[UdemyCourseDownloader] Machine Learning A-Z™ Hands-On Python & R In Data Science -移花宫-武林禁地，闲人禁止入内

[UdemyCourseDownloader] Machine Learning A-Z™ Hands-On Python & R In Data Science

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[UdemyCourseDownloader] Machine Learning A-Z™ Hands-On Python & R In Data Science

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收录时间：2022-01-09
最近下载：2024-12-31

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文件列表

12 Logistic Regression/096 Logistic Regression in R - Step 5.mp4 98.3 MB
31 Artificial Neural Networks/225 ANN in Python - Step 2.mp4 89.0 MB
17 Decision Tree Classification/123 Decision Tree Classification in R.mp4 71.5 MB
14 Support Vector Machine (SVM)/105 SVM in R.mp4 68.5 MB
18 Random Forest Classification/127 Random Forest Classification in R.mp4 67.2 MB
32 Convolutional Neural Networks/256 CNN in Python - Step 9.mp4 65.4 MB
18 Random Forest Classification/126 Random Forest Classification in Python.mp4 65.1 MB
07 Support Vector Regression (SVR)/068 SVR in Python.mp4 63.1 MB
05 Multiple Linear Regression/045 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK.mp4 62.0 MB
27 Upper Confidence Bound (UCB)/178 Upper Confidence Bound in R - Step 3.mp4 60.6 MB
36 Kernel PCA/274 Kernel PCA in R.mp4 59.3 MB
24 Apriori/161 Apriori in R - Step 3.mp4 59.3 MB
08 Decision Tree Regression/073 Decision Tree Regression in R.mp4 59.0 MB
13 K-Nearest Neighbors (K-NN)/101 K-NN in R.mp4 58.5 MB
28 Thompson Sampling/183 Thompson Sampling in Python - Step 1.mp4 58.2 MB
15 Kernel SVM/111 Kernel SVM in Python.mp4 57.5 MB
06 Polynomial Regression/063 Polynomial Regression in R - Step 3.mp4 57.5 MB
06 Polynomial Regression/058 Polynomial Regression in Python - Step 3.mp4 57.1 MB
05 Multiple Linear Regression/046 Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 56.9 MB
29 -------------------- Part 7 Natural Language Processing --------------------/210 Natural Language Processing in R - Step 10.mp4 56.8 MB
27 Upper Confidence Bound (UCB)/174 Upper Confidence Bound in Python - Step 3.mp4 56.3 MB
12 Logistic Regression/090 Logistic Regression in Python - Step 5.mp4 55.7 MB
02 -------------------- Part 1 Data Preprocessing --------------------/015 Categorical Data.mp4 55.4 MB
24 Apriori/159 Apriori in R - Step 1.mp4 55.4 MB
15 Kernel SVM/112 Kernel SVM in R.mp4 55.4 MB
09 Random Forest Regression/076 Random Forest Regression in Python.mp4 55.3 MB
05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Step 1.mp4 54.7 MB
29 -------------------- Part 7 Natural Language Processing --------------------/197 Natural Language Processing in Python - Step 8.mp4 54.5 MB
09 Random Forest Regression/077 Random Forest Regression in R.mp4 54.4 MB
35 Linear Discriminant Analysis (LDA)/271 LDA in R.mp4 53.8 MB
29 -------------------- Part 7 Natural Language Processing --------------------/201 Natural Language Processing in R - Step 1.mp4 53.7 MB
28 Thompson Sampling/185 Thompson Sampling in R - Step 1.mp4 53.5 MB
02 -------------------- Part 1 Data Preprocessing --------------------/017 Splitting the Dataset into the Training set and Test set.mp4 53.4 MB
05 Multiple Linear Regression/051 Multiple Linear Regression in R - Backward Elimination - HOMEWORK.mp4 53.3 MB
16 Naive Bayes/113 Bayes Theorem.mp4 52.9 MB
31 Artificial Neural Networks/234 ANN in R - Step 1.mp4 52.3 MB
21 K-Means Clustering/139 K-Means Clustering in Python.mp4 52.2 MB
16 Naive Bayes/119 Naive Bayes in R.mp4 52.2 MB
04 Simple Linear Regression/032 Simple Linear Regression in R - Step 4.mp4 51.5 MB
24 Apriori/162 Apriori in Python - Step 1.mp4 49.7 MB
39 XGBoost/285 XGBoost in R.mp4 49.6 MB
13 K-Nearest Neighbors (K-NN)/100 K-NN in Python.mp4 49.3 MB
07 Support Vector Regression (SVR)/067 SVR Intuition.mp4 48.9 MB
