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[GigaCourse.Com] Udemy - Machine Learning A-Z™ - AI, Python & R + ChatGPT Bonus [2023]

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种子名称： [GigaCourse.Com] Udemy - Machine Learning A-Z™ - AI, Python & R + ChatGPT Bonus [2023]
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文件数目： 343个文件
文件大小： 15.91 GB
收录时间： 2024-9-8 22:49
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[GigaCourse.Com] Udemy - Machine Learning A-Z™ - AI, Python & R + ChatGPT Bonus [2023].torrent
  • 1 - Welcome to the course Here we will help you get started in the best conditions/2 - Machine Learning Demo Get Excited.mp450.77MB
  • 1 - Welcome to the course Here we will help you get started in the best conditions/4 - How to use the ML AZ folder Google Colab.mp438.04MB
  • 1 - Welcome to the course Here we will help you get started in the best conditions/5 - Installing R and R Studio Mac Linux Windows.mp452.47MB
  • 10 - Decision Tree Regression/114 - Decision Tree Regression Intuition.mp429.62MB
  • 10 - Decision Tree Regression/115 - Decision Tree Regression in Python Step 1a.mp413.63MB
  • 10 - Decision Tree Regression/116 - Decision Tree Regression in Python Step 1b.mp416.75MB
  • 10 - Decision Tree Regression/117 - Decision Tree Regression in Python Step 2.mp417.86MB
  • 10 - Decision Tree Regression/118 - Decision Tree Regression in Python Step 3.mp412.89MB
  • 10 - Decision Tree Regression/119 - Decision Tree Regression in Python Step 4.mp417.58MB
  • 10 - Decision Tree Regression/120 - Decision Tree Regression in R Step 1.mp426.83MB
  • 10 - Decision Tree Regression/121 - Decision Tree Regression in R Step 2.mp464.4MB
  • 10 - Decision Tree Regression/122 - Decision Tree Regression in R Step 3.mp415.08MB
  • 10 - Decision Tree Regression/123 - Decision Tree Regression in R Step 4.mp418.26MB
  • 11 - Random Forest Regression/124 - Random Forest Regression Intuition.mp453.92MB
  • 11 - Random Forest Regression/125 - Random Forest Regression in Python Step 1.mp426.43MB
  • 11 - Random Forest Regression/126 - Random Forest Regression in Python Step 2.mp443.8MB
  • 11 - Random Forest Regression/127 - Random Forest Regression in R Step 1.mp432.93MB
  • 11 - Random Forest Regression/128 - Random Forest Regression in R Step 2.mp434.74MB
  • 11 - Random Forest Regression/129 - Random Forest Regression in R Step 3.mp427.04MB
  • 12 - Evaluating Regression Models Performance/130 - RSquared Intuition.mp416.54MB
  • 12 - Evaluating Regression Models Performance/131 - Adjusted RSquared Intuition.mp415.71MB
  • 13 - Regression Model Selection in Python/133 - Preparation of the Regression Code Templates Step 1.mp414.94MB
  • 13 - Regression Model Selection in Python/134 - Preparation of the Regression Code Templates Step 2.mp433.07MB
  • 13 - Regression Model Selection in Python/135 - Preparation of the Regression Code Templates Step 3.mp421.7MB
  • 13 - Regression Model Selection in Python/136 - Preparation of the Regression Code Templates Step 4.mp441.45MB
  • 13 - Regression Model Selection in Python/137 - THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION STEP 1.mp434.48MB
