Library chevron_right

Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

Contents
About

Course Contents

  • Section 1: Welcome to the course! Here we will help you get started in the best conditions.
    4 lectures • 18 min
    • 1 Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2025]
      02:06
    • 2 Machine Learning Demo - Get Excited!
      04:45
    • 3 How to use the ML A-Z folder & Google Colab
      05:44
    • 4 Installing R and R Studio (Mac, Linux & Windows)
      05:21
  • Section 2: -------------------- Part 1: Data Preprocessing --------------------
    3 lectures • 10 min
    • 5 The Machine Learning process
      01:31
    • 6 Splitting the data into a Training and Test set
      02:02
    • 7 Feature Scaling
      06:27
  • Section 3: Data Preprocessing in Python
    18 lectures • 1.5 hr
    • 8 Getting Started - Step 1
      05:21
    • 9 Getting Started - Step 2
      05:21
    • 10 Importing the Libraries
      03:34
    • 11 Importing the Dataset - Step 1
      05:13
    • 12 Importing the Dataset - Step 2
      04:42
    • 13 Importing the Dataset - Step 3
      05:46
    • 14 Taking care of Missing Data - Step 1
      05:56
    • 15 Taking care of Missing Data - Step 2
      05:58
    • 16 Encoding Categorical Data - Step 1
      04:24
    • 17 Encoding Categorical Data - Step 2
      05:54
    • 18 Encoding Categorical Data - Step 3
      04:39
    • 19 Splitting the dataset into the Training set and Test set - Step 1
      03:55
    • 20 Splitting the dataset into the Training set and Test set - Step 2
      05:59
    • 21 Splitting the dataset into the Training set and Test set - Step 3
      03:52
    • 22 Feature Scaling - Step 1
      05:56
    • 23 Feature Scaling - Step 2
      04:45
    • 24 Feature Scaling - Step 3
      03:48
    • 25 Feature Scaling - Step 4
      05:59
  • Section 4: Data Preprocessing in R
    10 lectures • 42 min
    • 26 Getting Started
      01:35
    • 27 Dataset Description
      01:57
    • 28 Importing the Dataset
      02:44
    • 29 Taking care of Missing Data
      05:55
    • 30 Encoding Categorical Data
      05:56
    • 31 Splitting the dataset into the Training set and Test set - Step 1
      04:38
    • 32 Splitting the dataset into the Training set and Test set - Step 2
      04:54
    • 33 Feature Scaling - Step 1
      04:25
    • 34 Feature Scaling - Step 2
      04:49
    • 35 Data Preprocessing Template
      05:15
  • Section 5: -------------------- Part 2: Regression --------------------
  • Section 6: Simple Linear Regression
    15 lectures • 1.2 hr
    • 36 Simple Linear Regression Intuition
      02:22
    • 37 Ordinary Least Squares
      03:17
    • 38 Simple Linear Regression in Python - Step 1a
      05:49
    • 39 Simple Linear Regression in Python - Step 1b
      05:58
    • 40 Simple Linear Regression in Python - Step 2a
      03:53
    • 41 Simple Linear Regression in Python - Step 2b
      03:58
    • 42 Simple Linear Regression in Python - Step 3
      04:35
    • 43 Simple Linear Regression in Python - Step 4a
      05:49
    • 44 Simple Linear Regression in Python - Step 4b
      05:57
    • 45 Simple Linear Regression in R - Step 1
      04:40
    • 46 Simple Linear Regression in R - Step 2
      05:58
    • 47 Simple Linear Regression in R - Step 3
      03:38
    • 48 Simple Linear Regression in R - Step 4a
      05:44
    • 49 Simple Linear Regression in R - Step 4b
      05:33
    • 50 Simple Linear Regression in R - Step 4c
      04:37
  • Section 7: Multiple Linear Regression
    22 lectures • 2.2 hr
    • 51 Dataset + Business Problem Description
      03:44
    • 52 Multiple Linear Regression Intuition
      02:26
    • 53 Assumptions of Linear Regression
      04:23
    • 54 Multiple Linear Regression Intuition - Step 3
      07:21