29 -------------------- Part 7 Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 1.mp4 48.3 MB
35 Linear Discriminant Analysis (LDA)/270 LDA in Python.mp4 47.6 MB
05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2.mp4 47.4 MB
02 -------------------- Part 1 Data Preprocessing --------------------/018 Feature Scaling.mp4 46.8 MB
27 Upper Confidence Bound (UCB)/173 Upper Confidence Bound in Python - Step 2.mp4 46.7 MB
31 Artificial Neural Networks/237 ANN in R - Step 4 (Last step).mp4 45.9 MB
38 Model Selection/278 k-Fold Cross Validation in R.mp4 45.8 MB
08 Decision Tree Regression/072 Decision Tree Regression in Python.mp4 45.5 MB
32 Convolutional Neural Networks/244 Step 4 - Full Connection.mp4 44.8 MB
14 Support Vector Machine (SVM)/104 SVM in Python.mp4 43.7 MB
32 Convolutional Neural Networks/242 Step 2 - Pooling.mp4 42.2 MB
04 Simple Linear Regression/028 Simple Linear Regression in Python - Step 4.mp4 41.3 MB
31 Artificial Neural Networks/228 ANN in Python - Step 5.mp4 41.3 MB
27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in Python - Step 1.mp4 40.9 MB
17 Decision Tree Classification/122 Decision Tree Classification in Python.mp4 40.7 MB
24 Apriori/160 Apriori in R - Step 2.mp4 40.7 MB
38 Model Selection/279 Grid Search in Python - Step 1.mp4 40.1 MB
31 Artificial Neural Networks/236 ANN in R - Step 3.mp4 39.7 MB
29 -------------------- Part 7 Natural Language Processing --------------------/209 Natural Language Processing in R - Step 9.mp4 39.5 MB
31 Artificial Neural Networks/224 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras.mp4 39.3 MB
24 Apriori/163 Apriori in Python - Step 2.mp4 39.1 MB
28 Thompson Sampling/180 Thompson Sampling Intuition.mp4 39.1 MB
21 K-Means Clustering/140 K-Means Clustering in R.mp4 38.7 MB
06 Polynomial Regression/060 Python Regression Template.mp4 38.6 MB
34 Principal Component Analysis (PCA)/267 PCA in R - Step 3.mp4 38.5 MB
38 Model Selection/281 Grid Search in R.mp4 37.3 MB
24 Apriori/164 Apriori in Python - Step 3.mp4 37.0 MB
06 Polynomial Regression/057 Polynomial Regression in Python - Step 2.mp4 36.8 MB
24 Apriori/157 Apriori Intuition.mp4 36.7 MB
15 Kernel SVM/108 The Kernel Trick.mp4 36.4 MB
32 Convolutional Neural Networks/251 CNN in Python - Step 4.mp4 36.3 MB
27 Upper Confidence Bound (UCB)/177 Upper Confidence Bound in R - Step 2.mp4 35.8 MB
31 Artificial Neural Networks/231 ANN in Python - Step 8.mp4 35.7 MB
27 Upper Confidence Bound (UCB)/176 Upper Confidence Bound in R - Step 1.mp4 35.7 MB
07 Support Vector Regression (SVR)/069 SVR in R.mp4 35.4 MB
36 Kernel PCA/273 Kernel PCA in Python.mp4 35.0 MB
32 Convolutional Neural Networks/246 Softmax Cross-Entropy.mp4 34.8 MB
29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - Step 10.mp4 34.5 MB
38 Model Selection/277 k-Fold Cross Validation in Python.mp4 34.4 MB
05 Multiple Linear Regression/040 Multiple Linear Regression Intuition - Step 5.mp4 34.4 MB
06 Polynomial Regression/062 Polynomial Regression in R - Step 2.mp4 33.9 MB
02 -------------------- Part 1 Data Preprocessing --------------------/014 Missing Data.mp4 33.7 MB
34 Principal Component Analysis (PCA)/260 Principal Component Analysis (PCA) Intuition.mp4 33.7 MB
39 XGBoost/284 XGBoost in Python - Step 2.mp4 33.5 MB
34 Principal Component Analysis (PCA)/262 PCA in Python - Step 1.mp4 33.5 MB
06 Polynomial Regression/056 Polynomial Regression in Python - Step 1.mp4 33.2 MB
06 Polynomial Regression/065 R Regression Template.mp4 32.9 MB
30 -------------------- Part 8 Deep Learning --------------------/213 What is Deep Learning.mp4 32.8 MB
16 Naive Bayes/118 Naive Bayes in Python.mp4 32.7 MB
16 Naive Bayes/114 Naive Bayes Intuition.mp4 32.6 MB
