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文档列表

37 Convolutional Neural Networks/289 Section-40-Convolutional-Neural-Networks-CNN.zip 234.9 MB
29 Apriori/197 Apriori in Python - Step 4.mp4 172.3 MB
37 Convolutional Neural Networks/295 CNN in Python - FINAL DEMO.mp4 160.2 MB
43 Model Selection/315 Grid Search in Python.mp4 159.2 MB
17 K-Nearest Neighbors (K-NN)/130 K-NN in Python.mp4 153.7 MB
23 Classification Model Selection in Python/160 THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION.mp4 142.6 MB
27 Hierarchical Clustering/183 Hierarchical Clustering in Python - Step 2.mp4 142.5 MB
13 Regression Model Selection in Python/103 Preparation of the Regression Code Templates.mp4 129.6 MB
26 K-Means Clustering/176 K-Means Clustering in Python - Step 5.mp4 126.4 MB
16 Logistic Regression/118 Logistic Regression in Python - Step 7.mp4 124.4 MB
37 Convolutional Neural Networks/292 CNN in Python - Step 3.mp4 124.3 MB
39 Principal Component Analysis (PCA)/300 PCA in Python - Step 1.mp4 118.4 MB
43 Model Selection/314 k-Fold Cross Validation in Python.mp4 117.8 MB
36 Artificial Neural Networks/270 ANN in Python - Step 2.mp4 116.4 MB
21 Decision Tree Classification/153 Decision Tree Classification in Python.mp4 113.3 MB
29 Apriori/195 Apriori in Python - Step 2.mp4 112.9 MB
37 Convolutional Neural Networks/291 CNN in Python - Step 2.mp4 112.1 MB
18 Support Vector Machine (SVM)/134 SVM in Python.mp4 109.8 MB
34 -------------------- Part 7 Natural Language Processing --------------------/234 Bag-Of-Words Model.mp4 108.5 MB
40 Linear Discriminant Analysis (LDA)/307 LDA in Python.mp4 106.9 MB
03 Data Preprocessing in Python/025 Feature Scaling.mp4 106.6 MB
36 Artificial Neural Networks/273 ANN in Python - Step 5.mp4 106.3 MB
20 Naive Bayes/149 Naive Bayes in Python.mp4 105.3 MB
37 Convolutional Neural Networks/294 CNN in Python - Step 5.mp4 102.4 MB
22 Random Forest Classification/157 Random Forest Classification in Python.mp4 101.4 MB
01 Welcome to the course/010 Presentation of the ML A-Z folder Colaboratory Jupyter Notebook and Spyder.mp4 99.4 MB
16 Logistic Regression/124 Logistic Regression in R - Step 5.mp4 98.3 MB
09 Support Vector Regression (SVR)/087 SVR in Python - Step 5.mp4 98.2 MB
44 XGBoost/319 XGBoost in Python.mp4 94.4 MB
34 -------------------- Part 7 Natural Language Processing --------------------/240 Natural Language Processing in Python - Step 5.mp4 94.0 MB
03 Data Preprocessing in Python/023 Encoding Categorical Data.mp4 92.9 MB
19 Kernel SVM/142 Kernel SVM in Python.mp4 92.7 MB
09 Support Vector Regression (SVR)/084 SVR in Python - Step 2.mp4 91.1 MB
04 Data Preprocessing in R/033 Splitting the dataset into the Training set and Test set.mp4 90.7 MB
32 Upper Confidence Bound (UCB)/212 Upper Confidence Bound in Python - Step 4.mp4 89.5 MB
16 Logistic Regression/113 Logistic Regression in Python - Step 2.mp4 88.8 MB
34 -------------------- Part 7 Natural Language Processing --------------------/233 Classical vs Deep Learning Models.mp4 88.0 MB
26 K-Means Clustering/174 K-Means Clustering in Python - Step 3.mp4 85.3 MB
04 Data Preprocessing in R/034 Feature Scaling.mp4 82.7 MB
33 Thompson Sampling/225 Thompson Sampling in Python - Step 3.mp4 82.5 MB
08 Polynomial Regression/073 Polynomial Regression in Python - Step 3.mp4 81.6 MB
41 Kernel PCA/310 Kernel PCA in Python.mp4 81.3 MB
30 Eclat/203 Eclat in Python.mp4 79.2 MB
27 Hierarchical Clustering/184 Hierarchical Clustering in Python - Step 3.mp4 78.9 MB
36 Artificial Neural Networks/271 ANN in Python - Step 3.mp4 78.7 MB
06 Simple Linear Regression/043 Simple Linear Regression in Python - Step 4.mp4 78.2 MB
11 Random Forest Regression/098 Random Forest Regression in Python.mp4 78.0 MB