  • 13 - Regression Model Selection in Python/138 - THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION STEP 2.mp446.49MB
  • 14 - Regression Model Selection in R/140 - Evaluating Regression Models Performance Homeworks Final Part.mp446.33MB
  • 14 - Regression Model Selection in R/141 - Interpreting Linear Regression Coefficients.mp486.86MB
  • 16 - Logistic Regression/144 - What is Classification.mp47.89MB
  • 16 - Logistic Regression/145 - Logistic Regression Intuition.mp424.75MB
  • 16 - Logistic Regression/146 - Maximum Likelihood.mp49.08MB
  • 16 - Logistic Regression/147 - Logistic Regression in Python Step 1a.mp417.2MB
  • 16 - Logistic Regression/148 - Logistic Regression in Python Step 1b.mp413.65MB
  • 16 - Logistic Regression/149 - Logistic Regression in Python Step 2a.mp443.99MB
  • 16 - Logistic Regression/150 - Logistic Regression in Python Step 2b.mp451.38MB
  • 16 - Logistic Regression/151 - Logistic Regression in Python Step 3a.mp432.58MB
  • 16 - Logistic Regression/152 - Logistic Regression in Python Step 3b.mp411.56MB
  • 16 - Logistic Regression/153 - Logistic Regression in Python Step 4a.mp427MB
  • 16 - Logistic Regression/154 - Logistic Regression in Python Step 4b.mp46.67MB
  • 16 - Logistic Regression/155 - Logistic Regression in Python Step 5.mp426.68MB
  • 16 - Logistic Regression/156 - Logistic Regression in Python Step 6a.mp422.1MB
  • 16 - Logistic Regression/157 - Logistic Regression in Python Step 6b.mp417.28MB
  • 16 - Logistic Regression/158 - Logistic Regression in Python Step 7a.mp431.29MB
  • 16 - Logistic Regression/159 - Logistic Regression in Python Step 7b.mp438.55MB
  • 16 - Logistic Regression/160 - Logistic Regression in Python Step 7c.mp431.79MB
  • 16 - Logistic Regression/162 - Logistic Regression in R Step 1.mp432.5MB
  • 16 - Logistic Regression/163 - Logistic Regression in R Step 2.mp422.75MB
  • 16 - Logistic Regression/164 - Logistic Regression in R Step 3.mp448.89MB
  • 16 - Logistic Regression/165 - Logistic Regression in R Step 4.mp450.73MB
  • 16 - Logistic Regression/167 - Logistic Regression in R Step 5a.mp449.29MB
  • 16 - Logistic Regression/168 - Logistic Regression in R Step 5b.mp441.58MB
  • 16 - Logistic Regression/169 - Logistic Regression in R Step 5c.mp465.46MB
  • 16 - Logistic Regression/171 - R Classification Template.mp440.65MB
  • 17 - KNearest Neighbors KNN/174 - KNearest Neighbor Intuition.mp413.41MB
  • 17 - KNearest Neighbors KNN/175 - KNN in Python Step 1.mp457.81MB
  • 17 - KNearest Neighbors KNN/176 - KNN in Python Step 2.mp452.72MB
  • 17 - KNearest Neighbors KNN/177 - KNN in Python Step 3.mp453.69MB
  • 17 - KNearest Neighbors KNN/178 - KNN in R Step 1.mp468.65MB
  • 17 - KNearest Neighbors KNN/179 - KNN in R Step 2.mp429.85MB
  • 17 - KNearest Neighbors KNN/180 - KNN in R Step 3.mp462.09MB
  • 18 - Support Vector Machine SVM/181 - SVM Intuition.mp426.68MB
  • 18 - Support Vector Machine SVM/182 - SVM in Python Step 1.mp482.86MB
  • 18 - Support Vector Machine SVM/183 - SVM in Python Step 2.mp456.37MB
  • 18 - Support Vector Machine SVM/184 - SVM in Python Step 3.mp419.18MB
