Section 1 : Welcome to the course!

lecture 1 Applications of Machine Learning 2:32
lecture 2 Why Machine Learning is the Future 6:38
lecture 3 Installing R and R Studio (MAC & Windows) 5:25
lecture 4 Update Recommended Anaconda Version Text
lecture 5 Installing Python and Anaconda (MAC & Windows) 6:22
lecture 6 BONUS Meet your instructors Text

Section 2 : Part 1 Data Preprocessing

lecture 7 Welcome to Part 1 - Data Preprocessing
lecture 8 Get the dataset 6:25
lecture 9 Importing the Libraries 5:20
lecture 10 Importing the Dataset 11:55
lecture 11 For Python learners, summary of Object-oriented pr Text
lecture 12 Missing Data 15:58
lecture 13 Categorical Data
lecture 14 Splitting the Dataset into the Training set and Te 17:37
lecture 15 Feature Scaling 15:36
lecture 16 And here is our Data Preprocessing Template! 8:48

Section 3 : Part 2 Regression

lecture 17 Welcome to Part 2 - Regression Text

Section 4 : Simple Linear Regression

lecture 18 How to get the dataset
lecture 19 Dataset + Business Problem Description 2:56
lecture 20 Simple Linear Regression Intuition - Step 1 5:46
lecture 21 Simple Linear Regression Intuition - Step 2
lecture 22 Simple Linear Regression in Python - Step 1 9:56
lecture 23 Simple Linear Regression in Python - Step 2 8:20
lecture 24 Simple Linear Regression in Python - Step 3 6:43
lecture 25 Simple Linear Regression in Python - Step 4 14:50
lecture 26 Simple Linear Regression in R - Step 1 4:40
lecture 27 Simple Linear Regression in R - Step 2 5:59
lecture 28 Simple Linear Regression in R - Step 3 3:39
lecture 29 Simple Linear Regression in R - Step 4 5:42

Section 5 : Multiple Linear Regression

lecture 30 How to get the dataset 3:19
lecture 31 Dataset + Business Problem Description 3:44
lecture 32 Multiple Linear Regression Intuition - Step 1 1:3
lecture 33 Multiple Linear Regression Intuition - Step 2 1:0
lecture 34 Multiple Linear Regression Intuition - Step 3 7:21
lecture 35 Multiple Linear Regression Intuition - Step 4 2:11
lecture 36 Multiple Linear Regression Intuition - Step 5 15:42
lecture 37 Multiple Linear Regression in Python - Step 1 15:58
lecture 38 Multiple Linear Regression in Python - Step 2 2:57
lecture 39 Multiple Linear Regression in Python - Step 3 5:28
lecture 40 Multiple Linear Regression in Python - Backward El 13:15
lecture 41 Multiple Linear Regression in Python - Backward El 12:41
lecture 42 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
lecture 43 Multiple Linear Regression in R - Step 1 7:51
lecture 44 Multiple Linear Regression in R - Step 2 10:26
lecture 45 About Certification Pdf
lecture 46 Multiple Linear Regression in R - Backward Elimina 17:51
lecture 47 Multiple Linear Regression in R - Backward Elimina 7:34

Section 6 : Polynomial Regression

lecture 48 Polynomial Regression Intuition 5:9
lecture 49 How to get the dataset 3:19
lecture 50 Polynomial Regression in Python - Step 1 11:39
lecture 51 Polynomial Regression in Python - Step 2 11:45
lecture 52 Polynomial Regression in Python - Step 3 19:58
lecture 53 Polynomial Regression in Python - Step 4 5:46
lecture 54 Python Regression Template 10:59
lecture 55 Polynomial Regression in R - Step 1 9:13
lecture 56 Polynomial Regression in R - Step 2 9:59
lecture 57 Polynomial Regression in R - Step 3 19:55
lecture 58 Polynomial Regression in R - Step 4 9:36
lecture 59 R Regression Template 11:59

Section 7 : Support Vector Regression (SVR)

lecture 60 How to get the dataset 3:19
lecture 61 SVR in Python 19:57
lecture 62 SVR in R 11:44

Section 8 : Decision Tree Regression

lecture 63 Decision Tree Regression Intuition 11:7
lecture 64 How to get the dataset 3:19
lecture 65 Decision Tree Regression in Python 14:46
lecture 66 Decision Tree Regression in R 19:54