07 Multiple Linear Regression/060 Multiple Linear Regression in Python - Step 4.mp4 76.0 MB
03 Data Preprocessing in Python/020 Importing the Dataset.mp4 75.3 MB
37 Convolutional Neural Networks/290 CNN in Python - Step 1.mp4 74.2 MB
33 Thompson Sampling/224 Thompson Sampling in Python - Step 2.mp4 73.4 MB
29 Apriori/194 Apriori in Python - Step 1.mp4 73.2 MB
08 Polynomial Regression/072 Polynomial Regression in Python - Step 2.mp4 72.7 MB
29 Apriori/196 Apriori in Python - Step 3.mp4 72.6 MB
03 Data Preprocessing in Python/022 Taking care of Missing Data.mp4 72.4 MB
21 Decision Tree Classification/154 Decision Tree Classification in R.mp4 71.5 MB
03 Data Preprocessing in Python/024 Splitting the dataset into the Training set and Test set.mp4 70.9 MB
36 Artificial Neural Networks/268 ANN in Python - Step 1.mp4 69.7 MB
19 Kernel SVM/140 Non-Linear Kernel SVR (Advanced).mp4 68.8 MB
36 Artificial Neural Networks/272 ANN in Python - Step 4.mp4 68.5 MB
18 Support Vector Machine (SVM)/135 SVM in R.mp4 68.5 MB
22 Random Forest Classification/158 Random Forest Classification in R.mp4 67.2 MB
07 Multiple Linear Regression/058 Multiple Linear Regression in Python - Step 2.mp4 65.4 MB
34 -------------------- Part 7 Natural Language Processing --------------------/238 Natural Language Processing in Python - Step 3.mp4 63.5 MB
34 -------------------- Part 7 Natural Language Processing --------------------/239 Natural Language Processing in Python - Step 4.mp4 63.0 MB
32 Upper Confidence Bound (UCB)/209 Upper Confidence Bound in Python - Step 1.mp4 61.6 MB
08 Polynomial Regression/071 Polynomial Regression in Python - Step 1.mp4 61.1 MB
07 Multiple Linear Regression/059 Multiple Linear Regression in Python - Step 3.mp4 61.0 MB
32 Upper Confidence Bound (UCB)/218 Upper Confidence Bound in R - Step 3.mp4 60.6 MB
04 Data Preprocessing in R/032 Encoding Categorical Data.mp4 60.1 MB
13 Regression Model Selection in Python/104 THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION.mp4 59.5 MB
41 Kernel PCA/311 Kernel PCA in R.mp4 59.3 MB
29 Apriori/200 Apriori in R - Step 3.mp4 59.3 MB
07 Multiple Linear Regression/054 Understanding the P-Value.mp4 59.2 MB
10 Decision Tree Regression/095 Decision Tree Regression in R.mp4 59.0 MB
17 K-Nearest Neighbors (K-NN)/131 K-NN in R.mp4 58.5 MB
08 Polynomial Regression/077 Polynomial Regression in R - Step 3.mp4 57.5 MB
10 Decision Tree Regression/094 Decision Tree Regression in Python - Step 4.mp4 57.4 MB
03 Data Preprocessing in Python/018 Getting Started.mp4 57.0 MB
34 -------------------- Part 7 Natural Language Processing --------------------/253 Natural Language Processing in R - Step 10.mp4 56.8 MB
26 K-Means Clustering/173 K-Means Clustering in Python - Step 2.mp4 56.7 MB
16 Logistic Regression/117 Logistic Regression in Python - Step 6.mp4 55.5 MB
34 -------------------- Part 7 Natural Language Processing --------------------/241 Natural Language Processing in Python - Step 6.mp4 55.5 MB
29 Apriori/198 Apriori in R - Step 1.mp4 55.4 MB
19 Kernel SVM/143 Kernel SVM in R.mp4 55.4 MB
44 XGBoost/322 THANK YOU bonus video.mp4 54.8 MB
11 Random Forest Regression/099 Random Forest Regression in R.mp4 54.4 MB
40 Linear Discriminant Analysis (LDA)/308 LDA in R.mp4 53.8 MB
34 -------------------- Part 7 Natural Language Processing --------------------/244 Natural Language Processing in R - Step 1.mp4 53.7 MB
33 Thompson Sampling/228 Thompson Sampling in R - Step 1.mp4 53.5 MB
07 Multiple Linear Regression/057 Multiple Linear Regression in Python - Step 1.mp4 53.4 MB
07 Multiple Linear Regression/066 Multiple Linear Regression in R - Backward Elimination - HOMEWORK.mp4 53.3 MB
04 Data Preprocessing in R/035 Data Preprocessing Template.mp4 53.2 MB