  • 18 - Support Vector Machine SVM/185 - SVM in R Step 1.mp483.79MB
  • 18 - Support Vector Machine SVM/186 - SVM in R Step 2.mp472.55MB
  • 19 - Kernel SVM/187 - Kernel SVM Intuition.mp49.13MB
  • 19 - Kernel SVM/188 - Mapping to a higher dimension.mp441.43MB
  • 19 - Kernel SVM/189 - The Kernel Trick.mp452.69MB
  • 19 - Kernel SVM/190 - Types of Kernel Functions.mp415.78MB
  • 19 - Kernel SVM/191 - NonLinear Kernel SVR Advanced.mp445.29MB
  • 19 - Kernel SVM/192 - Kernel SVM in Python Step 1.mp457.02MB
  • 19 - Kernel SVM/193 - Kernel SVM in Python Step 2.mp456.5MB
  • 19 - Kernel SVM/194 - Kernel SVM in R Step 1.mp455.33MB
  • 19 - Kernel SVM/195 - Kernel SVM in R Step 2.mp430.23MB
  • 19 - Kernel SVM/196 - Kernel SVM in R Step 3.mp464.44MB
  • 2 - Part 1 Data Preprocessing/10 - Feature Scaling.mp417.92MB
  • 2 - Part 1 Data Preprocessing/8 - The Machine Learning process.mp47.98MB
  • 2 - Part 1 Data Preprocessing/9 - Splitting the data into a Training and Test set.mp47.85MB
  • 20 - Naive Bayes/197 - Bayes Theorem.mp4195.21MB
  • 20 - Naive Bayes/198 - Naive Bayes Intuition.mp476.25MB
  • 20 - Naive Bayes/199 - Naive Bayes Intuition Challenge Reveal.mp418.1MB
  • 20 - Naive Bayes/200 - Naive Bayes Intuition Extras.mp424.01MB
  • 20 - Naive Bayes/201 - Naive Bayes in Python Step 1.mp485.62MB
  • 20 - Naive Bayes/202 - Naive Bayes in Python Step 2.mp464.77MB
  • 20 - Naive Bayes/203 - Naive Bayes in Python Step 3.mp410.61MB
  • 20 - Naive Bayes/204 - Naive Bayes in R Step 1.mp430.61MB
  • 20 - Naive Bayes/205 - Naive Bayes in R Step 2.mp438.83MB
  • 20 - Naive Bayes/206 - Naive Bayes in R Step 3.mp445.9MB
  • 21 - Decision Tree Classification/207 - Decision Tree Classification Intuition.mp423.66MB
  • 21 - Decision Tree Classification/208 - Decision Tree Classification in Python Step 1.mp462.32MB
  • 21 - Decision Tree Classification/209 - Decision Tree Classification in Python Step 2.mp452.64MB
  • 21 - Decision Tree Classification/210 - Decision Tree Classification in R Step 1.mp489.87MB
  • 21 - Decision Tree Classification/211 - Decision Tree Classification in R Step 2.mp471.93MB
  • 21 - Decision Tree Classification/212 - Decision Tree Classification in R Step 3.mp436MB
  • 22 - Random Forest Classification/213 - Random Forest Classification Intuition.mp467.99MB
  • 22 - Random Forest Classification/214 - Random Forest Classification in Python Step 1.mp456.26MB
  • 22 - Random Forest Classification/215 - Random Forest Classification in Python Step 2.mp452.3MB
  • 22 - Random Forest Classification/216 - Random Forest Classification in R Step 1.mp438.89MB
  • 22 - Random Forest Classification/217 - Random Forest Classification in R Step 2.mp462.9MB
  • 22 - Random Forest Classification/218 - Random Forest Classification in R Step 3.mp476.84MB
  • 23 - Classification Model Selection in Python/220 - Confusion Matrix Accuracy Ratios.mp428.7MB
  • 23 - Classification Model Selection in Python/221 - ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION STEP 1.mp431.24MB
  • 23 - Classification Model Selection in Python/222 - ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION STEP 2.mp451.84MB
