Section 1 : Welcome to the course!
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Lecture 1 | Applications of Machine Learning 2 | 00:03:15 Duration |
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Lecture 2 | BONUS #1 Learning Paths | |
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Lecture 3 | BONUS #2 ML vs. DL vs. AI - What’s the Difference | |
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Lecture 4 | BONUS #3 Regression Types | |
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Lecture 5 | Why Machine Learning is the Future | 00:06:33 Duration |
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Lecture 6 | Important notes, tips & tricks for this course | |
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Lecture 7 | This PDF resource will help you a lot! | |
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Lecture 8 | GET ALL THE CODES AND DATASETS HERE! | |
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Lecture 9 | Presentation of the ML A-Z folder, Colaboratory, J | 00:07:22 Duration |
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Lecture 10 | Installing R and R Studio (Mac, Linux & Windows) | 00:05:41 Duration |
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Lecture 11 | BONUS Meet your instructors | |
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Lecture 12 | Some Additional Resource | |
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Lecture 13 | FAQBot! | |
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Lecture 14 | Your Shortcut To Becoming A Better Data Scientist |
Section 2 : Part 1 Data Preprocessing
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Lecture 1 | 1. Welcome to Part 1 - Data Preprocessing |
Section 3 : Data Preprocessing in Python
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Lecture 1 | . Make sure you have your Machine Learning A-Z fol | |
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Lecture 2 | 2. Getting Started | 00:10:50 Duration |
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Lecture 3 | 3. Importing the Libraries | 00:03:34 Duration |
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Lecture 4 | 4. Importing the Dataset | 00:15:42 Duration |
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Lecture 5 | 5. For Python learners, summary of Object-oriented | |
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Lecture 6 | 6. Taking care of Missing Data | 00:12:15 Duration |
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Lecture 7 | 7. Encoding Categorical Data | 00:14:58 Duration |
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Lecture 8 | 8. Splitting the dataset into the Training set and | 00:13:47 Duration |
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Lecture 9 | 9. Feature Scaling | 00:01:35 Duration |
Section 4 : Data Preprocessing in R
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Lecture 1 | Welcome | |
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Lecture 2 | Getting Started | 00:01:35 Duration |
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Lecture 3 | 3. Make sure you have your dataset ready | |
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Lecture 4 | 4. Dataset Description | 00:01:58 Duration |
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Lecture 5 | 5. Importing the Dataset. | 00:02:45 Duration |
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Lecture 6 | 6. Taking care of Missing Data | 00:06:23 Duration |
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Lecture 7 | 7. Encoding Categorical Data | 00:06:02 Duration |
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Lecture 8 | 8. Splitting the dataset into the Training set and | 00:09:35 Duration |
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Lecture 9 | 9. Feature Scaling | 00:09:15 Duration |
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Lecture 10 | 10. Data Preprocessing Template | 00:05:15 Duration |
Section 5 : Part 2 Regression
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Lecture 1 | 1. Welcome to Part 2 - Regression |
Section 6 : Simple Linear Regression
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Lecture 1 | 1. Simple Linear Regression Intuition - Step 1 | 00:05:46 Duration |
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Lecture 2 | 2. Simple Linear Regression Intuition - Step 2 | 00:03:09 Duration |
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Lecture 3 | 3. Make sure you have your Machine Learning A-Z fo | |
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Lecture 4 | 4. Simple Linear Regression in Python - Step 1 | 00:12:48 Duration |
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Lecture 5 | 5. Simple Linear Regression in Python - Step 2 | 00:07:56 Duration |
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Lecture 6 | 6. Simple Linear Regression in Python - Step 3 | 00:04:35 Duration |
