Section 1 : Welcome to the course!

 		lecture 1 Applications of Machine Learning 2 3:15
lecture 2 BONUS #1 Learning Paths Text
lecture 3 BONUS #2 ML vs. DL vs. AI - What’s the Difference Text
lecture 4 BONUS #3 Regression Types Text
lecture 5 Why Machine Learning is the Future 6:33
lecture 6 Important notes, tips & tricks for this course Text
lecture 7 This PDF resource will help you a lot! Text
lecture 8 GET ALL THE CODES AND DATASETS HERE! Text
lecture 9 Presentation of the ML A-Z folder, Colaboratory, J 7:22
lecture 10 Installing R and R Studio (Mac, Linux & Windows) 5:41
lecture 11 BONUS Meet your instructors Text
lecture 12 Some Additional Resource Text
lecture 13 FAQBot! Text
lecture 14 Your Shortcut To Becoming A Better Data Scientist Text

Section 2 : Part 1 Data Preprocessing

 		lecture 15 1. Welcome to Part 1 - Data Preprocessing Text

Section 3 : Data Preprocessing in Python

 		lecture 16 . Make sure you have your Machine Learning A-Z fol Text
lecture 17 2. Getting Started 10:50
lecture 18 3. Importing the Libraries 3:34
lecture 19 4. Importing the Dataset 15:42
lecture 20 5. For Python learners, summary of Object-oriented Text
lecture 21 6. Taking care of Missing Data 12:15
lecture 22 7. Encoding Categorical Data 14:58
lecture 23 8. Splitting the dataset into the Training set and 13:47
lecture 24 9. Feature Scaling 1:35

Section 4 : Data Preprocessing in R

 		lecture 25 Welcome Text
lecture 26 Getting Started 1:35
lecture 27 3. Make sure you have your dataset ready Text
lecture 28 4. Dataset Description 1:58
lecture 29 5. Importing the Dataset. 2:45
lecture 30 6. Taking care of Missing Data 6:23
lecture 31 7. Encoding Categorical Data 6:2
lecture 32 8. Splitting the dataset into the Training set and 9:35
lecture 33 9. Feature Scaling 9:15
lecture 34 10. Data Preprocessing Template 5:15

Section 5 : Part 2 Regression

lecture 35 1. Welcome to Part 2 - Regression Text

Section 6 : Simple Linear Regression

 		lecture 36 1. Simple Linear Regression Intuition - Step 1 5:46
lecture 37 2. Simple Linear Regression Intuition - Step 2 3:9
lecture 38 3. Make sure you have your Machine Learning A-Z fo Text
lecture 39 4. Simple Linear Regression in Python - Step 1 12:48
lecture 40 5. Simple Linear Regression in Python - Step 2 7:56
lecture 41 6. Simple Linear Regression in Python - Step 3 4:35
lecture 42 Simple Linear Regression in Python - Step 4 12:56
lecture 43 8. Simple Linear Regression in Python - BONUS Text
lecture 44 9. Simple Linear Regression in R - Step 1 4:40
lecture 45 10. Simple Linear Regression in R - Step 2 5:59
lecture 46 11. Simple Linear Regression in R - Step 3 3:39
lecture 47 12. Simple Linear Regression in R - Step 4 15:56