20 Naive Bayes/144 Bayes Theorem.mp4 52.9 MB
36 Artificial Neural Networks/274 ANN in R - Step 1.mp4 52.3 MB
20 Naive Bayes/150 Naive Bayes in R.mp4 52.2 MB
06 Simple Linear Regression/048 Simple Linear Regression in R - Step 4.mp4 51.5 MB
06 Simple Linear Regression/040 Simple Linear Regression in Python - Step 1.mp4 51.0 MB
44 XGBoost/321 XGBoost in R.mp4 49.6 MB
09 Support Vector Regression (SVR)/086 SVR in Python - Step 4.mp4 48.5 MB
07 Multiple Linear Regression/064 Multiple Linear Regression in R - Step 2.mp4 47.4 MB
16 Logistic Regression/115 Logistic Regression in Python - Step 4.mp4 47.4 MB
32 Upper Confidence Bound (UCB)/214 Upper Confidence Bound in Python - Step 6.mp4 47.1 MB
33 Thompson Sampling/226 Thompson Sampling in Python - Step 4.mp4 46.8 MB
16 Logistic Regression/112 Logistic Regression in Python - Step 1.mp4 46.8 MB
36 Artificial Neural Networks/277 ANN in R - Step 4 (Last step).mp4 45.9 MB
43 Model Selection/316 k-Fold Cross Validation in R.mp4 45.8 MB
32 Upper Confidence Bound (UCB)/215 Upper Confidence Bound in Python - Step 7.mp4 45.4 MB
16 Logistic Regression/114 Logistic Regression in Python - Step 3.mp4 45.1 MB
37 Convolutional Neural Networks/286 Step 4 - Full Connection.mp4 44.8 MB
09 Support Vector Regression (SVR)/083 SVR in Python - Step 1.mp4 44.6 MB
10 Decision Tree Regression/091 Decision Tree Regression in Python - Step 1.mp4 44.5 MB
39 Principal Component Analysis (PCA)/301 PCA in Python - Step 2.mp4 42.8 MB
34 -------------------- Part 7 Natural Language Processing --------------------/237 Natural Language Processing in Python - Step 2.mp4 42.4 MB
37 Convolutional Neural Networks/284 Step 2 - Pooling.mp4 42.2 MB
27 Hierarchical Clustering/182 Hierarchical Clustering in Python - Step 1.mp4 42.2 MB
37 Convolutional Neural Networks/293 CNN in Python - Step 4.mp4 42.0 MB
06 Simple Linear Regression/041 Simple Linear Regression in Python - Step 2.mp4 41.8 MB
04 Data Preprocessing in R/031 Taking care of Missing Data.mp4 41.7 MB
29 Apriori/199 Apriori in R - Step 2.mp4 40.7 MB
08 Polynomial Regression/074 Polynomial Regression in Python - Step 4.mp4 40.7 MB
32 Upper Confidence Bound (UCB)/211 Upper Confidence Bound in Python - Step 3.mp4 40.3 MB
26 K-Means Clustering/172 K-Means Clustering in Python - Step 1.mp4 39.9 MB
36 Artificial Neural Networks/276 ANN in R - Step 3.mp4 39.7 MB
34 -------------------- Part 7 Natural Language Processing --------------------/252 Natural Language Processing in R - Step 9.mp4 39.5 MB
33 Thompson Sampling/220 Thompson Sampling Intuition.mp4 39.1 MB
26 K-Means Clustering/177 K-Means Clustering in R.mp4 38.7 MB
09 Support Vector Regression (SVR)/080 SVR Intuition (Updated).mp4 38.6 MB
39 Principal Component Analysis (PCA)/304 PCA in R - Step 3.mp4 38.5 MB
43 Model Selection/317 Grid Search in R.mp4 37.3 MB
26 K-Means Clustering/175 K-Means Clustering in Python - Step 4.mp4 36.8 MB
29 Apriori/192 Apriori Intuition.mp4 36.7 MB
09 Support Vector Regression (SVR)/085 SVR in Python - Step 3.mp4 36.5 MB
19 Kernel SVM/138 The Kernel Trick.mp4 36.4 MB
32 Upper Confidence Bound (UCB)/217 Upper Confidence Bound in R - Step 2.mp4 35.8 MB
34 -------------------- Part 7 Natural Language Processing --------------------/236 Natural Language Processing in Python - Step 1.mp4 35.7 MB
32 Upper Confidence Bound (UCB)/216 Upper Confidence Bound in R - Step 1.mp4 35.7 MB
09 Support Vector Regression (SVR)/088 SVR in R.mp4 35.4 MB
37 Convolutional Neural Networks/288 Softmax Cross-Entropy.mp4 34.8 MB
07 Multiple Linear Regression/055 Multiple Linear Regression Intuition - Step 5.mp4 34.4 MB
32 Upper Confidence Bound (UCB)/213 Upper Confidence Bound in Python - Step 5.mp4 34.0 MB
08 Polynomial Regression/076 Polynomial Regression in R - Step 2.mp4 33.9 MB
