------------------- Part 7 Natural Language Processing --------------------/024 Natural Language Processing in R - Step 10.mp466.5MB
  • 35 - -------------------- Part 8 Deep Learning --------------------/002 What is Deep Learning.mp4102.91MB
  • 36 - Artificial Neural Networks/001 Plan of attack.mp44.79MB
  • 36 - Artificial Neural Networks/002 The Neuron.mp444.1MB
  • 36 - Artificial Neural Networks/003 The Activation Function.mp417.25MB
  • 36 - Artificial Neural Networks/004 How do Neural Networks work.mp467.19MB
  • 36 - Artificial Neural Networks/005 How do Neural Networks learn.mp443.33MB
  • 36 - Artificial Neural Networks/006 Gradient Descent.mp425.66MB
  • 36 - Artificial Neural Networks/007 Stochastic Gradient Descent.mp426.82MB
  • 36 - Artificial Neural Networks/008 Backpropagation.mp414.01MB
  • 36 - Artificial Neural Networks/009 Business Problem Description.mp443.69MB
  • 36 - Artificial Neural Networks/010 ANN in Python - Step 1.mp450.84MB
  • 36 - Artificial Neural Networks/011 ANN in Python - Step 2.mp484.49MB
  • 36 - Artificial Neural Networks/012 ANN in Python - Step 3.mp438.57MB
  • 36 - Artificial Neural Networks/013 ANN in Python - Step 4.mp431.86MB
  • 36 - Artificial Neural Networks/014 ANN in Python - Step 5.mp475.21MB
  • 36 - Artificial Neural Networks/015 ANN in R - Step 1.mp4132.7MB
  • 36 - Artificial Neural Networks/016 ANN in R - Step 2.mp424.97MB
  • 36 - Artificial Neural Networks/017 ANN in R - Step 3.mp4115.66MB
  • 36 - Artificial Neural Networks/018 ANN in R - Step 4 (Last step).mp454.58MB
  • 37 - Convolutional Neural Networks/001 Plan of attack.mp46.23MB
  • 37 - Convolutional Neural Networks/002 What are convolutional neural networks.mp471.07MB
  • 37 - Convolutional Neural Networks/003 Step 1 - Convolution Operation.mp465.62MB
  • 37 - Convolutional Neural Networks/004 Step 1(b) - ReLU Layer.mp420.62MB
  • 37 - Convolutional Neural Networks/005 Step 2 - Pooling.mp487.48MB
  • 37 - Convolutional Neural Networks/006 Step 3 - Flattening.mp43.13MB
  • 37 - Convolutional Neural Networks/007 Step 4 - Full Connection.mp458.57MB
  • 37 - Convolutional Neural Networks/008 Summary.mp410.79MB
  • 37 - Convolutional Neural Networks/009 Softmax & Cross-Entropy.mp442.11MB
  • 37 - Convolutional Neural Networks/010 CNN in Python - Step 1.mp431.81MB
  • 37 - Convolutional Neural Networks/011 CNN in Python - Step 2.mp4100.06MB
  • 37 - Convolutional Neural Networks/012 CNN in Python - Step 3.mp464.3MB
  • 37 - Convolutional Neural Networks/013 CNN in Python - Step 4.mp422.65MB
  • 37 - Convolutional Neural Networks/014 CNN in Python - Step 5.mp484.9MB
  • 37 - Convolutional Neural Networks/015 CNN in Python - FINAL DEMO!.mp4112.1MB
  • 39 - Principal Component Analysis (PCA)/001 Principal Component Analysis (PCA) Intuition.mp421MB
  • 39 - Principal Component Analysis (PCA)/002 PCA in Python - Step 1.mp485.98MB
  • 39 - Principal Component Analysis (PCA)/003 PCA in Python - Step 2.mp420.75MB
  • 39 - Principal Component Analysis (PCA)/004 PCA in R - Step 1.mp4100.61MB
  • 39 - Principal Component Analysis (PCA)/005 PCA in R - Step 2.mp446.51MB
  • 39 - Principal Component Analysis (PCA)/006 PCA in R - Step 3.mp465.32MB
  • 40 - Linear Discriminant Analysis (LDA)/001 Linear Discriminant Analysis (LDA) Intuition.mp415.06MB
  • 40 - Linear Discriminant Analysis (LDA)/002 LDA in Python.mp475.41MB
  • 40 - Linear Discriminant Analysis (LDA)/003 LDA in R.mp493.68MB
  • 41 - Kernel PCA/001 Kernel PCA in Python.mp456.92MB
  • 41 - Kernel PCA/002 Kernel PCA in R.mp4228.81MB
  • 43 - Model Selection/001 k-Fold Cross Validation in Python.mp462.05MB
  • 43 - Model Selection/002 Grid Search in Python.mp4114.43MB
  • 43 - Model Selection/003 k-Fold Cross Validation in R.mp453.26MB
  • 43 - Model Selection/004 Grid Search in R.mp450.05MB
  • 44 - XGBoost/001 XGBoost in Python.mp484.25MB
  • 44 - XGBoost/003 XGBoost in R.mp469.37MB
  • 46 - Annex Logistic Regression (Long Explanation)/001 Logistic Regression Intuition.mp432.45MB



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