Section 7 : Multiple Linear Regression

 		lecture 48 1. Dataset + Business Problem Description 3:44
lecture 49 2. Multiple Linear Regression Intuition - Step 1 1:3
lecture 50 3. Multiple Linear Regression Intuition - Step 2 1:0
lecture 51 4. Multiple Linear Regression Intuition - Step 3 7:21
lecture 52 5. Multiple Linear Regression Intuition - Step 4 2:11
lecture 53 6. Understanding the P-Value 11:45
lecture 54 7. Multiple Linear Regression Intuition - Step 15:41
lecture 55 8. Make sure you have your Machine Learning A-Z fo Text
lecture 56 9. Multiple Linear Regression in Python - Step 1 8:30
lecture 57 10. Multiple Linear Regression in Python - Step 2 9:12
lecture 58 11. Multiple Linear Regression in Python - Step 3 10:37
lecture 59 12. Multiple Linear Regression in Python - Step 4 11:45
lecture 60 13. Multiple Linear Regression in Python - Backwar Text
lecture 61 14. Multiple Linear Regression in Python - BONUS Text
lecture 62 15. Multiple Linear Regression in R - Step 1 7:51
lecture 63 16. Multiple Linear Regression in R - Step 2 10:26
lecture 64 17. Multiple Linear Regression in R - Step 3 4:27
lecture 65 18. Multiple Linear Regression in R - Backward Eli 17:51
lecture 66 19. Multiple Linear Regression in R - Backward Eli 7:34
lecture 67 20. Multiple Linear Regression in R - Automatic Ba Text

Section 8 : Polynomial Regression

 		lecture 68 1. Polynomial Regression Intuition 5:9
lecture 69 2. Make sure you have your Machine Learning A-Z fo Text
lecture 70 3. Polynomial Regression in Python - Step 1 13:30
lecture 71 4. Polynomial Regression in Python - Step 2 11:40
lecture 72 5. Polynomial Regression in Python - Step 3 12:54
lecture 73 6. Polynomial Regression in Python - Step 4 8:10
lecture 74 7. Polynomial Regression in R - Step 1 9:13
lecture 75 8. Polynomial Regression in R - Step 2 9:58
lecture 76 9. Polynomial Regression in R - Step 3 19:55
lecture 77 10. Polynomial Regression in R - Step 4 9:36
lecture 78 11. R Regression Template 11:58

Section 9 : Support Vector Regression (SVR)

 		lecture 79 SVR Intuition (Updated!) 8:10
lecture 80 2. Heads-up on non-linear SVR 3:57
lecture 81 3.1 Machine Learning A-Z (Codes and Datasets) Zip
lecture 82 4. SVR in Python - Step 1 9:16
lecture 83 5. SVR in Python - Step 2 15:10
lecture 84 6. SVR in Python - Step 3 6:27
lecture 85 7. SVR in Python - Step 4 8:1
lecture 86 8. SVR in Python - Step 5 15:40
lecture 87 9. SVR in R. 11:44

Section 10 : xDecision Tree Regression

 		lecture 88 1. Decision Tree Regression Intuition 11:7
lecture 89 2. Make sure you have your Machine Learning A-Z fo Text
lecture 90 3. Decision Tree Regression in Python - Step 1 8:39
lecture 91 Decision Tree Regression in Python - Step 2 5:0
lecture 92 5. Decision Tree Regression in Python - Step 3 3:16
lecture 93 6. Decision Tree Regression in Python - Step 4 9:50
lecture 94 7. Decision Tree Regression in R 19:54

Section 11 : Random Forest Regression

 		lecture 95 1. Random Forest Regression Intuition 6:44
lecture 96 2. Make sure you have your Machine Learning A-Z fo Text
lecture 97 3. Random Forest Regression in Python 13:23
lecture 98 4. Random Forest Regression in R 17:43

Section 12 : Evaluating Regression Models Performance

 		lecture 99 1. R-Squared Intuition 5:11
lecture 100 2. Adjusted R-Squared Intuition 9:57

Section 13 : Regression Model Selection in Python

 		lecture 101 1. Make sure you have this Model Selection folder Text
lecture 102 2. Preparation of the Regression Code Templates 19:26
lecture 103 3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CO 9:3
lecture 104 4. Conclusion of Part 2 - Regression Text

Section 14 : Regression Model Selection in R

 		lecture 105 1. Evaluating Regression Models Performance - Home 8:54
lecture 106 Interpreting Linear Regression Coefficients 9:16
lecture 107 3. Conclusion of Part 2 - Regression Text

