Section 1 : Welcome to the course!
Lecture 1 Applications of Machine Learning 2 00:03:15 Duration
Lecture 2 Why Machine Learning is the Future 00:06:33 Duration
Lecture 3 Presentation of the ML A-Z folder, Colaboratory, J 00:07:22 Duration
Lecture 4 Installing R and R Studio (Mac, Linux & Windows) 00:05:41 Duration

Section 2 : Data Preprocessing in Python
Lecture 1 2. Getting Started 00:10:50 Duration
Lecture 2 3. Importing the Libraries 00:03:34 Duration
Lecture 3 4. Importing the Dataset 00:15:42 Duration
Lecture 4 6. Taking care of Missing Data 00:12:15 Duration
Lecture 5 7. Encoding Categorical Data 00:14:58 Duration
Lecture 6 8. Splitting the dataset into the Training set and 00:13:47 Duration
Lecture 7 9. Feature Scaling 00:01:35 Duration

Section 3 : Data Preprocessing in R
Lecture 1 Getting Started 00:01:35 Duration
Lecture 2 4. Dataset Description 00:01:58 Duration
Lecture 3 5. Importing the Dataset. 00:02:45 Duration
Lecture 4 6. Taking care of Missing Data 00:06:23 Duration
Lecture 5 7. Encoding Categorical Data 00:06:02 Duration
Lecture 6 8. Splitting the dataset into the Training set and 00:09:35 Duration
Lecture 7 9. Feature Scaling 00:09:15 Duration
Lecture 8 10. Data Preprocessing Template 00:05:15 Duration

Section 4 : Simple Linear Regression
Lecture 1 1. Simple Linear Regression Intuition - Step 1 00:05:46 Duration
Lecture 2 2. Simple Linear Regression Intuition - Step 2 00:03:09 Duration
Lecture 3 4. Simple Linear Regression in Python - Step 1 00:12:48 Duration
Lecture 4 5. Simple Linear Regression in Python - Step 2 00:07:56 Duration
Lecture 5 6. Simple Linear Regression in Python - Step 3 00:04:35 Duration
Lecture 6 Simple Linear Regression in Python - Step 4 00:12:56 Duration
Lecture 7 9. Simple Linear Regression in R - Step 1 00:04:40 Duration
Lecture 8 10. Simple Linear Regression in R - Step 2 00:05:59 Duration
Lecture 9 11. Simple Linear Regression in R - Step 3 00:03:39 Duration
Lecture 10 12. Simple Linear Regression in R - Step 4 00:15:56 Duration

Section 5 : Multiple Linear Regression
Lecture 1 1. Dataset + Business Problem Description 00:03:44 Duration
Lecture 2 2. Multiple Linear Regression Intuition - Step 1 00:01:03 Duration
Lecture 3 3. Multiple Linear Regression Intuition - Step 2 00:01:00 Duration
Lecture 4 4. Multiple Linear Regression Intuition - Step 3 00:07:21 Duration
Lecture 5 5. Multiple Linear Regression Intuition - Step 4 00:02:11 Duration
Lecture 6 6. Understanding the P-Value 00:11:45 Duration
Lecture 7 7. Multiple Linear Regression Intuition - Step 00:15:41 Duration
Lecture 8 9. Multiple Linear Regression in Python - Step 1 00:08:30 Duration
Lecture 9 10. Multiple Linear Regression in Python - Step 2 00:09:12 Duration
Lecture 10 11. Multiple Linear Regression in Python - Step 3 00:10:37 Duration
Lecture 11 12. Multiple Linear Regression in Python - Step 4 00:11:45 Duration
Lecture 12 15. Multiple Linear Regression in R - Step 1 00:07:51 Duration
Lecture 13 16. Multiple Linear Regression in R - Step 2 00:10:26 Duration
Lecture 14 17. Multiple Linear Regression in R - Step 3 00:04:27 Duration
Lecture 15 18. Multiple Linear Regression in R - Backward Eli 00:17:51 Duration
Lecture 16 19. Multiple Linear Regression in R - Backward Eli 00:07:34 Duration