32 Convolutional Neural Networks/240 Step 1 - Convolution Operation.mp4 32.5 MB
34 Principal Component Analysis (PCA)/265 PCA in R - Step 1.mp4 32.1 MB
32 Convolutional Neural Networks/248 CNN in Python - Step 1.mp4 32.1 MB
27 Upper Confidence Bound (UCB)/169 The Multi-Armed Bandit Problem.mp4 31.7 MB
21 K-Means Clustering/135 K-Means Clustering Intuition.mp4 31.4 MB
31 Artificial Neural Networks/215 The Neuron.mp4 31.3 MB
29 -------------------- Part 7 Natural Language Processing --------------------/193 Natural Language Processing in Python - Step 4.mp4 31.2 MB
29 -------------------- Part 7 Natural Language Processing --------------------/188 Natural Language Processing Intuition.mp4 31.1 MB
38 Model Selection/280 Grid Search in Python - Step 2.mp4 30.9 MB
32 Convolutional Neural Networks/239 What are convolutional neural networks.mp4 30.9 MB
27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound (UCB) Intuition.mp4 30.7 MB
31 Artificial Neural Networks/223 Business Problem Description.mp4 30.7 MB
12 Logistic Regression/084 Logistic Regression Intuition.mp4 30.6 MB
34 Principal Component Analysis (PCA)/266 PCA in R - Step 2.mp4 30.4 MB
02 -------------------- Part 1 Data Preprocessing --------------------/012 Importing the Dataset.mp4 30.0 MB
06 Polynomial Regression/064 Polynomial Regression in R - Step 4.mp4 29.9 MB
31 Artificial Neural Networks/232 ANN in Python - Step 9.mp4 29.9 MB
31 Artificial Neural Networks/233 ANN in Python - Step 10.mp4 29.8 MB
10 Evaluating Regression Models Performance/080 Evaluating Regression Models Performance - Homeworks Final Part.mp4 29.7 MB
04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1.mp4 29.3 MB
32 Convolutional Neural Networks/257 CNN in Python - Step 10.mp4 29.1 MB
12 Logistic Regression/094 Logistic Regression in R - Step 3.mp4 28.8 MB
29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2.mp4 28.8 MB
10 Evaluating Regression Models Performance/081 Interpreting Linear Regression Coefficients.mp4 28.7 MB
35 Linear Discriminant Analysis (LDA)/268 Linear Discriminant Analysis (LDA) Intuition.mp4 28.3 MB
31 Artificial Neural Networks/218 How do Neural Networks learn.mp4 27.8 MB
02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template.mp4 27.1 MB
21 K-Means Clustering/137 K-Means Selecting The Number Of Clusters.mp4 26.9 MB
18 Random Forest Classification/124 Random Forest Classification Intuition.mp4 26.9 MB
34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3.mp4 26.7 MB
05 Multiple Linear Regression/043 Multiple Linear Regression in Python - Step 3.mp4 26.7 MB
08 Decision Tree Regression/070 Decision Tree Regression Intuition.mp4 26.6 MB
25 Eclat/167 Eclat in R.mp4 26.5 MB
04 Simple Linear Regression/030 Simple Linear Regression in R - Step 2.mp4 26.1 MB
04 Simple Linear Regression/026 Simple Linear Regression in Python - Step 2.mp4 25.8 MB
01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows).mp4 25.1 MB
05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 25.0 MB
31 Artificial Neural Networks/217 How do Neural Networks work.mp4 24.7 MB
05 Multiple Linear Regression/048 Multiple Linear Regression in R - Step 1.mp4 24.6 MB
01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows).mp4 24.3 MB
22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms.mp4 23.9 MB
29 -------------------- Part 7 Natural Language Processing --------------------/196 Natural Language Processing in Python - Step 7.mp4 23.2 MB
34 Principal Component Analysis (PCA)/263 PCA in Python - Step 2.mp4 23.1 MB
05 Multiple Linear Regression/052 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 23.0 MB
29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - Step 2.mp4 22.7 MB
17 Decision Tree Classification/120 Decision Tree Classification Intuition.mp4 22.7 MB