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Lecture 7 | Simple Linear Regression in Python - Step 4 | 00:12:56 Duration |
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Lecture 8 | 8. Simple Linear Regression in Python - BONUS | |
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Lecture 9 | 9. Simple Linear Regression in R - Step 1 | 00:04:40 Duration |
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Lecture 10 | 10. Simple Linear Regression in R - Step 2 | 00:05:59 Duration |
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Lecture 11 | 11. Simple Linear Regression in R - Step 3 | 00:03:39 Duration |
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Lecture 12 | 12. Simple Linear Regression in R - Step 4 | 00:15:56 Duration |
Section 7 : Multiple Linear Regression
Section 8 : Polynomial Regression
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Lecture 1 | 1. Polynomial Regression Intuition | 00:05:09 Duration |
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Lecture 2 | 2. Make sure you have your Machine Learning A-Z fo | |
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Lecture 3 | 3. Polynomial Regression in Python - Step 1 | 00:13:30 Duration |
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Lecture 4 | 4. Polynomial Regression in Python - Step 2 | 00:11:40 Duration |
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Lecture 5 | 5. Polynomial Regression in Python - Step 3 | 00:12:54 Duration |
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Lecture 6 | 6. Polynomial Regression in Python - Step 4 | 00:08:10 Duration |
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Lecture 7 | 7. Polynomial Regression in R - Step 1 | 00:09:13 Duration |
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Lecture 8 | 8. Polynomial Regression in R - Step 2 | 00:09:58 Duration |
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Lecture 9 | 9. Polynomial Regression in R - Step 3 | 00:19:55 Duration |
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Lecture 10 | 10. Polynomial Regression in R - Step 4 | 00:09:36 Duration |
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Lecture 11 | 11. R Regression Template | 00:11:58 Duration |
Section 9 : Support Vector Regression (SVR)
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Lecture 1 | SVR Intuition (Updated!) | 00:08:10 Duration |
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Lecture 2 | 2. Heads-up on non-linear SVR | 00:03:57 Duration |
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Lecture 3 | 3.1 Machine Learning A-Z (Codes and Datasets) | |
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Lecture 4 | 4. SVR in Python - Step 1 | 00:09:16 Duration |
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Lecture 5 | 5. SVR in Python - Step 2 | 00:15:10 Duration |
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Lecture 6 | 6. SVR in Python - Step 3 | 00:06:27 Duration |
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Lecture 7 | 7. SVR in Python - Step 4 | 00:08:01 Duration |
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Lecture 8 | 8. SVR in Python - Step 5 | 00:15:40 Duration |
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Lecture 9 | 9. SVR in R. | 00:11:44 Duration |
Section 10 : xDecision Tree Regression
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Lecture 1 | 1. Decision Tree Regression Intuition | 00:11:07 Duration |
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Lecture 2 | 2. Make sure you have your Machine Learning A-Z fo | |
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Lecture 3 | 3. Decision Tree Regression in Python - Step 1 | 00:08:39 Duration |
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Lecture 4 | Decision Tree Regression in Python - Step 2 | 00:05:00 Duration |
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Lecture 5 | 5. Decision Tree Regression in Python - Step 3 | 00:03:16 Duration |
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Lecture 6 | 6. Decision Tree Regression in Python - Step 4 | 00:09:50 Duration |
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Lecture 7 | 7. Decision Tree Regression in R | 00:19:54 Duration |
Section 11 : Random Forest Regression
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Lecture 1 | 1. Random Forest Regression Intuition | 00:06:44 Duration |
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Lecture 2 | 2. Make sure you have your Machine Learning A-Z fo | |
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Lecture 3 | 3. Random Forest Regression in Python | 00:13:23 Duration |
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Lecture 4 | 4. Random Forest Regression in R | 00:17:43 Duration |
Section 12 : Evaluating Regression Models Performance
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Lecture 1 | 1. R-Squared Intuition | 00:05:11 Duration |