Section 9 : Random Forest Regression

lecture 67 Random Forest Regression Intuition 6:44
lecture 68 How to get the dataset 3:19
lecture 69 Random Forest Regression in Python 16:45
lecture 70 Random Forest Regression in R 17:43

Section 10 : Evaluating Regression Models Performance

lecture 71 R-Squared Intuition 5:12
lecture 72 Adjusted R-Squared Intuition 9:57
lecture 73 Evaluating Regression Models Performance - Homewor 8:54
lecture 74 Interpreting Linear Regression Coefficient 9:16
lecture 75 Conclusion of Part 2 - Regression Text

Section 11 : Part 3 Classification

lecture 76 Welcome to Part 3 - Classification Text

Section 12 : Logistic Regression

lecture 77 Logistic Regression Intuition 17:7
lecture 78 How to get the dataset 3:19
lecture 79 Logistic Regression in Python - Step 1 5:48
lecture 80 Logistic Regression in Python - Step 2 3:24
lecture 81 Logistic Regression in Python - Step 3 2:35
lecture 82 Logistic Regression in Python - Step 4 4:34
lecture 83 Logistic Regression in Python - Step 5 19:40
lecture 84 Python Classification Template 3:53
lecture 85 Logistic Regression in R - Step 1 5:59
lecture 86 Logistic Regression in R - Step 2 2:59
lecture 87 Logistic Regression in R - Step 3 5:23
lecture 88 Logistic Regression in R - Step 4 2:48
lecture 89 Logistic Regression in R - Step 5 19:24
lecture 90 R Classification Template 4:17

Section 13 : K-Nearest Neighbors (K-NN)

lecture 91 K-Nearest Neighbor Intuition 4:53
lecture 92 How to get the dataset 3:19
lecture 93 K-NN in Python 14:10
lecture 94 K-NN in R 15:47

Section 14 : Support Vector Machine (SVM)

lecture 95 SVM Intuition 9:49
lecture 96 How to get the dataset 3:19
lecture 97 SVM in Python 12:24
lecture 98 SVM in R 12:9

Section 15 : Kernel SVM

lecture 99 Kernel SVM Intuition 3:18
lecture 100 Mapping to a higher dimension 7:50
lecture 101 The Kernel Trick 12:21
lecture 102 Types of Kernel Functions 3:49
lecture 103 How to get the dataset 3:20
lecture 104 Kernel SVM in Python. 17:54
lecture 105 Kernel SVM in R 16:36

Section 16 : Naive Bayes

lecture 106 Bayes Theorem 20:27
lecture 107 Naive Bayes Intuition 14:3
lecture 108 Naive Bayes Intuition (Challenge Reveal) 6:4
lecture 109 Naive Bayes Intuition (Extras) 9:44
lecture 110 How to get the dataset 3:21
lecture 111 Naive Bayes in Python 9:14
lecture 112 Naive Bayes in R 14:54

Section 17 : Decision Tree Classification

lecture 113 Decision Tree Classification Intuition 0:31
lecture 114 How to get the dataset 3:20
lecture 115 Decision Tree Classification in Python 12:35
lecture 116 Decision Tree Classification in R 19:48

Section 18 : Random Forest Classification

lecture 117 Random Forest Classification Intuition 4:29
lecture 118 How to get the dataset 3:20
lecture 119 Random Forest Classification in Python 19:56
lecture 120 Random Forest Classification in R 19:56

Section 19 : Evaluating Classification Models Performance

lecture 121 False Positives & False Negatives 8:1
lecture 122 Confusion Matrix 4:57
lecture 123 Accuracy Paradox 2:13
lecture 124 CAP Curve 11:18
lecture 125 CAP Curve Analysis 2:51
lecture 126 Conclusion of Part 3 - Classification Text

Section 20 : Part 4 Clustering

lecture 127 Welcome to Part 4 - Clustering Text

Section 21 : K-Means Clustering

lecture 128 K-Means Clustering Intuition 14:19
lecture 129 K-Means Random Initialization Trap 7:51
lecture 130 K-Means Selecting The Number Of Clusters 11:55
lecture 131 How to get the dataset 3:19
lecture 132 K-Means Clustering in Python 17:57
lecture 133 K-Means Clustering in R 11:47