  • 23 - Classification Model Selection in Python/223 - ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION STEP 3.mp433.97MB
  • 23 - Classification Model Selection in Python/224 - ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION STEP 4.mp412.45MB
  • 24 - Evaluating Classification Models Performance/225 - False Positives False Negatives.mp426.7MB
  • 24 - Evaluating Classification Models Performance/226 - Accuracy Paradox.mp45.89MB
  • 24 - Evaluating Classification Models Performance/227 - CAP Curve.mp425.64MB
  • 24 - Evaluating Classification Models Performance/228 - CAP Curve Analysis.mp421.1MB
  • 26 - KMeans Clustering/231 - What is Clustering Supervised vs Unsupervised Learning.mp415.45MB
  • 26 - KMeans Clustering/232 - KMeans Clustering Intuition.mp44.98MB
  • 26 - KMeans Clustering/233 - The Elbow Method.mp49.63MB
  • 26 - KMeans Clustering/234 - KMeans.mp418.73MB
  • 26 - KMeans Clustering/235 - KMeans Clustering in Python Step 1a.mp415.19MB
  • 26 - KMeans Clustering/236 - KMeans Clustering in Python Step 1b.mp423.18MB
  • 26 - KMeans Clustering/237 - KMeans Clustering in Python Step 2a.mp420.07MB
  • 26 - KMeans Clustering/238 - KMeans Clustering in Python Step 2b.mp419.12MB
  • 26 - KMeans Clustering/239 - KMeans Clustering in Python Step 3a.mp419.14MB
  • 26 - KMeans Clustering/240 - KMeans Clustering in Python Step 3b.mp419.53MB
  • 26 - KMeans Clustering/241 - KMeans Clustering in Python Step 3c.mp414.27MB
  • 26 - KMeans Clustering/242 - KMeans Clustering in Python Step 4.mp424.55MB
  • 26 - KMeans Clustering/243 - KMeans Clustering in Python Step 5a.mp422.99MB
  • 26 - KMeans Clustering/244 - KMeans Clustering in Python Step 5b.mp435.71MB
  • 26 - KMeans Clustering/245 - KMeans Clustering in Python Step 5c.mp441.33MB
  • 26 - KMeans Clustering/246 - KMeans Clustering in R Step 1.mp424.02MB
  • 26 - KMeans Clustering/247 - KMeans Clustering in R Step 2.mp447.13MB
  • 27 - Hierarchical Clustering/248 - Hierarchical Clustering Intuition.mp436.21MB
  • 27 - Hierarchical Clustering/249 - Hierarchical Clustering How Dendrograms Work.mp425.9MB
  • 27 - Hierarchical Clustering/250 - Hierarchical Clustering Using Dendrograms.mp440.92MB
  • 27 - Hierarchical Clustering/251 - Hierarchical Clustering in Python Step 1.mp431.03MB
  • 27 - Hierarchical Clustering/252 - Hierarchical Clustering in Python Step 2a.mp416.13MB
  • 27 - Hierarchical Clustering/253 - Hierarchical Clustering in Python Step 2b.mp436.81MB
  • 27 - Hierarchical Clustering/254 - Hierarchical Clustering in Python Step 2c.mp447.06MB
  • 27 - Hierarchical Clustering/255 - Hierarchical Clustering in Python Step 3a.mp425.85MB
  • 27 - Hierarchical Clustering/256 - Hierarchical Clustering in Python Step 3b.mp423.37MB
  • 27 - Hierarchical Clustering/257 - Hierarchical Clustering in R Step 1.mp412.22MB
  • 27 - Hierarchical Clustering/258 - Hierarchical Clustering in R Step 2.mp421.01MB
  • 27 - Hierarchical Clustering/259 - Hierarchical Clustering in R Step 3.mp449.26MB
  • 27 - Hierarchical Clustering/260 - Hierarchical Clustering in R Step 4.mp433.96MB