    • 55 Multiple Linear Regression Intuition - Step 4
      02:10
    • 56 Understanding the P-Value
      11:44
    • 57 Multiple Linear Regression Intuition - Step 5
      15:41
    • 58 Multiple Linear Regression in Python - Step 1a
      05:54
    • 59 Multiple Linear Regression in Python - Step 1b
      02:35
    • 60 Multiple Linear Regression in Python - Step 2a
      04:28
    • 61 Multiple Linear Regression in Python - Step 2b
      04:43
    • 62 Multiple Linear Regression in Python - Step 3a
      05:52
    • 63 Multiple Linear Regression in Python - Step 3b
      04:32
    • 64 Multiple Linear Regression in Python - Step 4a
      05:38
    • 65 Multiple Linear Regression in Python - Step 4b
      05:34
    • 66 Multiple Linear Regression in R - Step 1a
      03:53
    • 67 Multiple Linear Regression in R - Step 1b
      03:57
    • 68 Multiple Linear Regression in R - Step 2a
      05:22
    • 69 Multiple Linear Regression in R - Step 2b
      04:20
    • 70 Multiple Linear Regression in R - Step 3
      04:26
    • 71 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
      17:51
    • 72 Multiple Linear Regression in R - Backward Elimination - Homework Solution
      07:33
  • Section 8: Polynomial Regression
    20 lectures • 1.7 hr
    • 73 Polynomial Regression Intuition
      05:08
    • 74 Polynomial Regression in Python - Step 1a
      04:36
    • 75 Polynomial Regression in Python - Step 1b
      05:55
    • 76 Polynomial Regression in Python - Step 2a
      05:55
    • 77 Polynomial Regression in Python - Step 2b
      05:43
    • 78 Polynomial Regression in Python - Step 3a
      05:57
    • 79 Polynomial Regression in Python - Step 3b
      05:38
    • 80 Polynomial Regression in Python - Step 4a
      03:59
    • 81 Polynomial Regression in Python - Step 4b
      03:59
    • 82 Polynomial Regression in R - Step 1a
      03:45
    • 83 Polynomial Regression in R - Step 1b
      03:39
    • 84 Polynomial Regression in R - Step 2a
      04:40
    • 85 Polynomial Regression in R - Step 2b
      04:55
    • 86 Polynomial Regression in R - Step 3a
      04:59
    • 87 Polynomial Regression in R - Step 3b
      05:31
    • 88 Polynomial Regression in R - Step 3c
      05:42
    • 89 Polynomial Regression in R - Step 4a
      03:58
    • 90 Polynomial Regression in R - Step 4b
      03:47
    • 91 R Regression Template - Step 1
      05:57
    • 92 R Regression Template - Step 2
      05:25
  • Section 9: Support Vector Regression (SVR)
    13 lectures • 1.1 hr
    • 93 SVR Intuition (Updated!)
      08:09
    • 94 Heads-up on non-linear SVR
      03:57
    • 95 SVR in Python - Step 1a
      05:46
    • 96 SVR in Python - Step 1b
      03:29
    • 97 SVR in Python - Step 2a
      05:34
    • 98 SVR in Python - Step 2b
      04:56
    • 99 SVR in Python - Step 2c
      03:31
    • 100 SVR in Python - Step 3
      05:57
    • 101 SVR in Python - Step 4
      03:46
    • 102 SVR in Python - Step 5a
      03:42
    • 103 SVR in Python - Step 5b
      03:40
    • 104 SVR in R - Step 1
      05:58
    • 105 SVR in R - Step 2
      04:58
  • Section 10: Decision Tree Regression
    10 lectures • 52 min
    • 106 Decision Tree Regression Intuition
      11:06
    • 107 Decision Tree Regression in Python - Step 1a
      04:40
    • 108 Decision Tree Regression in Python - Step 1b
      03:58
    • 109 Decision Tree Regression in Python - Step 2
      04:59
    • 110 Decision Tree Regression in Python - Step 3
      03:16
    • 111 Decision Tree Regression in Python - Step 4
      04:59
    • 112 Decision Tree Regression in R - Step 1
      04:55
    • 113 Decision Tree Regression in R - Step 2
      05:49
    • 114 Decision Tree Regression in R - Step 3
      04:55
    • 115 Decision Tree Regression in R - Step 4
      03:50