Section 6 : Polynomial Regression
Lecture 1 1. Polynomial Regression Intuition 00:05:09 Duration
Lecture 2 3. Polynomial Regression in Python - Step 1 00:13:30 Duration
Lecture 3 4. Polynomial Regression in Python - Step 2 00:11:40 Duration
Lecture 4 5. Polynomial Regression in Python - Step 3 00:12:54 Duration
Lecture 5 6. Polynomial Regression in Python - Step 4 00:08:10 Duration
Lecture 6 7. Polynomial Regression in R - Step 1 00:09:13 Duration
Lecture 7 8. Polynomial Regression in R - Step 2 00:09:58 Duration
Lecture 8 9. Polynomial Regression in R - Step 3 00:19:55 Duration
Lecture 9 10. Polynomial Regression in R - Step 4 00:09:36 Duration
Lecture 10 11. R Regression Template 00:11:58 Duration

Section 7 : Support Vector Regression (SVR)
Lecture 1 SVR Intuition (Updated!) 00:08:10 Duration
Lecture 2 2. Heads-up on non-linear SVR 00:03:57 Duration
Lecture 3 4. SVR in Python - Step 1 00:09:16 Duration
Lecture 4 5. SVR in Python - Step 2 00:15:10 Duration
Lecture 5 6. SVR in Python - Step 3 00:06:27 Duration
Lecture 6 7. SVR in Python - Step 4 00:08:01 Duration
Lecture 7 8. SVR in Python - Step 5 00:15:40 Duration
Lecture 8 9. SVR in R. 00:11:44 Duration

Section 8 : xDecision Tree Regression
Lecture 1 1. Decision Tree Regression Intuition 00:11:07 Duration
Lecture 2 3. Decision Tree Regression in Python - Step 1 00:08:39 Duration
Lecture 3 Decision Tree Regression in Python - Step 2 00:05:00 Duration
Lecture 4 5. Decision Tree Regression in Python - Step 3 00:03:16 Duration
Lecture 5 6. Decision Tree Regression in Python - Step 4 00:09:50 Duration
Lecture 6 7. Decision Tree Regression in R 00:19:54 Duration

Section 9 : Random Forest Regression
Lecture 1 1. Random Forest Regression Intuition 00:06:44 Duration
Lecture 2 3. Random Forest Regression in Python 00:13:23 Duration
Lecture 3 4. Random Forest Regression in R 00:17:43 Duration

Section 10 : Evaluating Regression Models Performance
Lecture 1 1. R-Squared Intuition 00:05:11 Duration
Lecture 2 2. Adjusted R-Squared Intuition 00:09:57 Duration

Section 11 : Regression Model Selection in Python
Lecture 1 2. Preparation of the Regression Code Templates 00:19:26 Duration
Lecture 2 3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CO 00:09:03 Duration

Section 12 : Regression Model Selection in R
Lecture 1 1. Evaluating Regression Models Performance - Home 00:08:54 Duration
Lecture 2 Interpreting Linear Regression Coefficients 00:09:16 Duration

Section 13 : Logistic Regression
Lecture 1 1. Logistic Regression Intuition 00:17:07 Duration
Lecture 2 3. Logistic Regression in Python - Step 1 00:09:43 Duration
Lecture 3 4. Logistic Regression in Python - Step 2 00:13:38 Duration
Lecture 4 5. Logistic Regression in Python - Step 3 00:07:40 Duration
Lecture 5 6. Logistic Regression in Python - Step 4 00:07:49 Duration
Lecture 6 7. Logistic Regression in Python - Step 5 00:06:15 Duration
Lecture 7 8. Logistic Regression in Python - Step 6 00:09:26 Duration
Lecture 8 9. Logistic Regression in Python - Step 7 00:16:06 Duration
Lecture 9 10. Logistic Regression in R - Step 1 00:05:59 Duration
Lecture 10 11. Logistic Regression in R - Step 2. 00:02:59 Duration
Lecture 11 12. Logistic Regression in R - Step 3 00:05:23 Duration
Lecture 12 13. Logistic Regression in R - Step 4 00:02:48 Duration
Lecture 13 15. Logistic Regression in R - Step 5 00:19:24 Duration
Lecture 14 16. R Classification Template 00:04:17 Duration

Section 14 : K-Nearest Neighbors (K-NN)
Lecture 1 K-Nearest Neighbor Intuition 00:04:53 Duration
Lecture 2 3. K-NN in Python 00:19:58 Duration
Lecture 3 4. K-NN in R 00:15:47 Duration

Section 15 : Support Vector Machine (SVM)
Lecture 1 2. SVM Intuition 00:09:49 Duration
Lecture 2 4. SVM in Python 00:14:52 Duration
Lecture 3 5. SVM in R 00:12:09 Duration