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Lecture 2 | 2. Adjusted R-Squared Intuition | 00:09:57 Duration |
Section 13 : Regression Model Selection in Python
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Lecture 1 | 1. Make sure you have this Model Selection folder | |
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Lecture 2 | 2. Preparation of the Regression Code Templates | 00:19:26 Duration |
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Lecture 3 | 3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CO | 00:09:03 Duration |
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Lecture 4 | 4. Conclusion of Part 2 - Regression |
Section 14 : Regression Model Selection in R
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Lecture 1 | 1. Evaluating Regression Models Performance - Home | 00:08:54 Duration |
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Lecture 2 | Interpreting Linear Regression Coefficients | 00:09:16 Duration |
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Lecture 3 | 3. Conclusion of Part 2 - Regression |
Section 15 : Part 3 Classification
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Lecture 1 | 1. Welcome to Part 3 - Classification |
Section 16 : Logistic Regression
Section 17 : K-Nearest Neighbors (K-NN)
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Lecture 1 | K-Nearest Neighbor Intuition | 00:04:53 Duration |
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Lecture 2 | 2. Make sure you have your Machine Learning A-Z fo | |
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Lecture 3 | 3. K-NN in Python | 00:19:58 Duration |
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Lecture 4 | 4. K-NN in R | 00:15:47 Duration |
Section 18 : Support Vector Machine (SVM)
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Lecture 1 | 2. SVM Intuition | 00:09:49 Duration |
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Lecture 2 | 3. Make sure you have your Machine Learning A-Z fo | |
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Lecture 3 | 4. SVM in Python | 00:14:52 Duration |
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Lecture 4 | 5. SVM in R | 00:12:09 Duration |
Section 19 : Kernel SVM
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Lecture 1 | Kernel SVM Intuition | 00:03:17 Duration |
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Lecture 2 | 2. Mapping to a higher dimension | 00:07:50 Duration |
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Lecture 3 | 3. The Kernel Trick | 00:12:20 Duration |
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Lecture 4 | 4. Types of Kernel Functions | |
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Lecture 5 | 5. Non-Linear Kernel SVR (Advanced) | 00:10:55 Duration |
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Lecture 6 | 6. Make sure you have your Machine Learning A-Z fo | |
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Lecture 7 | 7. Kernel SVM in Python | 00:03:47 Duration |
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Lecture 8 | 8. Kernel SVM in R. | 00:16:34 Duration |
Section 20 : Naive Bayes
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Lecture 1 | 1. Bayes Theorem | 00:20:26 Duration |
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Lecture 2 | 2. Naive Bayes Intuition | 00:14:03 Duration |
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Lecture 3 | 3. Naive Bayes Intuition (Challenge Reveal) | |
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Lecture 4 | 4. Naive Bayes Intuition (Extras) | 00:09:42 Duration |
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Lecture 5 | 5. Make sure you have your Machine Learning A-Z fo | |
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Lecture 6 | 6. Naive Bayes in Python | 00:14:19 Duration |
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Lecture 7 | 7. Naive Bayes in R | 00:14:54 Duration |
Section 21 : Decision Tree Classification
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Lecture 1 | 1. Decision Tree Classification Intuition | |
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Lecture 2 | 2. Make sure you have your Machine Learning A-Z fo | |
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Lecture 3 | 3. Decision Tree Classification in Python | 00:14:03 Duration |
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Lecture 4 | 4. Decision Tree Classification in R | 00:19:48 Duration |
Section 22 : Random Forest Classification
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Lecture 1 | 1. Random Forest Classification Intuition | 00:04:29 Duration |
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Lecture 2 | 2. Make sure you have your Machine Learning A-Z fo | |
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Lecture 3 | 3. Random Forest Classification in Python | 00:13:28 Duration |