39 Principal Component Analysis (PCA)/298 Principal Component Analysis (PCA) Intuition.mp4 33.7 MB
08 Polynomial Regression/079 R Regression Template.mp4 32.9 MB
35 -------------------- Part 8 Deep Learning --------------------/257 What is Deep Learning.mp4 32.8 MB
20 Naive Bayes/145 Naive Bayes Intuition.mp4 32.6 MB
37 Convolutional Neural Networks/282 Step 1 - Convolution Operation.mp4 32.5 MB
39 Principal Component Analysis (PCA)/302 PCA in R - Step 1.mp4 32.1 MB
16 Logistic Regression/116 Logistic Regression in Python - Step 5.mp4 32.1 MB
33 Thompson Sampling/223 Thompson Sampling in Python - Step 1.mp4 32.1 MB
32 Upper Confidence Bound (UCB)/206 The Multi-Armed Bandit Problem.mp4 31.7 MB
26 K-Means Clustering/168 K-Means Clustering Intuition.mp4 31.4 MB
36 Artificial Neural Networks/259 The Neuron.mp4 31.3 MB
37 Convolutional Neural Networks/281 What are convolutional neural networks.mp4 30.9 MB
32 Upper Confidence Bound (UCB)/207 Upper Confidence Bound (UCB) Intuition.mp4 30.7 MB
36 Artificial Neural Networks/266 Business Problem Description.mp4 30.7 MB
16 Logistic Regression/110 Logistic Regression Intuition.mp4 30.6 MB
39 Principal Component Analysis (PCA)/303 PCA in R - Step 2.mp4 30.4 MB
08 Polynomial Regression/078 Polynomial Regression in R - Step 4.mp4 29.9 MB
14 Regression Model Selection in R/106 Evaluating Regression Models Performance - Homeworks Final Part.mp4 29.7 MB
06 Simple Linear Regression/042 Simple Linear Regression in Python - Step 3.mp4 29.6 MB
16 Logistic Regression/121 Logistic Regression in R - Step 3.mp4 28.8 MB
14 Regression Model Selection in R/107 Interpreting Linear Regression Coefficients.mp4 28.7 MB
40 Linear Discriminant Analysis (LDA)/305 Linear Discriminant Analysis (LDA) Intuition.mp4 28.3 MB
36 Artificial Neural Networks/262 How do Neural Networks learn.mp4 27.8 MB
10 Decision Tree Regression/092 Decision Tree Regression in Python - Step 2.mp4 27.5 MB
26 K-Means Clustering/170 K-Means Selecting The Number Of Clusters.mp4 26.9 MB
22 Random Forest Classification/155 Random Forest Classification Intuition.mp4 26.9 MB
10 Decision Tree Regression/089 Decision Tree Regression Intuition.mp4 26.6 MB
30 Eclat/204 Eclat in R.mp4 26.5 MB
06 Simple Linear Regression/046 Simple Linear Regression in R - Step 2.mp4 26.1 MB
36 Artificial Neural Networks/261 How do Neural Networks work.mp4 24.7 MB
07 Multiple Linear Regression/063 Multiple Linear Regression in R - Step 1.mp4 24.6 MB
01 Welcome to the course/011 Installing R and R Studio (Mac Linux Windows).mp4 24.3 MB
27 Hierarchical Clustering/180 Hierarchical Clustering Using Dendrograms.mp4 23.9 MB
34 -------------------- Part 7 Natural Language Processing --------------------/232 Types of Natural Language Processing.mp4 23.6 MB
07 Multiple Linear Regression/067 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 23.0 MB
34 -------------------- Part 7 Natural Language Processing --------------------/245 Natural Language Processing in R - Step 2.mp4 22.7 MB
21 Decision Tree Classification/151 Decision Tree Classification Intuition.mp4 22.7 MB
12 Evaluating Regression Models Performance/101 Adjusted R-Squared Intuition.mp4 22.5 MB
08 Polynomial Regression/075 Polynomial Regression in R - Step 1.mp4 22.2 MB
01 Welcome to the course/008 Updates on Udemy Reviews.mp4 21.4 MB
24 Evaluating Classification Models Performance/164 CAP Curve.mp4 21.3 MB
18 Support Vector Machine (SVM)/132 SVM Intuition.mp4 20.9 MB
09 Support Vector Regression (SVR)/081 Heads-up on non-linear SVR.mp4 20.7 MB
10 Decision Tree Regression/093 Decision Tree Regression in Python - Step 3.mp4 20.4 MB
20 Naive Bayes/147 Naive Bayes Intuition (Extras).mp4 19.9 MB
36 Artificial Neural Networks/263 Gradient Descent.mp4 19.4 MB