Section 16 : Kernel SVM
Lecture 1 Kernel SVM Intuition 00:03:17 Duration
Lecture 2 2. Mapping to a higher dimension 00:07:50 Duration
Lecture 3 3. The Kernel Trick 00:12:20 Duration
Lecture 4 4. Types of Kernel Functions
Lecture 5 5. Non-Linear Kernel SVR (Advanced) 00:10:55 Duration
Lecture 6 7. Kernel SVM in Python 00:03:47 Duration
Lecture 7 8. Kernel SVM in R. 00:16:34 Duration

Section 17 : Naive Bayes
Lecture 1 1. Bayes Theorem 00:20:26 Duration
Lecture 2 2. Naive Bayes Intuition 00:14:03 Duration
Lecture 3 3. Naive Bayes Intuition (Challenge Reveal)
Lecture 4 4. Naive Bayes Intuition (Extras) 00:09:42 Duration
Lecture 5 6. Naive Bayes in Python 00:14:19 Duration
Lecture 6 7. Naive Bayes in R 00:14:54 Duration

Section 18 : Decision Tree Classification
Lecture 1 1. Decision Tree Classification Intuition
Lecture 2 3. Decision Tree Classification in Python 00:14:03 Duration
Lecture 3 4. Decision Tree Classification in R 00:19:48 Duration

Section 19 : Random Forest Classification
Lecture 1 1. Random Forest Classification Intuition 00:04:29 Duration
Lecture 2 3. Random Forest Classification in Python 00:13:28 Duration
Lecture 3 4. Random Forest Classification in R 00:19:56 Duration

Section 20 : Classification Model Selection in Python
Lecture 1 2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATIO 00:21:00 Duration

Section 21 : Evaluating Classification Models Performance
Lecture 1 1. False Positives & False Negatives 00:07:58 Duration
Lecture 2 2. Confusion Matrix 00:04:57 Duration
Lecture 3 3. Accuracy Paradox 00:02:13 Duration
Lecture 4 4. CAP Curve 00:11:16 Duration
Lecture 5 5. CAP Curve Analysis 00:06:19 Duration

Section 22 : K-Means Clustering
Lecture 1 1. K-Means Clustering Intuition 00:14:17 Duration
Lecture 2 2. K-Means Random Initialization Trap 00:07:49 Duration
Lecture 3 3. K-Means Selecting The Number Of Clusters 00:11:52 Duration
Lecture 4 5. K-Means Clustering in Python - Step 1 00:08:25 Duration
Lecture 5 6. K-Means Clustering in Python - Step 2 00:10:36 Duration
Lecture 6 7. K-Means Clustering in Python - Step 3 00:16:58 Duration
Lecture 7 8. K-Means Clustering in Python - Step 4 00:06:44 Duration
Lecture 8 9. K-Means Clustering in Python - Step 5 00:19:35 Duration
Lecture 9 10. K-Means Clustering in R 00:11:47 Duration

Section 23 : Hierarchical Clustering
Lecture 1 2. Hierarchical Clustering Intuition 00:08:48 Duration
Lecture 2 3. Hierarchical Clustering How Dendrograms Work 00:08:48 Duration
Lecture 3 4. Hierarchical Clustering Using Dendrograms 00:11:22 Duration
Lecture 4 6. Hierarchical Clustering in Python - Step 1 00:06:57 Duration
Lecture 5 7. Hierarchical Clustering in Python - Step 2 00:17:12 Duration
Lecture 6 8. Hierarchical Clustering in Python - Step 3 00:12:20 Duration
Lecture 7 9. Hierarchical Clustering in R - Step 1 00:03:45 Duration
Lecture 8 10. Hierarchical Clustering in R - Step 2 00:05:24 Duration
Lecture 9 11. Hierarchical Clustering in R - Step 3 00:03:19 Duration
Lecture 10 12. Hierarchical Clustering in R - Step 4 00:02:46 Duration
Lecture 11 13. Hierarchical Clustering in R - Step 5 00:02:33 Duration