  • Section 11: Random Forest Regression
    6 lectures • 36 min
    • 116 Random Forest Regression Intuition
      06:44
    • 117 Random Forest Regression in Python - Step 1
      05:53
    • 118 Random Forest Regression in Python - Step 2
      05:55
    • 119 Random Forest Regression in R - Step 1
      05:51
    • 120 Random Forest Regression in R - Step 2
      05:58
    • 121 Random Forest Regression in R - Step 3
      05:26
  • Section 12: Evaluating Regression Models Performance
    2 lectures • 10 min
    • 122 R-Squared Intuition
      04:35
    • 123 Adjusted R-Squared Intuition
      05:30
  • Section 13: Regression Model Selection in Python
    6 lectures • 28 min
    • 124 Preparation of the Regression Code Templates - Step 1
      04:45
    • 125 Preparation of the Regression Code Templates - Step 2
      05:59
    • 126 Preparation of the Regression Code Templates - Step 3
      03:59
    • 127 Preparation of the Regression Code Templates - Step 4
      03:58
    • 128 THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! - STEP 1
      04:47
    • 129 THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! - STEP 2
      04:15
  • Section 14: Regression Model Selection in R
    2 lectures • 18 min
    • 130 Evaluating Regression Models Performance - Homework's Final Part
      08:54
    • 131 Interpreting Linear Regression Coefficients
      09:16
  • Section 15: -------------------- Part 3: Classification --------------------
    1 lecture • 3 min
    • 132 What is Classification?
      02:30
  • Section 16: Logistic Regression
    24 lectures • 1.9 hr
    • 133 Logistic Regression Intuition
      04:55
    • 134 Maximum Likelihood
      03:50
    • 135 Logistic Regression in Python - Step 1a
      05:43
    • 136 Logistic Regression in Python - Step 1b
      03:59
    • 137 Logistic Regression in Python - Step 2a
      05:51
    • 138 Logistic Regression in Python - Step 2b
      05:57
    • 139 Logistic Regression in Python - Step 3a
      03:58
    • 140 Logistic Regression in Python - Step 3b
      03:30
    • 141 Logistic Regression in Python - Step 4a
      05:59
    • 142 Logistic Regression in Python - Step 4b
      01:49
    • 143 Logistic Regression in Python - Step 5
      05:59
    • 144 Logistic Regression in Python - Step 6a
      05:52
    • 145 Logistic Regression in Python - Step 6b
      03:33
    • 146 Logistic Regression in Python - Step 7a
      05:54
    • 147 Logistic Regression in Python - Step 7b
      03:44
    • 148 Logistic Regression in Python - Step 7c
      03:19
    • 149 Logistic Regression in R - Step 1
      05:58
    • 150 Logistic Regression in R - Step 2
      02:58
    • 151 Logistic Regression in R - Step 3
      05:23
    • 152 Logistic Regression in R - Step 4
      02:48
    • 153 Logistic Regression in R - Step 5a
      05:48
    • 154 Logistic Regression in R - Step 5b
      05:59
    • 155 Logistic Regression in R - Step 5c
      04:59
    • 156 R Classification Template
      05:22
  • Section 17: K-Nearest Neighbors (K-NN)
    7 lectures • 38 min
    • 157 K-Nearest Neighbor Intuition
      04:52
    • 158 K-NN in Python - Step 1
      05:58
    • 159 K-NN in Python - Step 2
      05:51
    • 160 K-NN in Python - Step 3
      05:58
    • 161 K-NN in R - Step 1
      05:54
    • 162 K-NN in R - Step 2
      04:33
    • 163 K-NN in R - Step 3
      04:44
  • Section 18: Support Vector Machine (SVM)
    6 lectures • 36 min
    • 164 SVM Intuition
      09:49
    • 165 SVM in Python - Step 1
      05:58
    • 166 SVM in Python - Step 2
      05:53
    • 167 SVM in Python - Step 3
      02:39
    • 168 SVM in R - Step 1
      05:47
    • 169 SVM in R - Step 2
      05:27
  • Section 19: Kernel SVM
    10 lectures • 1.1 hr
    • 170 Kernel SVM Intuition
      03:17
    • 171 Mapping to a higher dimension