36 Artificial Neural Networks/275 ANN in R - Step 2.mp4 19.1 MB
32 Upper Confidence Bound (UCB)/210 Upper Confidence Bound in Python - Step 2.mp4 18.6 MB
16 Logistic Regression/125 R Classification Template.mp4 18.4 MB
27 Hierarchical Clustering/179 Hierarchical Clustering How Dendrograms Work.mp4 18.3 MB
34 -------------------- Part 7 Natural Language Processing --------------------/251 Natural Language Processing in R - Step 8.mp4 18.1 MB
34 -------------------- Part 7 Natural Language Processing --------------------/246 Natural Language Processing in R - Step 3.mp4 17.7 MB
36 Artificial Neural Networks/264 Stochastic Gradient Descent.mp4 17.6 MB
07 Multiple Linear Regression/052 Multiple Linear Regression Intuition - Step 3.mp4 17.4 MB
27 Hierarchical Clustering/178 Hierarchical Clustering Intuition.mp4 17.3 MB
04 Data Preprocessing in R/030 Importing the Dataset.mp4 17.2 MB
34 -------------------- Part 7 Natural Language Processing --------------------/249 Natural Language Processing in R - Step 6.mp4 16.9 MB
03 Data Preprocessing in Python/019 Importing the Libraries.mp4 16.7 MB
16 Logistic Regression/119 Logistic Regression in R - Step 1.mp4 16.5 MB
19 Kernel SVM/139 Types of Kernel Functions.mp4 16.5 MB
11 Random Forest Regression/096 Random Forest Regression Intuition.mp4 16.4 MB
19 Kernel SVM/137 Mapping to a higher dimension.mp4 16.1 MB
26 K-Means Clustering/169 K-Means Random Initialization Trap.mp4 16.1 MB
24 Evaluating Classification Models Performance/161 False Positives False Negatives.mp4 15.9 MB
16 Logistic Regression/120 Logistic Regression in R - Step 2.mp4 15.6 MB
36 Artificial Neural Networks/260 The Activation Function.mp4 15.5 MB
01 Welcome to the course/005 Why Machine Learning is the Future.mp4 15.2 MB
37 Convolutional Neural Networks/283 Step 1(b) - ReLU Layer.mp4 14.8 MB
33 Thompson Sampling/221 Algorithm Comparison UCB vs Thompson Sampling.mp4 14.8 MB
27 Hierarchical Clustering/186 Hierarchical Clustering in R - Step 2.mp4 14.5 MB
07 Multiple Linear Regression/065 Multiple Linear Regression in R - Step 3.mp4 14.5 MB
27 Hierarchical Clustering/189 Hierarchical Clustering in R - Step 5.mp4 14.3 MB
20 Naive Bayes/146 Naive Bayes Intuition (Challenge Reveal).mp4 13.9 MB
24 Evaluating Classification Models Performance/165 CAP Curve Analysis.mp4 13.6 MB
34 -------------------- Part 7 Natural Language Processing --------------------/231 NLP Intuition.mp4 13.3 MB
07 Multiple Linear Regression/049 Dataset Business Problem Description.mp4 13.2 MB
04 Data Preprocessing in R/029 Dataset Description.mp4 12.4 MB
16 Logistic Regression/122 Logistic Regression in R - Step 4.mp4 12.3 MB
06 Simple Linear Regression/045 Simple Linear Regression in R - Step 1.mp4 12.1 MB
06 Simple Linear Regression/047 Simple Linear Regression in R - Step 3.mp4 12.0 MB
36 Artificial Neural Networks/265 Backpropagation.mp4 11.5 MB
30 Eclat/201 Eclat Intuition.mp4 11.2 MB
06 Simple Linear Regression/037 Simple Linear Regression Intuition - Step 1.mp4 11.0 MB
17 K-Nearest Neighbors (K-NN)/128 K-Nearest Neighbor Intuition.mp4 11.0 MB
27 Hierarchical Clustering/188 Hierarchical Clustering in R - Step 4.mp4 10.7 MB
27 Hierarchical Clustering/187 Hierarchical Clustering in R - Step 3.mp4 10.4 MB
01 Welcome to the course/001 Applications of Machine Learning.mp4 10.3 MB
04 Data Preprocessing in R/027 Getting Started.mp4 10.3 MB
12 Evaluating Regression Models Performance/100 R-Squared Intuition.mp4 10.3 MB
34 -------------------- Part 7 Natural Language Processing --------------------/250 Natural Language Processing in R - Step 7.mp4 10.1 MB
33 Thompson Sampling/229 Thompson Sampling in R - Step 2.mp4 10.0 MB
32 Upper Confidence Bound (UCB)/219 Upper Confidence Bound in R - Step 4.mp4 10.0 MB