Section 15 : Part 3 Classification

 		lecture 108 1. Welcome to Part 3 - Classification Text

Section 16 : Logistic Regression

 		lecture 109 1. Logistic Regression Intuition 17:7
lecture 110 2. Make sure you have your Machine Learning A-Z fo Text
lecture 111 3. Logistic Regression in Python - Step 1 9:43
lecture 112 4. Logistic Regression in Python - Step 2 13:38
lecture 113 5. Logistic Regression in Python - Step 3 7:40
lecture 114 6. Logistic Regression in Python - Step 4 7:49
lecture 115 7. Logistic Regression in Python - Step 5 6:15
lecture 116 8. Logistic Regression in Python - Step 6 9:26
lecture 117 9. Logistic Regression in Python - Step 7 16:6
lecture 118 10. Logistic Regression in R - Step 1 5:59
lecture 119 11. Logistic Regression in R - Step 2. 2:59
lecture 120 12. Logistic Regression in R - Step 3 5:23
lecture 121 13. Logistic Regression in R - Step 4 2:48
lecture 122 14. Warning - Update Text
lecture 123 15. Logistic Regression in R - Step 5 19:24
lecture 124 16. R Classification Template 4:17
lecture 125 17. Machine Learning Regression and Classification Text
lecture 126 19. BONUS Logistic Regression Practical Case Study Text

Section 17 : K-Nearest Neighbors (K-NN)

 		lecture 127 K-Nearest Neighbor Intuition 4:53
lecture 128 2. Make sure you have your Machine Learning A-Z fo Text
lecture 129 3. K-NN in Python 19:58
lecture 130 4. K-NN in R 15:47

Section 18 : Support Vector Machine (SVM)

 		lecture 131 2. SVM Intuition 9:49
lecture 132 3. Make sure you have your Machine Learning A-Z fo Text
lecture 133 4. SVM in Python 14:52
lecture 134 5. SVM in R 12:9

Section 19 : Kernel SVM

 		lecture 135 Kernel SVM Intuition 3:17
lecture 136 2. Mapping to a higher dimension 7:50
lecture 137 3. The Kernel Trick 12:20
lecture 138 4. Types of Kernel Functions
lecture 139 5. Non-Linear Kernel SVR (Advanced) 10:55
lecture 140 6. Make sure you have your Machine Learning A-Z fo Text
lecture 141 7. Kernel SVM in Python 3:47
lecture 142 8. Kernel SVM in R. 16:34

Section 20 : Naive Bayes

 		lecture 143 1. Bayes Theorem 20:26
lecture 144 2. Naive Bayes Intuition 14:3
lecture 145 3. Naive Bayes Intuition (Challenge Reveal)
lecture 146 4. Naive Bayes Intuition (Extras) 9:42
lecture 147 5. Make sure you have your Machine Learning A-Z fo Text
lecture 148 6. Naive Bayes in Python 14:19
lecture 149 7. Naive Bayes in R 14:54

Section 21 : Decision Tree Classification

 		lecture 150 1. Decision Tree Classification Intuition
lecture 151 2. Make sure you have your Machine Learning A-Z fo Text
lecture 152 3. Decision Tree Classification in Python 14:3
lecture 153 4. Decision Tree Classification in R 19:48

Section 22 : Random Forest Classification

 		lecture 154 1. Random Forest Classification Intuition 4:29
lecture 155 2. Make sure you have your Machine Learning A-Z fo Text
lecture 156 3. Random Forest Classification in Python 13:28
lecture 157 4. Random Forest Classification in R 19:56

Section 23 : Classification Model Selection in Python

 		lecture 158 Make sure you have this Model Selection folder rea Text
lecture 159 2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATIO 21:0