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Lecture 4 | 4. Random Forest Classification in R | 00:19:56 Duration |
Section 23 : Classification Model Selection in Python
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Lecture 1 | Make sure you have this Model Selection folder rea | |
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Lecture 2 | 2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATIO | 00:21:00 Duration |
Section 24 : Evaluating Classification Models Performance
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Lecture 1 | 1. False Positives & False Negatives | 00:07:58 Duration |
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Lecture 2 | 2. Confusion Matrix | 00:04:57 Duration |
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Lecture 3 | 3. Accuracy Paradox | 00:02:13 Duration |
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Lecture 4 | 4. CAP Curve | 00:11:16 Duration |
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Lecture 5 | 5. CAP Curve Analysis | 00:06:19 Duration |
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Lecture 6 | 6. Conclusion of Part 3 - Classification |
Section 25 : Part 4 Clustering
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Lecture 1 | 1. Welcome to Part 4 - Clustering |
Section 26 : K-Means Clustering
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Lecture 1 | 1. K-Means Clustering Intuition | 00:14:17 Duration |
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Lecture 2 | 2. K-Means Random Initialization Trap | 00:07:49 Duration |
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Lecture 3 | 3. K-Means Selecting The Number Of Clusters | 00:11:52 Duration |
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Lecture 4 | Make sure you have your Machine Learning A-Z fol | |
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Lecture 5 | 5. K-Means Clustering in Python - Step 1 | 00:08:25 Duration |
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Lecture 6 | 6. K-Means Clustering in Python - Step 2 | 00:10:36 Duration |
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Lecture 7 | 7. K-Means Clustering in Python - Step 3 | 00:16:58 Duration |
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Lecture 8 | 8. K-Means Clustering in Python - Step 4 | 00:06:44 Duration |
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Lecture 9 | 9. K-Means Clustering in Python - Step 5 | 00:19:35 Duration |
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Lecture 10 | 10. K-Means Clustering in R | 00:11:47 Duration |
Section 27 : Hierarchical Clustering
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Lecture 1 | 2. Hierarchical Clustering Intuition | 00:08:48 Duration |
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Lecture 2 | 3. Hierarchical Clustering How Dendrograms Work | 00:08:48 Duration |
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Lecture 3 | 4. Hierarchical Clustering Using Dendrograms | 00:11:22 Duration |
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Lecture 4 | 5. Make sure you have your Machine Learning A-Z fo | |
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Lecture 5 | 6. Hierarchical Clustering in Python - Step 1 | 00:06:57 Duration |
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Lecture 6 | 7. Hierarchical Clustering in Python - Step 2 | 00:17:12 Duration |
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Lecture 7 | 8. Hierarchical Clustering in Python - Step 3 | 00:12:20 Duration |
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Lecture 8 | 9. Hierarchical Clustering in R - Step 1 | 00:03:45 Duration |
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Lecture 9 | 10. Hierarchical Clustering in R - Step 2 | 00:05:24 Duration |
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Lecture 10 | 11. Hierarchical Clustering in R - Step 3 | 00:03:19 Duration |
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Lecture 11 | 12. Hierarchical Clustering in R - Step 4 | 00:02:46 Duration |
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Lecture 12 | 13. Hierarchical Clustering in R - Step 5 | 00:02:33 Duration |
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Lecture 13 | 15. Conclusion of Part 4 - Clustering |
Section 28 : Part 5 Association Rule Learning
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Lecture 1 | 1. Welcome to Part 5 - Association Rule Learning |
Section 29 : Apriori
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Lecture 1 | 1. Apriori Intuition | 00:18:14 Duration |
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Lecture 2 | 2. Make sure you have your Machine Learning A-Z fo | |
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Lecture 3 | 3. Apriori in Python - Step 1 | 00:08:46 Duration |
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Lecture 4 | 4. Apriori in Python - Step 2 | 00:17:07 Duration |
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Lecture 5 | 5. Apriori in Python - Step 3 | 00:12:49 Duration |