      07:50
    • 172 The Kernel Trick
      12:20
    • 173 Types of Kernel Functions
      02:24
    • 174 Non-Linear Kernel SVR (Advanced)
      10:55
    • 175 Kernel SVM in Python - Step 1
      05:59
    • 176 Kernel SVM in Python - Step 2
      05:59
    • 177 Kernel SVM in R - Step 1
      05:42
    • 178 Kernel SVM in R - Step 2
      05:41
    • 179 Kernel SVM in R - Step 3
      04:58
  • Section 20: Naive Bayes
    10 lectures • 1.3 hr
    • 180 Bayes Theorem
      20:25
    • 181 Naive Bayes Intuition
      14:03
    • 182 Naive Bayes Intuition (Challenge Reveal)
      06:04
    • 183 Naive Bayes Intuition (Extras)
      09:41
    • 184 Naive Bayes in Python - Step 1
      05:56
    • 185 Naive Bayes in Python - Step 2
      05:48
    • 186 Naive Bayes in Python - Step 3
      01:35
    • 187 Naive Bayes in R - Step 1
      04:53
    • 188 Naive Bayes in R - Step 2
      04:41
    • 189 Naive Bayes in R - Step 3
      03:29
  • Section 21: Decision Tree Classification
    6 lectures • 38 min
    • 190 Decision Tree Classification Intuition
      08:08
    • 191 Decision Tree Classification in Python - Step 1
      05:59
    • 192 Decision Tree Classification in Python - Step 2
      05:56
    • 193 Decision Tree Classification in R - Step 1
      05:55
    • 194 Decision Tree Classification in R - Step 2
      05:51
    • 195 Decision Tree Classification in R - Step 3
      05:42
  • Section 22: Random Forest Classification
    6 lectures • 34 min
    • 196 Random Forest Classification Intuition
      04:28
    • 197 Random Forest Classification in Python - Step 1
      05:56
    • 198 Random Forest Classification in Python - Step 2
      05:56
    • 199 Random Forest Classification in R - Step 1
      05:56
    • 200 Random Forest Classification in R - Step 2
      05:58
    • 201 Random Forest Classification in R - Step 3
      05:26
  • Section 23: Classification Model Selection in Python
    5 lectures • 25 min
    • 202 Confusion Matrix & Accuracy Ratios
      04:52
    • 203 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 1
      05:51
    • 204 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 2
      05:59
    • 205 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 3
      05:52
    • 206 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 4
      02:38
  • Section 24: Evaluating Classification Models Performance
    4 lectures • 28 min
    • 207 False Positives & False Negatives
      07:57
    • 208 Accuracy Paradox
      02:12
    • 209 CAP Curve
      11:16
    • 210 CAP Curve Analysis
      06:19
  • Section 25: -------------------- Part 4: Clustering --------------------
  • Section 26: K-Means Clustering
    17 lectures • 1.4 hr
    • 211 What is Clustering? (Supervised vs Unsupervised Learning)
      03:19
    • 212 K-Means Clustering Intuition
      02:37
    • 213 The Elbow Method
      03:59
    • 214 K-Means++
      04:48
    • 215 K-Means Clustering in Python - Step 1a
      04:59
    • 216 K-Means Clustering in Python - Step 1b
      02:58
    • 217 K-Means Clustering in Python - Step 2a
      04:55
    • 218 K-Means Clustering in Python - Step 2b
      05:25
    • 219 K-Means Clustering in Python - Step 3a
      05:59
    • 220 K-Means Clustering in Python - Step 3b
      05:57
    • 221 K-Means Clustering in Python - Step 3c
      03:58
    • 222 K-Means Clustering in Python - Step 4
      05:58
    • 223 K-Means Clustering in Python - Step 5a
      05:59
    • 224 K-Means Clustering in Python - Step 5b
      04:57
    • 225 K-Means Clustering in Python - Step 5c
      06:59
    • 226 K-Means Clustering in R - Step 1
      05:59
    • 227 K-Means Clustering in R - Step 2
      05:39
  • Section 27: Hierarchical Clustering
    14 lectures • 1.3 hr
    • 228 Hierarchical Clustering Intuition
      08:47