Section 24 : Apriori
Lecture 1 1. Apriori Intuition 00:18:14 Duration
Lecture 2 3. Apriori in Python - Step 1 00:08:46 Duration
Lecture 3 4. Apriori in Python - Step 2 00:17:07 Duration
Lecture 4 5. Apriori in Python - Step 3 00:12:49 Duration
Lecture 5 6. Apriori in Python - Step 4 00:19:41 Duration
Lecture 6 Apriori in R - Step 1 00:19:53 Duration
Lecture 7 8. Apriori in R - Step 2 00:14:25 Duration
Lecture 8 9. Apriori in R - Step 3 00:19:18 Duration

Section 25 : Eclat
Lecture 1 1. Eclat Intuition 00:06:05 Duration
Lecture 2 3. Eclat in Python 00:12:01 Duration
Lecture 3 4. Eclat in R 00:10:09 Duration

Section 26 : Upper Confidence Bound (UCB)
Lecture 1 1. The Multi-Armed Bandit Problem 00:15:36 Duration
Lecture 2 2. Upper Confidence Bound (UCB) Intuition 00:14:54 Duration
Lecture 3 4. Upper Confidence Bound in Python - Step 1 00:12:43 Duration
Lecture 4 5. Upper Confidence Bound in Python - Step 2 00:03:52 Duration
Lecture 5 6. Upper Confidence Bound in Python - Step 3 00:07:17 Duration
Lecture 6 7. Upper Confidence Bound in Python - Step 4 00:15:46 Duration
Lecture 7 8. Upper Confidence Bound in Python - Step 5 00:06:12 Duration
Lecture 8 9. Upper Confidence Bound in Python - Step 6 00:06:12 Duration
Lecture 9 10. Upper Confidence Bound in Python - Step 7. 00:08:10 Duration
Lecture 10 11. Upper Confidence Bound in R - Step 1 00:13:39 Duration
Lecture 11 12. Upper Confidence Bound in R - Step 2 00:15:59 Duration
Lecture 12 13. Upper Confidence Bound in R - Step 3 00:17:38 Duration
Lecture 13 14. Upper Confidence Bound in R - Step 4 00:03:18 Duration

Section 27 : Thompson Sampling
Lecture 1 Thompson Sampling Intuition 00:19:12 Duration
Lecture 2 2. Algorithm Comparison UCB vs Thompson Sampling 00:08:12 Duration
Lecture 3 4. Thompson Sampling in Python - Step 1 00:05:48 Duration
Lecture 4 5. Thompson Sampling in Python - Step 00:12:20 Duration
Lecture 5 6. Thompson Sampling in Python - Step 3 00:14:04 Duration
Lecture 6 7. Thompson Sampling in Python - Step 4 00:07:45 Duration
Lecture 7 9. Thompson Sampling in R - Step 1 00:14:04 Duration
Lecture 8 10. Thompson Sampling in R - Step 2 00:03:27 Duration

Section 28 : Part 7 Natural Language Processing

Lecture 1 2. NLP Intuition 00:03:03 Duration
Lecture 2 3. Types of Natural Language Processing 00:04:11 Duration
Lecture 3 4. Classical vs Deep Learning Models 00:11:23 Duration
Lecture 4 5. Bag-Of-Words Model 00:17:06 Duration
Lecture 5 7. Natural Language Processing in Python - Step 1 00:07:13 Duration
Lecture 6 8. Natural Language Processing in Python - Step 2 00:06:46 Duration
Lecture 7 9. Natural Language Processing in Python - Step 3 00:12:54 Duration
Lecture 8 10. Natural Language Processing in Python - Step 4 00:11:01 Duration
Lecture 9 11. Natural Language Processing in Python - Step 5 00:17:24 Duration
Lecture 10 12. Natural Language Processing in Python - Step 6 00:09:53 Duration
Lecture 11 15. Natural Language Processing in R - Step 1 00:16:35 Duration
Lecture 12 16. Natural Language Processing in R - Step 2 00:08:39 Duration
Lecture 13 17. Natural Language Processing in R - Step 3 00:06:28 Duration
Lecture 14 18. Natural Language Processing in R - Step 4 00:02:58 Duration
Lecture 15 19. Natural Language Processing in R - Step 5 00:02:05 Duration
Lecture 16 20. Natural Language Processing in R - Step 6 00:05:49 Duration
Lecture 17 21. Natural Language Processing in R - Step 7 00:03:27 Duration
Lecture 18 22. Natural Language Processing in R - Step 00:05:21 Duration
Lecture 19 23. Natural Language Processing in R - Step 9 00:12:51 Duration
Lecture 20 24. Natural Language Processing in R - Step 10 00:17:31 Duration