  • 27 - Hierarchical Clustering/261 - Hierarchical Clustering in R Step 5.mp425.72MB
  • 29 - Apriori/264 - Apriori Intuition.mp481.56MB
  • 29 - Apriori/265 - Apriori in Python Step 1.mp4101.7MB
  • 29 - Apriori/266 - Apriori in Python Step 2.mp4131.63MB
  • 29 - Apriori/267 - Apriori in Python Step 3.mp458.63MB
  • 29 - Apriori/268 - Apriori in Python Step 4.mp4192.47MB
  • 29 - Apriori/269 - Apriori in R Step 1.mp4123.16MB
  • 29 - Apriori/270 - Apriori in R Step 2.mp4161.94MB
  • 29 - Apriori/271 - Apriori in R Step 3.mp4269.02MB
  • 3 - Data Preprocessing in Python/11 - Getting Started Step 1.mp415.28MB
  • 3 - Data Preprocessing in Python/12 - Getting Started Step 2.mp453.33MB
  • 3 - Data Preprocessing in Python/13 - Importing the Libraries.mp411.08MB
  • 3 - Data Preprocessing in Python/14 - Importing the Dataset Step 1.mp418.31MB
  • 3 - Data Preprocessing in Python/15 - Importing the Dataset Step 2.mp414.36MB
  • 3 - Data Preprocessing in Python/16 - Importing the Dataset Step 3.mp420.58MB
  • 3 - Data Preprocessing in Python/18 - Taking care of Missing Data Step 1.mp423.97MB
  • 3 - Data Preprocessing in Python/19 - Taking care of Missing Data Step 2.mp445.48MB
  • 3 - Data Preprocessing in Python/20 - Encoding Categorical Data Step 1.mp419.65MB
  • 3 - Data Preprocessing in Python/21 - Encoding Categorical Data Step 2.mp430.43MB
  • 3 - Data Preprocessing in Python/22 - Encoding Categorical Data Step 3.mp421.53MB
  • 3 - Data Preprocessing in Python/23 - Splitting the dataset into the Training set and Test set Step 1.mp415.07MB
  • 3 - Data Preprocessing in Python/24 - Splitting the dataset into the Training set and Test set Step 2.mp420.51MB
  • 3 - Data Preprocessing in Python/25 - Splitting the dataset into the Training set and Test set Step 3.mp417.46MB
  • 3 - Data Preprocessing in Python/26 - Feature Scaling Step 1.mp418.9MB
  • 3 - Data Preprocessing in Python/27 - Feature Scaling Step 2.mp417.38MB
  • 3 - Data Preprocessing in Python/28 - Feature Scaling Step 3.mp416.72MB
  • 3 - Data Preprocessing in Python/29 - Feature Scaling Step 4.mp424.82MB
  • 30 - Eclat/272 - Eclat Intuition.mp435.29MB
  • 30 - Eclat/273 - Eclat in Python.mp491.33MB
  • 30 - Eclat/274 - Eclat in R.mp4110.13MB
  • 32 - Upper Confidence Bound UCB/276 - The MultiArmed Bandit Problem.mp496.44MB
  • 32 - Upper Confidence Bound UCB/277 - Upper Confidence Bound UCB Intuition.mp479.23MB
  • 32 - Upper Confidence Bound UCB/278 - Upper Confidence Bound in Python Step 1.mp466.3MB
  • 32 - Upper Confidence Bound UCB/279 - Upper Confidence Bound in Python Step 2.mp413.69MB
  • 32 - Upper Confidence Bound UCB/280 - Upper Confidence Bound in Python Step 3.mp428.59MB
  • 32 - Upper Confidence Bound UCB/281 - Upper Confidence Bound in Python Step 4.mp463.84MB
  • 32 - Upper Confidence Bound UCB/282 - Upper Confidence Bound in Python Step 5.mp425.87MB
  • 32 - Upper Confidence Bound UCB/283 - Upper Confidence Bound in Python Step 6.mp429.76MB
  • 32 - Upper Confidence Bound UCB/284 - Upper Confidence Bound in Python Step 7.mp431.76MB
  • 32 - Upper Confidence Bound UCB/285 - Upper Confidence Bound in R Step 1.mp456.62MB