Section 22 : Hierarchical Clustering

lecture 134 Hierarchical Clustering Intuition 8:49
lecture 135 Hierarchical Clustering How Dendrograms Work 8:49
lecture 136 Hierarchical Clustering Using Dendrograms 6:57
lecture 137 How to get the dataset 3:19
lecture 138 HC in Python - Step 1 4:58
lecture 139 HC in Python - Step 2 6:33
lecture 140 HC in Python - Step 3 5:29
lecture 141 HC in Python - Step 4 4:29
lecture 142 HC in Python - Step 5 4:6
lecture 143 HC in R - Step 1 3:46
lecture 144 HC in R - Step 2 5:24
lecture 145 HC in R - Step 3 3:19
lecture 146 HC in R - Step 4 2:46
lecture 147 HC in R - Step 5 2:33
lecture 148 Conclusion of Part 4 - Clustering Text

Section 23 : Part 5 Association Rule Learning

lecture 149 Welcome to Part 5 - Association Rule Learning Text

Section 24 : Apriori

lecture 150 Apriori Intuition 18:14
lecture 151 How to get the dataset 3:19
lecture 152 Apriori in R - Step 1 19:53
lecture 153 Apriori in R - Step 2 14:25
lecture 154 Apriori in R - Step 3 19:18
lecture 155 Apriori in Python - Step 1 17:59
lecture 156 Apriori in Python - Step 2 14:39
lecture 157 Apriori in Python - Step 3 12:6

Section 25 : Eclat

lecture 158 Eclat Intuition 6:5
lecture 159 How to get the dataset 3:19
lecture 159 How to get the dataset 3:19
lecture 160 Eclat in R 10:9

Section 26 : Part 6 Reinforcement Learning

lecture 161 Welcome to Part 6 - Reinforcement Learning Text

Section 27 : Upper Confidence Bound (UCB)

lecture 162 The Multi-Armed Bandit Problem 15:2
lecture 163 Upper Confidence Bound (UCB) Intuition 14:54
lecture 164 How to get the dataset 3:19
lecture 165 Upper Confidence Bound in Python - Step 1 14:42
lecture 166 Upper Confidence Bound in Python - Step 2 18:9
lecture 167 Upper Confidence Bound in Python - Step 3 18:47
lecture 168 Upper Confidence Bound in Python - Step 4 3:54
lecture 169 Upper Confidence Bound in R - Step 1 13:40
lecture 170 Upper Confidence Bound in R - Step 2 15:59
lecture 171 Upper Confidence Bound in R - Step 3 17:38
lecture 172 Upper Confidence Bound in R - Step 4 3:18

Section 28 : Thompson Sampling

lecture 173 Thompson Sampling Intuition 19:13
lecture 174 Algorithm Comparison UCB vs Thompson Sampling 8:12
lecture 175 How to get the dataset 3:19
lecture 176 Thompson Sampling in Python - Step 1 19:47
lecture 177 Thompson Sampling in Python - Step 2 3:43
lecture 178 Thompson Sampling in R - Step 1 19:2
lecture 179 Thompson Sampling in R - Step 2 3:27

Section 29 : Part 7 Natural Language Processing

lecture 180 Welcome to Part 7 - Natural Language Processing Text
lecture 181 How to get the dataset 3:19
lecture 182 Natural Language Processing in Python - Step 1 12:43
lecture 183 Natural Language Processing in Python - Step 2 10:55
lecture 184 Natural Language Processing in Python - Step 3 1:41
lecture 185 Natural Language Processing in Python - Step 4 6:12
lecture 186 Natural Language Processing in Python - Step 5 7:16
lecture 187 Natural Language Processing in Python - Step 6 3:5
lecture 188 Natural Language Processing in Python - Step 7 7:24
lecture 189 Natural Language Processing in Python - Step 8 16:58
lecture 190 natural Language Processing in Python - Step 9 5:59
lecture 191 Natural Language Processing in Python - Step 10 9:57
lecture 192 Homework Challenge Text
lecture 193 Natural Language Processing in R - Step 1 16:35
lecture 194 Natural Language Processing in R - Step 2 8:39
lecture 195 Natural Language Processing in R - Step 3 6:28
lecture 196 Natural Language Processing in R - Step 4 2:58
lecture 197 Natural Language Processing in R - Step 5 2:5
lecture 198 Natural Language Processing in R - Step 6 5:49
lecture 199 Natural Language Processing in R - Step 7 3:27
lecture 200 Natural Language Processing in R - Step 8 5:21
lecture 201 Natural Language Processing in R - Step 9 12:51
lecture 202 Natural Language Processing in R - Step 10 17:31
lecture 203 Homework Challenge Text