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Lecture 6 | 6. Apriori in Python - Step 4 | 00:19:41 Duration |
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Lecture 7 | Apriori in R - Step 1 | 00:19:53 Duration |
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Lecture 8 | 8. Apriori in R - Step 2 | 00:14:25 Duration |
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Lecture 9 | 9. Apriori in R - Step 3 | 00:19:18 Duration |
Section 30 : Eclat
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Lecture 1 | 1. Eclat Intuition | 00:06:05 Duration |
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Lecture 2 | .2. Make sure you have your Machine Learning A-Z f | |
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Lecture 3 | 3. Eclat in Python | 00:12:01 Duration |
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Lecture 4 | 4. Eclat in R | 00:10:09 Duration |
Section 31 : Part 6 Reinforcement Learning
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Lecture 1 | 1. Welcome to Part 6 - Reinforcement Learning |
Section 32 : Upper Confidence Bound (UCB)
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Lecture 1 | 1. The Multi-Armed Bandit Problem | 00:15:36 Duration |
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Lecture 2 | 2. Upper Confidence Bound (UCB) Intuition | 00:14:54 Duration |
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Lecture 3 | 3. Make sure you have your Machine Learning A-Z fo | |
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Lecture 4 | 4. Upper Confidence Bound in Python - Step 1 | 00:12:43 Duration |
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Lecture 5 | 5. Upper Confidence Bound in Python - Step 2 | 00:03:52 Duration |
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Lecture 6 | 6. Upper Confidence Bound in Python - Step 3 | 00:07:17 Duration |
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Lecture 7 | 7. Upper Confidence Bound in Python - Step 4 | 00:15:46 Duration |
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Lecture 8 | 8. Upper Confidence Bound in Python - Step 5 | 00:06:12 Duration |
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Lecture 9 | 9. Upper Confidence Bound in Python - Step 6 | 00:06:12 Duration |
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Lecture 10 | 10. Upper Confidence Bound in Python - Step 7. | 00:08:10 Duration |
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Lecture 11 | 11. Upper Confidence Bound in R - Step 1 | 00:13:39 Duration |
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Lecture 12 | 12. Upper Confidence Bound in R - Step 2 | 00:15:59 Duration |
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Lecture 13 | 13. Upper Confidence Bound in R - Step 3 | 00:17:38 Duration |
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Lecture 14 | 14. Upper Confidence Bound in R - Step 4 | 00:03:18 Duration |
Section 33 : Thompson Sampling
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Lecture 1 | Thompson Sampling Intuition | 00:19:12 Duration |
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Lecture 2 | 2. Algorithm Comparison UCB vs Thompson Sampling | 00:08:12 Duration |
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Lecture 3 | 3. Make sure you have your Machine Learning A-Z fo | |
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Lecture 4 | 4. Thompson Sampling in Python - Step 1 | 00:05:48 Duration |
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Lecture 5 | 5. Thompson Sampling in Python - Step | 00:12:20 Duration |
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Lecture 6 | 6. Thompson Sampling in Python - Step 3 | 00:14:04 Duration |
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Lecture 7 | 7. Thompson Sampling in Python - Step 4 | 00:07:45 Duration |
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Lecture 8 | 8. Additional Resource for this Section | |
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Lecture 9 | 9. Thompson Sampling in R - Step 1 | 00:14:04 Duration |
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Lecture 10 | 10. Thompson Sampling in R - Step 2 | 00:03:27 Duration |
Section 34 : Part 7 Natural Language Processing
Section 35 : Part 8 Deep Learning
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Lecture 1 | 1. Welcome to Part 8 - Deep Learning | |
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Lecture 2 | 2. What is Deep Learning | 00:12:34 Duration |
Section 36 : Artificial Neural Networks
Section 37 : Convolutional Neural Networks
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Lecture 1 | 1. Plan of attack | 00:03:32 Duration |
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Lecture 2 | 2. What are convolutional neural networks | 00:15:49 Duration |
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Lecture 3 | 3. Step 1 - Convolution Operation | 00:15:49 Duration |