  • 32 - Upper Confidence Bound UCB/286 - Upper Confidence Bound in R Step 2.mp4121.64MB
  • 32 - Upper Confidence Bound UCB/287 - Upper Confidence Bound in R Step 3.mp4164.71MB
  • 32 - Upper Confidence Bound UCB/288 - Upper Confidence Bound in R Step 4.mp413.94MB
  • 33 - Thompson Sampling/289 - Thompson Sampling Intuition.mp469.13MB
  • 33 - Thompson Sampling/290 - Algorithm Comparison UCB vs Thompson Sampling.mp423.81MB
  • 33 - Thompson Sampling/291 - Thompson Sampling in Python Step 1.mp419.49MB
  • 33 - Thompson Sampling/292 - Thompson Sampling in Python Step 2.mp451.95MB
  • 33 - Thompson Sampling/293 - Thompson Sampling in Python Step 3.mp462.18MB
  • 33 - Thompson Sampling/294 - Thompson Sampling in Python Step 4.mp432.27MB
  • 33 - Thompson Sampling/296 - Thompson Sampling in R Step 1.mp498.77MB
  • 33 - Thompson Sampling/297 - Thompson Sampling in R Step 2.mp416.42MB
  • 34 - Part 7 Natural Language Processing/299 - NLP Intuition.mp47.55MB
  • 34 - Part 7 Natural Language Processing/300 - Types of Natural Language Processing.mp412.13MB
  • 34 - Part 7 Natural Language Processing/301 - Classical vs Deep Learning Models.mp483.96MB
  • 34 - Part 7 Natural Language Processing/302 - BagOfWords Model.mp459.37MB
  • 34 - Part 7 Natural Language Processing/303 - Natural Language Processing in Python Step 1.mp422.43MB
  • 34 - Part 7 Natural Language Processing/304 - Natural Language Processing in Python Step 2.mp458.33MB
  • 34 - Part 7 Natural Language Processing/305 - Natural Language Processing in Python Step 3.mp442.5MB
  • 34 - Part 7 Natural Language Processing/306 - Natural Language Processing in Python Step 4.mp455.18MB
  • 34 - Part 7 Natural Language Processing/307 - Natural Language Processing in Python Step 5.mp4136.84MB
  • 34 - Part 7 Natural Language Processing/308 - Natural Language Processing in Python Step 6.mp473.92MB
  • 34 - Part 7 Natural Language Processing/311 - Natural Language Processing in R Step 1.mp484.05MB
  • 34 - Part 7 Natural Language Processing/313 - Natural Language Processing in R Step 2.mp440.05MB
  • 34 - Part 7 Natural Language Processing/314 - Natural Language Processing in R Step 3.mp431.81MB
  • 34 - Part 7 Natural Language Processing/315 - Natural Language Processing in R Step 4.mp415.02MB
  • 34 - Part 7 Natural Language Processing/316 - Natural Language Processing in R Step 5.mp410.45MB
  • 34 - Part 7 Natural Language Processing/317 - Natural Language Processing in R Step 6.mp429.51MB
  • 34 - Part 7 Natural Language Processing/318 - Natural Language Processing in R Step 7.mp417.78MB
  • 34 - Part 7 Natural Language Processing/319 - Natural Language Processing in R Step 8.mp428.32MB
  • 34 - Part 7 Natural Language Processing/320 - Natural Language Processing in R Step 9.mp467.46MB
  • 34 - Part 7 Natural Language Processing/321 - Natural Language Processing in R Step 10.mp4116.33MB
  • 35 - Part 8 Deep Learning/324 - What is Deep Learning.mp4178.16MB
  • 36 - Artificial Neural Networks/325 - Plan of attack.mp46.81MB
  • 36 - Artificial Neural Networks/326 - The Neuron.mp463.78MB
  • 36 - Artificial Neural Networks/327 - The Activation Function.mp424.1MB