    • 229 Hierarchical Clustering How Dendrograms Work
      08:47
    • 230 Hierarchical Clustering Using Dendrograms
      11:21
    • 231 Hierarchical Clustering in Python - Step 1
      05:58
    • 232 Hierarchical Clustering in Python - Step 2a
      04:52
    • 233 Hierarchical Clustering in Python - Step 2b
      05:58
    • 234 Hierarchical Clustering in Python - Step 2c
      05:59
    • 235 Hierarchical Clustering in Python - Step 3a
      05:45
    • 236 Hierarchical Clustering in Python - Step 3b
      05:42
    • 237 Hierarchical Clustering in R - Step 1
      03:45
    • 238 Hierarchical Clustering in R - Step 2
      05:23
    • 239 Hierarchical Clustering in R - Step 3
      03:18
    • 240 Hierarchical Clustering in R - Step 4
      02:45
    • 241 Hierarchical Clustering in R - Step 5
      02:33
  • Section 28: -------------------- Part 5: Association Rule Learning --------------------
  • Section 29: Apriori
    8 lectures • 2.2 hr
    • 242 Apriori Intuition
      18:13
    • 243 Apriori in Python - Step 1
      08:46
    • 244 Apriori in Python - Step 2
      17:07
    • 245 Apriori in Python - Step 3
      12:48
    • 246 Apriori in Python - Step 4
      19:41
    • 247 Apriori in R - Step 1
      19:53
    • 248 Apriori in R - Step 2
      14:24
    • 249 Apriori in R - Step 3
      19:17
  • Section 30: Eclat
    3 lectures • 28 min
    • 250 Eclat Intuition
      06:05
    • 251 Eclat in Python
      12:00
    • 252 Eclat in R
      10:09
  • Section 31: -------------------- Part 6: Reinforcement Learning --------------------
  • Section 32: Upper Confidence Bound (UCB)
    13 lectures • 2.4 hr
    • 253 The Multi-Armed Bandit Problem
      15:36
    • 254 Upper Confidence Bound (UCB) Intuition
      14:53
    • 255 Upper Confidence Bound in Python - Step 1
      12:42
    • 256 Upper Confidence Bound in Python - Step 2
      03:51
    • 257 Upper Confidence Bound in Python - Step 3
      07:16
    • 258 Upper Confidence Bound in Python - Step 4
      15:45
    • 259 Upper Confidence Bound in Python - Step 5
      06:12
    • 260 Upper Confidence Bound in Python - Step 6
      07:28
    • 261 Upper Confidence Bound in Python - Step 7
      08:09
    • 262 Upper Confidence Bound in R - Step 1
      13:39
    • 263 Upper Confidence Bound in R - Step 2
      15:58
    • 264 Upper Confidence Bound in R - Step 3
      17:37
    • 265 Upper Confidence Bound in R - Step 4
      03:18
  • Section 33: Thompson Sampling
    8 lectures • 1.5 hr
    • 266 Thompson Sampling Intuition
      19:12
    • 267 Algorithm Comparison: UCB vs Thompson Sampling
      08:12
    • 268 Thompson Sampling in Python - Step 1
      05:47
    • 269 Thompson Sampling in Python - Step 2
      12:19
    • 270 Thompson Sampling in Python - Step 3
      14:03
    • 271 Thompson Sampling in Python - Step 4
      07:45
    • 272 Thompson Sampling in R - Step 1
      19:01
    • 273 Thompson Sampling in R - Step 2
      03:27
  • Section 34: -------------------- Part 7: Natural Language Processing --------------------
    20 lectures • 3.0 hr
    • 274 NLP Intuition
      03:02
    • 275 Types of Natural Language Processing
      04:11
    • 276 Classical vs Deep Learning Models
      11:22
    • 277 Bag-Of-Words Model
      17:05
    • 278 Natural Language Processing in Python - Step 1
      07:13
    • 279 Natural Language Processing in Python - Step 2
      06:45
    • 280 Natural Language Processing in Python - Step 3
      12:54
    • 281 Natural Language Processing in Python - Step 4
      11:00
    • 282 Natural Language Processing in Python - Step 5
      17:24
    • 283 Natural Language Processing in Python - Step 6
      09:52
    • 284 Natural Language Processing in R - Step 1
      16:35
    • 285 Natural Language Processing in R - Step 2
      08:39