Section 24 : Evaluating Classification Models Performance

 		lecture 160 1. False Positives & False Negatives 7:58
lecture 161 2. Confusion Matrix 4:57
lecture 162 3. Accuracy Paradox 2:13
lecture 163 4. CAP Curve 11:16
lecture 164 5. CAP Curve Analysis 6:19
lecture 165 6. Conclusion of Part 3 - Classification Text

Section 25 : Part 4 Clustering

 		lecture 166 1. Welcome to Part 4 - Clustering Text

Section 26 : K-Means Clustering

 		lecture 167 1. K-Means Clustering Intuition 14:17
lecture 168 2. K-Means Random Initialization Trap 7:49
lecture 169 3. K-Means Selecting The Number Of Clusters 11:52
lecture 170 Make sure you have your Machine Learning A-Z fol Text
lecture 171 5. K-Means Clustering in Python - Step 1 8:25
lecture 172 6. K-Means Clustering in Python - Step 2 10:36
lecture 173 7. K-Means Clustering in Python - Step 3 16:58
lecture 174 8. K-Means Clustering in Python - Step 4 6:44
lecture 175 9. K-Means Clustering in Python - Step 5 19:35
lecture 176 10. K-Means Clustering in R 11:47

Section 27 : Hierarchical Clustering

 		lecture 177 2. Hierarchical Clustering Intuition 8:48
lecture 178 3. Hierarchical Clustering How Dendrograms Work 8:48
lecture 179 4. Hierarchical Clustering Using Dendrograms 11:22
lecture 180 5. Make sure you have your Machine Learning A-Z fo Text
lecture 181 6. Hierarchical Clustering in Python - Step 1 6:57
lecture 182 7. Hierarchical Clustering in Python - Step 2 17:12
lecture 183 8. Hierarchical Clustering in Python - Step 3 12:20
lecture 184 9. Hierarchical Clustering in R - Step 1 3:45
lecture 185 10. Hierarchical Clustering in R - Step 2 5:24
lecture 186 11. Hierarchical Clustering in R - Step 3 3:19
lecture 187 12. Hierarchical Clustering in R - Step 4 2:46
lecture 188 13. Hierarchical Clustering in R - Step 5 2:33
lecture 189 15. Conclusion of Part 4 - Clustering

Section 28 : Part 5 Association Rule Learning

lecture 190 1. Welcome to Part 5 - Association Rule Learning Text

Section 29 : Apriori

 		lecture 191 1. Apriori Intuition 18:14
lecture 192 2. Make sure you have your Machine Learning A-Z fo Text
lecture 193 3. Apriori in Python - Step 1 8:46
lecture 194 4. Apriori in Python - Step 2 17:7
lecture 195 5. Apriori in Python - Step 3 12:49
lecture 196 6. Apriori in Python - Step 4 19:41
lecture 197 Apriori in R - Step 1 19:53
lecture 198 8. Apriori in R - Step 2 14:25
lecture 199 9. Apriori in R - Step 3 19:18

Section 30 : Eclat

 		lecture 200 1. Eclat Intuition 6:5
lecture 201 .2. Make sure you have your Machine Learning A-Z f Text
lecture 202 3. Eclat in Python 12:1
lecture 203 4. Eclat in R 10:9

Section 31 : Part 6 Reinforcement Learning

lecture 204 1. Welcome to Part 6 - Reinforcement Learning Text

Section 32 : Upper Confidence Bound (UCB)

 		lecture 205 1. The Multi-Armed Bandit Problem 15:36
lecture 206 2. Upper Confidence Bound (UCB) Intuition 14:54
lecture 207 3. Make sure you have your Machine Learning A-Z fo Text
lecture 208 4. Upper Confidence Bound in Python - Step 1 12:43
lecture 209 5. Upper Confidence Bound in Python - Step 2 3:52
lecture 210 6. Upper Confidence Bound in Python - Step 3 7:17
lecture 211 7. Upper Confidence Bound in Python - Step 4 15:46
lecture 212 8. Upper Confidence Bound in Python - Step 5 6:12
lecture 213 9. Upper Confidence Bound in Python - Step 6 6:12
lecture 214 10. Upper Confidence Bound in Python - Step 7. 8:10
lecture 215 11. Upper Confidence Bound in R - Step 1 13:39
lecture 216 12. Upper Confidence Bound in R - Step 2 15:59
lecture 217 13. Upper Confidence Bound in R - Step 3 17:38
lecture 218 14. Upper Confidence Bound in R - Step 4 3:18