Section 30 : Part 8 Deep Learning

lecture 204 Welcome to Part 8 - Deep Learning Text
lecture 205 What is Deep Learning 12:34

Section 31 : Artificial Neural Networks

lecture 206 Plan of attack 2:52
lecture 207 The Neuron 16:25
lecture 208 The Activation Function 8:29
lecture 209 How do Neural Networks work 12:48
lecture 210 How do Neural Networks learn 12:59
lecture 211 Gradient Descent 9:49
lecture 212 Stochastic Gradient Descent 5:50
lecture 213 Backpropagation 2:47
lecture 214 How to get the dataset 3:19
lecture 215 Business Problem Description 4:59
lecture 216 ANN in Python - Step 1 - Installing Theano, Tensor 12:59
lecture 217 ANN in Python - Step 2 18:17
lecture 218 ANN in Python - Step 3 3:14
lecture 219 ANN in Python - Step 4 2:21
lecture 220 ANN in Python - Step 5 12:20
lecture 221 ANN in Python - Step 6 2:44
lecture 222 ANN in Python - Step 7 3:32
lecture 223 ANN in Python - Step 8 6:56
lecture 224 ANN in Python - Step 9 6:22
lecture 225 ANN in Python - Step 10 6:46
lecture 226 ANN in R - Step 1 17:17
lecture 227 ANN in R - Step 2 6:31
lecture 228 ANN in R - Step 3 12:30
lecture 229 ANN in R - Step 4 (Last step) 14:7

Section 32 : Convolutional Neural Networks

lecture 230 Plan of attack 3:32
lecture 231 What are convolutional neural networks 15:52
lecture 232 Step 1 - Convolution Operation 16:39
lecture 233 Step 1(b) - ReLU Layer 5:1
lecture 234 Step 2 - Pooling 14:14
lecture 235 Step 3 - Flattening 1:53
lecture 236 Step 4 - Full Connection 19:26
lecture 237 Summary 4:23
lecture 238 Softmax & Cross-Entropy 16:24
lecture 239 How to get the dataset 3:24
lecture 240 CNN in Python - Step 1 12:46
lecture 241 CNN in Python - Step 2 3:1
lecture 242 CNN in Python - Step 3 1:6
lecture 243 CNN in Python - Step 4 12:53
lecture 244 Welcome to Part 4 - Clustering Text
lecture 245 CNN in Python - Step 6 5:2
lecture 246 CNN in Python - Step 7 5:57
lecture 247 CNN in Python - Step 8 2:52
lecture 248 CNN in Python - Step 9 19:45
lecture 249 CNN in Python - Step 10 8:28
lecture 250 Conclusion Text

Section 33 : Part 9 Dimensionality Reduction

lecture 251 Welcome to Part 9 - Dimensionality Reduction Text

Section 34 : Principal Component Analysis (PCA)

lecture 252 How to get the dataset 3:19
lecture 253 PCA in Python - Step 1 11:48
lecture 254 PCA in Python - Step 2 8:5
lecture 255 PCA in Python - Step 3 9:49
lecture 256 PCA in R - Step 1 12:10
lecture 257 PCA in R - Step 2 11:23
lecture 258 PCA in R - Step 3 13:43

Section 35 : Linear Discriminant Analysis

lecture 259 How to get the dataset 3:24
lecture 260 LDA in Python 18:13
lecture 261 LDA in R 20:2

Section 36 : Kernel PCA

lecture 362 How to get the dataset 3:23
lecture 363 Kernel PCA in Python 14:28
lecture 264 Kernel PCA in R 20:34

Section 37 : Part 10 Model Selection & Boosting

lecture 265 Welcome to Part 10 - Model Selection & Boosting Text

Section 38 : Model Selection

lecture 266 How to get the dataset 3:23
lecture 267 k-Fold Cross Validation in Python 13:48
lecture 268 k-Fold Cross Validation in R 19:34
lecture 269 Grid Search in Python - Step 1 15:11
lecture 270 Grid Search in Python - Step 2 11:6
lecture 271 Grid Search in R 14:4

Section 39 : XGBoost

lecture 272 How to get the dataset 3:21
lecture 273 XGBoost in Python - Step 1 9:32
lecture 274 XGBoost in Python - Step 2 12:43
lecture 275 XGBoost in R 18:14