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Lecture 4 | 4. Step 1(b) - ReLU Layer | 00:06:41 Duration |
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Lecture 5 | 5. Step 2 - Pooling | 00:14:13 Duration |
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Lecture 6 | 6. Step 3 - Flattening | 00:01:53 Duration |
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Lecture 7 | 7. Step 4 - Full Connection | 00:19:25 Duration |
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Lecture 8 | 8. Summary | 00:04:20 Duration |
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Lecture 9 | 9. Softmax & Cross-Entropy | 00:18:20 Duration |
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Lecture 10 | 10. Make sure you have your dataset ready | |
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Lecture 11 | 11. CNN in Python - Step 1 | 00:11:35 Duration |
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Lecture 12 | 12. CNN in Python - Step 2 | 00:17:46 Duration |
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Lecture 13 | 13. CNN in Python - Step 3 | 00:17:56 Duration |
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Lecture 14 | 14. CNN in Python - Step 4 | 00:07:21 Duration |
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Lecture 15 | 15. CNN in Python - Step 5 | 00:14:56 Duration |
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Lecture 16 | 16. CNN in Python - FINAL DEMO! | 00:23:38 Duration |
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Lecture 17 | 17. Deep Learning BONUS #2 |
Section 38 : Part 9 Dimensionality Reduction
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Lecture 1 | 1. Welcome to Part 9 - Dimensionality Reduction |
Section 39 : Principal Component Analysis (PCA)
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Lecture 1 | 1. Principal Component Analysis (PCA) Intuition | 00:03:49 Duration |
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Lecture 2 | 2. Make sure you have your Machine Learning A-Z fo | |
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Lecture 3 | 3. PCA in Python - Step 1 | 00:16:53 Duration |
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Lecture 4 | 4. PCA in Python - Step 2 | 00:05:30 Duration |
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Lecture 5 | 5. PCA in R - Step 1 | 00:12:08 Duration |
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Lecture 6 | 6. PCA in R - Step 2 | 00:11:22 Duration |
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Lecture 7 | 7. PCA in R - Step 3 | 00:13:43 Duration |
Section 40 : Linear Discriminant Analysis (LDA)
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Lecture 1 | 1. Linear Discriminant Analysis (LDA) Intuition | 00:03:50 Duration |
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Lecture 2 | 2. Make sure you have your Machine Learning A-Z fo | |
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Lecture 3 | 3. LDA in Python | 00:14:52 Duration |
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Lecture 4 | 4. LDA in R | 00:20:00 Duration |
Section 41 : Kernel PCA
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Lecture 1 | 1. Make sure you have your Machine Learning A-Z fo | |
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Lecture 2 | 2. Kernel PCA in Python | 00:11:03 Duration |
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Lecture 3 | 3. Kernel PCA in R | 00:20:30 Duration |
Section 42 : Part 10 Model Selection & Boosting
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Lecture 1 | 1. Welcome to Part 10 - Model Selection & Boosting |
Section 43 : Model Selection
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Lecture 1 | 1. Make sure you have your Machine Learning A-Z fo | |
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Lecture 2 | 2. k-Fold Cross Validation in Python | 00:17:55 Duration |
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Lecture 3 | 3. Grid Search in Python | 00:21:57 Duration |
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Lecture 4 | 4. k-Fold Cross Validation in R | 00:19:29 Duration |
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Lecture 5 | 5. Grid Search in R | 00:13:59 Duration |
Section 44 : XGBoost
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Lecture 1 | Make sure you have your Machine Learning A-Z folde | |
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Lecture 2 | 2. XGBoost in Python | 00:14:49 Duration |
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Lecture 3 | 3. Model Selection and Boosting BONUS | |
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Lecture 4 | 4. XGBoost in R | 00:18:14 Duration |
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Lecture 5 | 5. THANK YOU Bonus Video | 00:00:06 Duration |
Section 45 : Bonus Lectures
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Lecture 1 | YOUR SPECIAL BONUS |