    • 286 Natural Language Processing in R - Step 3
      06:27
    • 287 Natural Language Processing in R - Step 4
      02:57
    • 288 Natural Language Processing in R - Step 5
      02:05
    • 289 Natural Language Processing in R - Step 6
      05:49
    • 290 Natural Language Processing in R - Step 7
      03:26
    • 291 Natural Language Processing in R - Step 8
      05:20
    • 292 Natural Language Processing in R - Step 9
      12:50
    • 293 Natural Language Processing in R - Step 10
      17:31
  • Section 35: -------------------- Part 8: Deep Learning --------------------
    1 lecture • 13 min
    • 294 What is Deep Learning?
      12:34
  • Section 36: Artificial Neural Networks
    18 lectures • 3.4 hr
    • 295 Plan of attack
      02:51
    • 296 The Neuron
      16:24
    • 297 The Activation Function
      08:29
    • 298 How do Neural Networks work?
      12:47
    • 299 How do Neural Networks learn?
      12:58
    • 300 Gradient Descent
      10:12
    • 301 Stochastic Gradient Descent
      08:44
    • 302 Backpropagation
      05:21
    • 303 Business Problem Description
      04:59
    • 304 ANN in Python - Step 1
      10:21
    • 305 ANN in Python - Step 2
      18:36
    • 306 ANN in Python - Step 3
      14:28
    • 307 ANN in Python - Step 4
      11:58
    • 308 ANN in Python - Step 5
      16:25
    • 309 ANN in R - Step 1
      17:17
    • 310 ANN in R - Step 2
      06:30
    • 311 ANN in R - Step 3
      12:29
    • 312 ANN in R - Step 4 (Last step)
      14:07
  • Section 37: Convolutional Neural Networks
    15 lectures • 3.2 hr
    • 313 Plan of attack
      03:31
    • 314 What are convolutional neural networks?
      15:49
    • 315 Step 1 - Convolution Operation
      16:38
    • 316 Step 1(b) - ReLU Layer
      06:41
    • 317 Step 2 - Pooling
      14:13
    • 318 Step 3 - Flattening
      01:52
    • 319 Step 4 - Full Connection
      19:24
    • 320 Summary
      04:19
    • 321 Softmax & Cross-Entropy
      18:20
    • 322 CNN in Python - Step 1
      11:35
    • 323 CNN in Python - Step 2
      17:46
    • 324 CNN in Python - Step 3
      17:56
    • 325 CNN in Python - Step 4
      07:21
    • 326 CNN in Python - Step 5
      14:55
    • 327 CNN in Python - FINAL DEMO!
      23:38
  • Section 38: -------------------- Part 9: Dimensionality Reduction --------------------
  • Section 39: Principal Component Analysis (PCA)
    6 lectures • 1.1 hr
    • 328 Principal Component Analysis (PCA) Intuition
      03:49
    • 329 PCA in Python - Step 1
      16:52
    • 330 PCA in Python - Step 2
      05:30
    • 331 PCA in R - Step 1
      12:08
    • 332 PCA in R - Step 2
      11:22
    • 333 PCA in R - Step 3
      13:42
  • Section 40: Linear Discriminant Analysis (LDA)
    3 lectures • 39 min
    • 334 Linear Discriminant Analysis (LDA) Intuition
      03:50
    • 335 LDA in Python
      14:52
    • 336 LDA in R
      19:59
  • Section 41: Kernel PCA
    2 lectures • 32 min
    • 337 Kernel PCA in Python
      11:03
    • 338 Kernel PCA in R
      20:30
  • Section 42: -------------------- Part 10: Model Selection & Boosting --------------------
  • Section 43: Model Selection
    6 lectures • 1.4 hr
    • 339 k-Fold Cross-Validation Intuition
      08:57
    • 340 Bias-Variance Tradeoff
      04:47
    • 341 k-Fold Cross Validation in Python
      13:45
    • 342 Grid Search in Python
      21:56
    • 343 k-Fold Cross Validation in R
      19:29
    • 344 Grid Search in R
      13:59
  • Section 44: XGBoost
    2 lectures • 33 min
    • 345 XGBoost in Python
      14:48
    • 346 XGBoost in R
      18:14
  • Section 45: Annex: Logistic Regression (Long Explanation)
    1 lecture • 17 min
    • 347 Logistic Regression Intuition
      17:06
  • Section 46: Congratulations!! Don't forget your Prize :)

Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

42h 24m 55s
English
October 5, 2024
Kirill

This course is designed to help you master machine learning algorithms using both Python and R. You'll gain hands-on experience from two data science experts, utilizing included code templates. It covers various machine learning techniques, including data preprocessing, regression, classification, clustering, association rule learning, reinforcement learning, natural language processing, and deep learning. It was originally published on udemy and is available Here for free streaming. If you find this course helpful, consider purchasing the original to support the creator!

Course Details

  • Instructor: Kirill Eremenko
  • Total Sections: 46
  • Total Lectures: 387
  • Url of original content:https://udemy.com/course/machinelearning/
  • Contributor: Suraj Singh

Thank you Suraj Singh for sharing this valuable course.

By the end of the course, you'll be able to:

  • Understand and apply various data preprocessing techniques.
  • Build and evaluate simple and multiple linear regression models.
  • Implement polynomial and support vector regression.
  • Use decision tree and random forest regression.
  • Understand the principles of logistic regression and K-Nearest Neighbors.
  • Implement Support Vector Machines and Kernel SVM.
  • Work with Naive Bayes and Decision Tree classification models.
  • Apply clustering techniques like K-Means and hierarchical clustering.
  • Understand association rule learning using Apriori and Eclat algorithms.
  • Apply reinforcement learning algorithms like Upper Confidence Bound (UCB) and Thompson Sampling.

This comprehensive course, Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024], provides a thorough exploration of machine learning. It starts with an introduction to the course, then moves on to data preprocessing in both Python and R. The course covers regression techniques, progressing from simple to more complex models like Polynomial Regression, SVR, Decision Tree Regression and Random Forest Regression. Classification algorithms are explored including Logistic Regression, KNN, SVM, Kernel SVM, Naive Bayes, and Decision Tree. The course also tackles clustering methodologies like K-Means and Hierarchical Clustering. Topics extend to Association Rule Learning with Apriori and Eclat. Reinforcement Learning is introduced via UCB and Thompson Sampling. The user will also delve into Natural Language Processing and Deep Learning. Furthermore, dimensionality reduction, modeling selection and boosting is also covered.

The course guides individuals to create machine learning algorithms using both programming languages and provides code templates. It is designed for those looking to gain a comprehensive understanding of Machine Learning from both theoretical and practical perspective.

Happy learning!