Section 29 : Part 8 Deep Learning

Lecture 1 2. What is Deep Learning 00:12:34 Duration

Section 30 : Artificial Neural Networks
Lecture 1 1. Plan of attack 00:02:52 Duration
Lecture 2 2. The Neuron 00:16:25 Duration
Lecture 3 3. The Activation Function 00:08:29 Duration
Lecture 4 4. How do Neural Networks work 00:12:48 Duration
Lecture 5 5. How do Neural Networks learn 00:12:59 Duration
Lecture 6 6. Gradient Descent 00:10:13 Duration
Lecture 7 6. Gradient Descent 00:10:13 Duration
Lecture 8 7. Stochastic Gradient Descent 00:08:45 Duration
Lecture 9 8. Backpropagation 00:05:22 Duration
Lecture 10 9. Business Problem Description 00:04:59 Duration
Lecture 11 1. ANN in Python - Step 1. 00:10:21 Duration
Lecture 12 13. ANN in Python - Step 2 00:18:37 Duration
Lecture 13 14. ANN in Python - Step 3 00:14:28 Duration
Lecture 14 15. ANN in Python - Step 4 00:11:58 Duration
Lecture 15 16. ANN in Python - Step 5
Lecture 16 17. ANN in R - Step 1 00:17:17 Duration
Lecture 17 18. ANN in R - Step 2 00:06:30 Duration
Lecture 18 19. ANN in R - Step 3 00:12:30 Duration
Lecture 19 20. ANN in R - Step 4 (Last step)

Section 31 : Convolutional Neural Networks
Lecture 1 1. Plan of attack 00:03:32 Duration
Lecture 2 2. What are convolutional neural networks 00:15:49 Duration
Lecture 3 3. Step 1 - Convolution Operation 00:15:49 Duration
Lecture 4 4. Step 1(b) - ReLU Layer 00:06:41 Duration
Lecture 5 5. Step 2 - Pooling 00:14:13 Duration
Lecture 6 6. Step 3 - Flattening 00:01:53 Duration
Lecture 7 7. Step 4 - Full Connection 00:19:25 Duration
Lecture 8 8. Summary 00:04:20 Duration
Lecture 9 9. Softmax & Cross-Entropy 00:18:20 Duration
Lecture 10 11. CNN in Python - Step 1 00:11:35 Duration
Lecture 11 12. CNN in Python - Step 2 00:17:46 Duration
Lecture 12 13. CNN in Python - Step 3 00:17:56 Duration
Lecture 13 14. CNN in Python - Step 4 00:07:21 Duration
Lecture 14 15. CNN in Python - Step 5 00:14:56 Duration
Lecture 15 16. CNN in Python - FINAL DEMO! 00:23:38 Duration

Section 32 : Principal Component Analysis (PCA)
Lecture 1 1. Principal Component Analysis (PCA) Intuition 00:03:49 Duration
Lecture 2 3. PCA in Python - Step 1 00:16:53 Duration
Lecture 3 4. PCA in Python - Step 2 00:05:30 Duration
Lecture 4 5. PCA in R - Step 1 00:12:08 Duration
Lecture 5 6. PCA in R - Step 2 00:11:22 Duration
Lecture 6 7. PCA in R - Step 3 00:13:43 Duration

Section 33 : Linear Discriminant Analysis (LDA)
Lecture 1 1. Linear Discriminant Analysis (LDA) Intuition 00:03:50 Duration
Lecture 2 3. LDA in Python 00:14:52 Duration
Lecture 3 4. LDA in R 00:20:00 Duration

Section 34 : Kernel PCA
Lecture 1 2. Kernel PCA in Python 00:11:03 Duration
Lecture 2 3. Kernel PCA in R 00:20:30 Duration

Section 35 : Model Selection
Lecture 1 2. k-Fold Cross Validation in Python 00:17:55 Duration
Lecture 2 3. Grid Search in Python 00:21:57 Duration
Lecture 3 4. k-Fold Cross Validation in R 00:19:29 Duration
Lecture 4 5. Grid Search in R 00:13:59 Duration

Section 36 : XGBoost
Lecture 1 2. XGBoost in Python 00:14:49 Duration
Lecture 2 4. XGBoost in R 00:18:14 Duration
Lecture 3 5. THANK YOU Bonus Video 00:00:06 Duration