08 Polynomial Regression/069 Polynomial Regression Intuition.mp4 9.9 MB
24 Evaluating Classification Models Performance/162 Confusion Matrix.mp4 9.3 MB
27 Hierarchical Clustering/185 Hierarchical Clustering in R - Step 1.mp4 9.0 MB
34 -------------------- Part 7 Natural Language Processing --------------------/247 Natural Language Processing in R - Step 4.mp4 8.6 MB
37 Convolutional Neural Networks/287 Summary.mp4 8.3 MB
19 Kernel SVM/136 Kernel SVM Intuition.mp4 6.7 MB
06 Simple Linear Regression/038 Simple Linear Regression Intuition - Step 2.mp4 6.3 MB
37 Convolutional Neural Networks/280 Plan of attack.mp4 6.2 MB
34 -------------------- Part 7 Natural Language Processing --------------------/248 Natural Language Processing in R - Step 5.mp4 6.1 MB
07 Multiple Linear Regression/053 Multiple Linear Regression Intuition - Step 4.mp4 5.6 MB
01 Welcome to the course/009 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
03 Data Preprocessing in Python/017 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
06 Simple Linear Regression/039 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
07 Multiple Linear Regression/056 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
08 Polynomial Regression/070 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
09 Support Vector Regression (SVR)/082 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
10 Decision Tree Regression/090 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
11 Random Forest Regression/097 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
16 Logistic Regression/111 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
17 K-Nearest Neighbors (K-NN)/129 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
18 Support Vector Machine (SVM)/133 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
19 Kernel SVM/141 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
20 Naive Bayes/148 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
21 Decision Tree Classification/152 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
22 Random Forest Classification/156 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
26 K-Means Clustering/171 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
27 Hierarchical Clustering/181 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
29 Apriori/193 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
30 Eclat/202 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
32 Upper Confidence Bound (UCB)/208 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
33 Thompson Sampling/222 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
34 -------------------- Part 7 Natural Language Processing --------------------/235 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
36 Artificial Neural Networks/267 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
39 Principal Component Analysis (PCA)/299 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
40 Linear Discriminant Analysis (LDA)/306 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
41 Kernel PCA/309 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
43 Model Selection/313 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
44 XGBoost/318 Machine-Learning-A-Z-Codes-and-Datasets.zip 5.5 MB
36 Artificial Neural Networks/258 Plan of attack.mp4 5.0 MB
24 Evaluating Classification Models Performance/163 Accuracy Paradox.mp4 4.4 MB
37 Convolutional Neural Networks/285 Step 3 - Flattening.mp4 3.4 MB
01 Welcome to the course/007 Machine-Learning-A-Z-Q-A.pdf 2.4 MB
07 Multiple Linear Regression/051 Multiple Linear Regression Intuition - Step 2.mp4 2.1 MB
07 Multiple Linear Regression/050 Multiple Linear Regression Intuition - Step 1.mp4 2.1 MB