Section 33 : Thompson Sampling

 		lecture 219 Thompson Sampling Intuition 19:12
lecture 220 2. Algorithm Comparison UCB vs Thompson Sampling 8:12
lecture 221 3. Make sure you have your Machine Learning A-Z fo Text
lecture 222 4. Thompson Sampling in Python - Step 1 5:48
lecture 223 5. Thompson Sampling in Python - Step 12:20
lecture 224 6. Thompson Sampling in Python - Step 3 14:4
lecture 225 7. Thompson Sampling in Python - Step 4 7:45
lecture 226 8. Additional Resource for this Section Text
lecture 227 9. Thompson Sampling in R - Step 1 14:4
lecture 228 10. Thompson Sampling in R - Step 2 3:27

Section 34 : Part 7 Natural Language Processing

lecture 229 1. Welcome to Part 7 - Natural Language Processing Text
lecture 230 2. NLP Intuition 3:3
lecture 231 3. Types of Natural Language Processing 4:11
lecture 232 4. Classical vs Deep Learning Models 11:23
lecture 233 5. Bag-Of-Words Model 17:6
lecture 234 6. Make sure you have your Machine Learning A-Z fo Text
lecture 235 7. Natural Language Processing in Python - Step 1 7:13
lecture 236 8. Natural Language Processing in Python - Step 2 6:46
lecture 237 9. Natural Language Processing in Python - Step 3 12:54
lecture 238 10. Natural Language Processing in Python - Step 4 11:1
lecture 239 11. Natural Language Processing in Python - Step 5 17:24
lecture 240 12. Natural Language Processing in Python - Step 6 9:53
lecture 241 13. Natural Language Processing in Python - BONUS Text
lecture 242 14. Homework Challenge Text
lecture 243 15. Natural Language Processing in R - Step 1 16:35
lecture 244 16. Natural Language Processing in R - Step 2 8:39
lecture 245 17. Natural Language Processing in R - Step 3 6:28
lecture 246 18. Natural Language Processing in R - Step 4 2:58
lecture 247 19. Natural Language Processing in R - Step 5 2:5
lecture 248 20. Natural Language Processing in R - Step 6 5:49
lecture 249 21. Natural Language Processing in R - Step 7 3:27
lecture 250 22. Natural Language Processing in R - Step 5:21
lecture 251 23. Natural Language Processing in R - Step 9 12:51
lecture 252 24. Natural Language Processing in R - Step 10 17:31
lecture 253 25. Homework Challenge Text
lecture 254 26. BONUS NLP BERT Text
lecture 254 26. BONUS NLP BERT Text
lecture 254 26. BONUS NLP BERT Text

Section 35 : Part 8 Deep Learning

lecture 255 1. Welcome to Part 8 - Deep Learning Text
lecture 256 2. What is Deep Learning 12:34