  • 36 - Artificial Neural Networks/328 - How do Neural Networks work.mp467.19MB
  • 36 - Artificial Neural Networks/329 - How do Neural Networks learn.mp466.48MB
  • 36 - Artificial Neural Networks/330 - Gradient Descent.mp438.28MB
  • 36 - Artificial Neural Networks/331 - Stochastic Gradient Descent.mp441.59MB
  • 36 - Artificial Neural Networks/332 - Backpropagation.mp422.89MB
  • 36 - Artificial Neural Networks/333 - Business Problem Description.mp468.44MB
  • 36 - Artificial Neural Networks/334 - ANN in Python Step 1.mp481.06MB
  • 36 - Artificial Neural Networks/335 - ANN in Python Step 2.mp4134.12MB
  • 36 - Artificial Neural Networks/336 - ANN in Python Step 3.mp459.13MB
  • 36 - Artificial Neural Networks/337 - ANN in Python Step 4.mp448.56MB
  • 36 - Artificial Neural Networks/338 - ANN in Python Step 5.mp4119.72MB
  • 36 - Artificial Neural Networks/339 - ANN in R Step 1.mp4227.63MB
  • 36 - Artificial Neural Networks/340 - ANN in R Step 2.mp443.59MB
  • 36 - Artificial Neural Networks/341 - ANN in R Step 3.mp4202.71MB
  • 36 - Artificial Neural Networks/342 - ANN in R Step 4 Last step.mp495.96MB
  • 37 - Convolutional Neural Networks/345 - Plan of attack.mp48.93MB
  • 37 - Convolutional Neural Networks/346 - What are convolutional neural networks.mp4103.85MB
  • 37 - Convolutional Neural Networks/347 - Step 1 Convolution Operation.mp491.59MB
  • 37 - Convolutional Neural Networks/348 - Step 1b ReLU Layer.mp431.05MB
  • 37 - Convolutional Neural Networks/349 - Step 2 Pooling.mp4139.46MB
  • 37 - Convolutional Neural Networks/350 - Step 3 Flattening.mp44.24MB
  • 37 - Convolutional Neural Networks/351 - Step 4 Full Connection.mp498.79MB
  • 37 - Convolutional Neural Networks/352 - Summary.mp416.6MB
  • 37 - Convolutional Neural Networks/353 - Softmax CrossEntropy.mp463.77MB
  • 37 - Convolutional Neural Networks/354 - CNN in Python Step 1.mp450.75MB
  • 37 - Convolutional Neural Networks/355 - CNN in Python Step 2.mp4174.45MB
  • 37 - Convolutional Neural Networks/356 - CNN in Python Step 3.mp4101.48MB
  • 37 - Convolutional Neural Networks/357 - CNN in Python Step 4.mp434.86MB
  • 37 - Convolutional Neural Networks/358 - CNN in Python Step 5.mp4140.16MB
  • 37 - Convolutional Neural Networks/359 - CNN in Python FINAL DEMO.mp4186.09MB
  • 39 - Principal Component Analysis PCA/362 - Principal Component Analysis PCA Intuition.mp435.88MB
  • 39 - Principal Component Analysis PCA/363 - PCA in Python Step 1.mp4136.4MB
  • 39 - Principal Component Analysis PCA/364 - PCA in Python Step 2.mp433.24MB
  • 39 - Principal Component Analysis PCA/365 - PCA in R Step 1.mp4175.79MB
  • 39 - Principal Component Analysis PCA/366 - PCA in R Step 2.mp482.4MB
  • 39 - Principal Component Analysis PCA/367 - PCA in R Step 3.mp4114.06MB
  • 4 - Data Preprocessing in R/30 - Getting Started.mp46.56MB
  • 4 - Data Preprocessing in R/31 - Dataset Description.mp49.59MB
  • 4 - Data Preprocessing in R/32 - Importing the Dataset.mp411.28MB
  • 4 - Data Preprocessing in R/33 - Taking care of Missing Data.mp433.12MB
  • 4 - Data Preprocessing in R/34 - Encoding Categorical Data.mp463.89MB