13 Regression Model Selection in Python/105 Regression-Bonus.zip 373.2 kB
14 Regression Model Selection in R/108 Regression-Bonus.zip 373.2 kB
13 Regression Model Selection in Python/102 Machine-Learning-A-Z-Model-Selection.zip 163.8 kB
23 Classification Model Selection in Python/159 Machine-Learning-A-Z-Model-Selection.zip 163.8 kB
30 Eclat/204 Eclat.zip 49.7 kB
24 Evaluating Classification Models Performance/166 Classification-Pros-Cons.pdf 30.0 kB
27 Hierarchical Clustering/190 Clustering-Pros-Cons.pdf 26.4 kB
18 Support Vector Machine (SVM)/135 SVM.zip 8.5 kB
45 Bonus Lectures/323 YOUR SPECIAL BONUS.html 5.7 kB
01 Welcome to the course/015 Your Shortcut To Becoming A Better Data Scientist.html 4.9 kB
07 Multiple Linear Regression/061 Multiple Linear Regression in Python - Backward Elimination.html 4.5 kB
24 Evaluating Classification Models Performance/166 Conclusion of Part 3 - Classification.html 4.3 kB
01 Welcome to the course/006 Important notes tips tricks for this course.html 4.2 kB
01 Welcome to the course/014 FAQBot.html 3.9 kB
33 Thompson Sampling/227 Additional Resource for this Section.html 3.2 kB
01 Welcome to the course/009 GET ALL THE CODES AND DATASETS HERE.html 2.8 kB
13 Regression Model Selection in Python/105 Conclusion of Part 2 - Regression.html 2.6 kB
14 Regression Model Selection in R/108 Conclusion of Part 2 - Regression.html 2.6 kB
34 -------------------- Part 7 Natural Language Processing --------------------/230 Welcome to Part 7 - Natural Language Processing.html 2.6 kB
31 -------------------- Part 6 Reinforcement Learning --------------------/205 Welcome to Part 6 - Reinforcement Learning.html 2.4 kB
03 Data Preprocessing in Python/021 For Python learners summary of Object-oriented programming classes objects.html 2.4 kB
01 Welcome to the course/007 This PDF resource will help you a lot.html 2.4 kB
34 -------------------- Part 7 Natural Language Processing --------------------/254 Homework Challenge.html 2.3 kB
01 Welcome to the course/002 BONUS Learning Paths.html 2.3 kB
34 -------------------- Part 7 Natural Language Processing --------------------/243 Homework Challenge.html 2.3 kB
16 Logistic Regression/123 Warning - Update.html 2.2 kB
38 -------------------- Part 9 Dimensionality Reduction --------------------/297 Welcome to Part 9 - Dimensionality Reduction.html 2.2 kB
07 Multiple Linear Regression/062 Multiple Linear Regression in Python - BONUS.html 2.1 kB
44 XGBoost/320 Model Selection and Boosting BONUS.html 2.0 kB
06 Simple Linear Regression/044 Simple Linear Regression in Python - BONUS.html 2.0 kB
34 -------------------- Part 7 Natural Language Processing --------------------/242 Natural Language Processing in Python - BONUS.html 2.0 kB
01 Welcome to the course/012 BONUS Meet your instructors.html 1.9 kB
23 Classification Model Selection in Python/159 Make sure you have this Model Selection folder ready.html 1.9 kB
36 Artificial Neural Networks/278 Deep Learning BONUS 1.html 1.9 kB
13 Regression Model Selection in Python/102 Make sure you have this Model Selection folder ready.html 1.9 kB
42 -------------------- Part 10 Model Selection Boosting --------------------/312 Welcome to Part 10 - Model Selection Boosting.html 1.8 kB
37 Convolutional Neural Networks/296 Deep Learning BONUS 2.html 1.8 kB
34 -------------------- Part 7 Natural Language Processing --------------------/255 BONUS NLP BERT.html 1.8 kB
05 -------------------- Part 2 Regression --------------------/036 Welcome to Part 2 - Regression.html 1.7 kB
35 -------------------- Part 8 Deep Learning --------------------/256 Welcome to Part 8 - Deep Learning.html 1.7 kB
16 Logistic Regression/126 Machine Learning Regression and Classification BONUS.html 1.7 kB