Section 36 : Artificial Neural Networks

 		lecture 257 1. Plan of attack 2:52
lecture 258 2. The Neuron 16:25
lecture 259 3. The Activation Function 8:29
lecture 260 4. How do Neural Networks work 12:48
lecture 261 5. How do Neural Networks learn 12:59
lecture 262 6. Gradient Descent 10:13
lecture 262 6. Gradient Descent 10:13
lecture 263 7. Stochastic Gradient Descent 8:45
lecture 264 8. Backpropagation 5:22
lecture 265 9. Business Problem Description 4:59
lecture 266 10. Make sure you have your Machine Learning A-Z f Text
lecture 267 1. ANN in Python - Step 1. 10:21
lecture 268 12. Check out our free course on ANN for Regressio Text
lecture 269 13. ANN in Python - Step 2 18:37
lecture 270 14. ANN in Python - Step 3 14:28
lecture 271 15. ANN in Python - Step 4 11:58
lecture 272 16. ANN in Python - Step 5
lecture 273 17. ANN in R - Step 1 17:17
lecture 274 18. ANN in R - Step 2 6:30
lecture 275 19. ANN in R - Step 3 12:30
lecture 276 20. ANN in R - Step 4 (Last step)
lecture 277 21. Deep Learning BONUS #1 Text
lecture 278 22. BONUS ANN Case Study Text

Section 37 : Convolutional Neural Networks

 		lecture 279 1. Plan of attack 3:32
lecture 280 2. What are convolutional neural networks 15:49
lecture 281 3. Step 1 - Convolution Operation 15:49
lecture 282 4. Step 1(b) - ReLU Layer 6:41
lecture 283 5. Step 2 - Pooling 14:13
lecture 284 6. Step 3 - Flattening 1:53
lecture 285 7. Step 4 - Full Connection 19:25
lecture 286 8. Summary 4:20
lecture 287 9. Softmax & Cross-Entropy 18:20
lecture 288 10. Make sure you have your dataset ready Text
lecture 289 11. CNN in Python - Step 1 11:35
lecture 290 12. CNN in Python - Step 2 17:46
lecture 291 13. CNN in Python - Step 3 17:56
lecture 292 14. CNN in Python - Step 4 7:21
lecture 293 15. CNN in Python - Step 5 14:56
lecture 294 16. CNN in Python - FINAL DEMO! 23:38
lecture 295 17. Deep Learning BONUS #2 Text

Section 38 : Part 9 Dimensionality Reduction

lecture 296 1. Welcome to Part 9 - Dimensionality Reduction Text

Section 39 : Principal Component Analysis (PCA)

 		lecture 297 1. Principal Component Analysis (PCA) Intuition 3:49
lecture 298 2. Make sure you have your Machine Learning A-Z fo Text
lecture 299 3. PCA in Python - Step 1 16:53
lecture 300 4. PCA in Python - Step 2 5:30
lecture 301 5. PCA in R - Step 1 12:8
lecture 302 6. PCA in R - Step 2 11:22
lecture 303 7. PCA in R - Step 3 13:43

Section 40 : Linear Discriminant Analysis (LDA)

 		lecture 304 1. Linear Discriminant Analysis (LDA) Intuition 3:50
lecture 305 2. Make sure you have your Machine Learning A-Z fo Text
lecture 306 3. LDA in Python 14:52
lecture 307 4. LDA in R 20:0

Section 41 : Kernel PCA

 		lecture 308 1. Make sure you have your Machine Learning A-Z fo Text
lecture 309 2. Kernel PCA in Python 11:3
lecture 310 3. Kernel PCA in R 20:30

Section 42 : Part 10 Model Selection & Boosting

 		lecture 311 1. Welcome to Part 10 - Model Selection & Boosting Text

Section 43 : Model Selection

 		lecture 312 1. Make sure you have your Machine Learning A-Z fo Text
lecture 313 2. k-Fold Cross Validation in Python 17:55
lecture 314 3. Grid Search in Python 21:57
lecture 315 4. k-Fold Cross Validation in R 19:29
lecture 316 5. Grid Search in R 13:59

Section 44 : XGBoost

 		lecture 317 Make sure you have your Machine Learning A-Z folde Text
lecture 318 2. XGBoost in Python 14:49
lecture 319 3. Model Selection and Boosting BONUS Text
lecture 320 4. XGBoost in R 18:14
lecture 321 5. THANK YOU Bonus Video 0:6

Section 45 : Bonus Lectures

 		lecture 322 YOUR SPECIAL BONUS Text