  • 4 - Data Preprocessing in R/35 - Splitting the dataset into the Training set and Test set Step 1.mp426.28MB
  • 4 - Data Preprocessing in R/36 - Splitting the dataset into the Training set and Test set Step 2.mp432.37MB
  • 4 - Data Preprocessing in R/37 - Feature Scaling Step 1.mp432.08MB
  • 4 - Data Preprocessing in R/38 - Feature Scaling Step 2.mp461.1MB
  • 4 - Data Preprocessing in R/39 - Data Preprocessing Template.mp437.85MB
  • 40 - Linear Discriminant Analysis LDA/368 - Linear Discriminant Analysis LDA Intuition.mp423.57MB
  • 40 - Linear Discriminant Analysis LDA/369 - LDA in Python.mp4122.81MB
  • 40 - Linear Discriminant Analysis LDA/370 - LDA in R.mp4165.27MB
  • 41 - Kernel PCA/371 - Kernel PCA in Python.mp493.25MB
  • 41 - Kernel PCA/372 - Kernel PCA in R.mp4399.31MB
  • 43 - Model Selection/374 - kFold Cross Validation in Python.mp497.77MB
  • 43 - Model Selection/375 - Grid Search in Python.mp4187.7MB
  • 43 - Model Selection/376 - kFold Cross Validation in R.mp490.34MB
  • 43 - Model Selection/377 - Grid Search in R.mp485.88MB
  • 44 - XGBoost/378 - XGBoost in Python.mp4142.56MB
  • 44 - XGBoost/380 - XGBoost in R.mp4120.96MB
  • 46 - Annex Logistic Regression Long Explanation/382 - Logistic Regression Intuition.mp444.95MB
  • 6 - Simple Linear Regression/41 - Simple Linear Regression Intuition.mp46.66MB
  • 6 - Simple Linear Regression/42 - Ordinary Least Squares.mp412.73MB
  • 6 - Simple Linear Regression/43 - Simple Linear Regression in Python Step 1a.mp411.83MB
  • 6 - Simple Linear Regression/44 - Simple Linear Regression in Python Step 1b.mp421.13MB
  • 6 - Simple Linear Regression/45 - Simple Linear Regression in Python Step 2a.mp412.24MB
  • 6 - Simple Linear Regression/46 - Simple Linear Regression in Python Step 2b.mp415.55MB
  • 6 - Simple Linear Regression/47 - Simple Linear Regression in Python Step 3.mp433.81MB
  • 6 - Simple Linear Regression/48 - Simple Linear Regression in Python Step 4a.mp426.62MB
  • 6 - Simple Linear Regression/49 - Simple Linear Regression in Python Step 4b.mp429.27MB
  • 6 - Simple Linear Regression/51 - Simple Linear Regression in R Step 1.mp418.35MB
  • 6 - Simple Linear Regression/52 - Simple Linear Regression in R Step 2.mp433.2MB
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  • 7 - Multiple Linear Regression/56 - Dataset Business Problem Description.mp423.97MB
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  • 7 - Multiple Linear Regression/78 - Multiple Linear Regression in R Backward Elimination HOMEWORK.mp4109.92MB
  • 7 - Multiple Linear Regression/79 - Multiple Linear Regression in R Backward Elimination Homework Solution.mp456.49MB
  • 8 - Polynomial Regression/100 - R Regression Template Step 2.mp421.06MB
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  • 9 - Support Vector Regression SVR/101 - SVR Intuition Updated.mp436.83MB
  • 9 - Support Vector Regression SVR/102 - Headsup on nonlinear SVR.mp419.76MB
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  • 9 - Support Vector Regression SVR/112 - SVR in R Step 1.mp428.63MB
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