15 -------------------- Part 3 Classification --------------------/109 Welcome to Part 3 - Classification.html 1.7 kB
37 Convolutional Neural Networks/289 Make sure you have your dataset ready.html 1.7 kB
06 Simple Linear Regression/039 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
07 Multiple Linear Regression/056 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
08 Polynomial Regression/070 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
09 Support Vector Regression (SVR)/082 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
10 Decision Tree Regression/090 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
11 Random Forest Regression/097 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
16 Logistic Regression/111 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
17 K-Nearest Neighbors (K-NN)/129 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
18 Support Vector Machine (SVM)/133 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
19 Kernel SVM/141 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
20 Naive Bayes/148 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
21 Decision Tree Classification/152 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
22 Random Forest Classification/156 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
26 K-Means Clustering/171 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
27 Hierarchical Clustering/181 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
29 Apriori/193 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
30 Eclat/202 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
32 Upper Confidence Bound (UCB)/208 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
33 Thompson Sampling/222 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
34 -------------------- Part 7 Natural Language Processing --------------------/235 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
36 Artificial Neural Networks/267 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
39 Principal Component Analysis (PCA)/299 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
40 Linear Discriminant Analysis (LDA)/306 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
41 Kernel PCA/309 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
43 Model Selection/313 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
44 XGBoost/318 Make sure you have your Machine Learning A-Z folder ready.html 1.7 kB
07 Multiple Linear Regression/068 Multiple Linear Regression in R - Automatic Backward Elimination.html 1.6 kB
25 -------------------- Part 4 Clustering --------------------/167 Welcome to Part 4 - Clustering.html 1.6 kB
03 Data Preprocessing in Python/017 Make sure you have your Machine Learning A-Z folder ready.html 1.6 kB
16 Logistic Regression/127 BONUS Logistic Regression Practical Case Study.html 1.5 kB
04 Data Preprocessing in R/026 Welcome.html 1.5 kB
01 Welcome to the course/013 Some Additional Resources.html 1.4 kB
36 Artificial Neural Networks/269 Check out our free course on ANN for Regression.html 1.4 kB
02 -------------------- Part 1 Data Preprocessing --------------------/016 Welcome to Part 1 - Data Preprocessing.html 1.4 kB
01 Welcome to the course/003 BONUS 2 ML vs DL vs AI Whats the Difference.html 1.4 kB
36 Artificial Neural Networks/279 BONUS ANN Case Study.html 1.4 kB
27 Hierarchical Clustering/190 Conclusion of Part 4 - Clustering.html 1.4 kB
01 Welcome to the course/004 BONUS 3 Regression Types.html 1.4 kB
04 Data Preprocessing in R/028 Make sure you have your dataset ready.html 1.3 kB
28 -------------------- Part 5 Association Rule Learning --------------------/191 Welcome to Part 5 - Association Rule Learning.html 1.3 kB

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