10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition.mp4 22.5 MB
39 XGBoost/283 XGBoost in Python - Step 1.mp4 22.4 MB
22 Hierarchical Clustering/148 HC in Python - Step 4.mp4 22.4 MB
06 Polynomial Regression/061 Polynomial Regression in R - Step 1.mp4 22.2 MB
02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset.mp4 22.2 MB
04 Simple Linear Regression/027 Simple Linear Regression in Python - Step 3.mp4 21.5 MB
19 Evaluating Classification Models Performance/131 CAP Curve.mp4 21.3 MB
14 Support Vector Machine (SVM)/102 SVM Intuition.mp4 20.9 MB
16 Naive Bayes/116 Naive Bayes Intuition (Extras).mp4 19.9 MB
29 -------------------- Part 7 Natural Language Processing --------------------/198 Natural Language Processing in Python - Step 9.mp4 19.8 MB
29 -------------------- Part 7 Natural Language Processing --------------------/194 Natural Language Processing in Python - Step 5.mp4 19.7 MB
31 Artificial Neural Networks/219 Gradient Descent.mp4 19.4 MB
31 Artificial Neural Networks/235 ANN in R - Step 2.mp4 19.1 MB
06 Polynomial Regression/059 Polynomial Regression in Python - Step 4.mp4 18.5 MB
12 Logistic Regression/091 Python Classification Template.mp4 18.4 MB
12 Logistic Regression/097 R Classification Template.mp4 18.4 MB
22 Hierarchical Clustering/142 Hierarchical Clustering How Dendrograms Work.mp4 18.3 MB
29 -------------------- Part 7 Natural Language Processing --------------------/208 Natural Language Processing in R - Step 8.mp4 18.1 MB
29 -------------------- Part 7 Natural Language Processing --------------------/203 Natural Language Processing in R - Step 3.mp4 17.7 MB
12 Logistic Regression/086 Logistic Regression in Python - Step 1.mp4 17.7 MB
31 Artificial Neural Networks/220 Stochastic Gradient Descent.mp4 17.6 MB
32 Convolutional Neural Networks/254 CNN in Python - Step 7.mp4 17.5 MB
05 Multiple Linear Regression/037 Multiple Linear Regression Intuition - Step 3.mp4 17.4 MB
22 Hierarchical Clustering/141 Hierarchical Clustering Intuition.mp4 17.3 MB
22 Hierarchical Clustering/147 HC in Python - Step 3.mp4 17.0 MB
29 -------------------- Part 7 Natural Language Processing --------------------/206 Natural Language Processing in R - Step 6.mp4 16.9 MB
12 Logistic Regression/092 Logistic Regression in R - Step 1.mp4 16.5 MB
15 Kernel SVM/109 Types of Kernel Functions.mp4 16.5 MB
09 Random Forest Regression/074 Random Forest Regression Intuition.mp4 16.4 MB
22 Hierarchical Clustering/146 HC in Python - Step 2.mp4 16.3 MB
15 Kernel SVM/107 Mapping to a higher dimension.mp4 16.1 MB
21 K-Means Clustering/136 K-Means Random Initialization Trap.mp4 16.1 MB
19 Evaluating Classification Models Performance/128 False Positives False Negatives.mp4 15.9 MB
31 Artificial Neural Networks/230 ANN in Python - Step 7.mp4 15.6 MB
12 Logistic Regression/093 Logistic Regression in R - Step 2.mp4 15.6 MB
31 Artificial Neural Networks/216 The Activation Function.mp4 15.5 MB
31 Artificial Neural Networks/226 ANN in Python - Step 3.mp4 15.3 MB
01 Welcome to the course/002 Why Machine Learning is the Future.mp4 15.2 MB
32 Convolutional Neural Networks/241 Step 1(b) - ReLU Layer.mp4 14.8 MB
28 Thompson Sampling/181 Algorithm Comparison UCB vs Thompson Sampling.mp4 14.8 MB
12 Logistic Regression/089 Logistic Regression in Python - Step 4.mp4 14.5 MB
22 Hierarchical Clustering/151 HC in R - Step 2.mp4 14.5 MB
05 Multiple Linear Regression/050 Multiple Linear Regression in R - Step 3.mp4 14.5 MB
22 Hierarchical Clustering/145 HC in Python - Step 1.mp4 14.4 MB
22 Hierarchical Clustering/154 HC in R - Step 5.mp4 14.3 MB
02 -------------------- Part 1 Data Preprocessing --------------------/011 Importing the Libraries.mp4 14.2 MB
16 Naive Bayes/115 Naive Bayes Intuition (Challenge Reveal).mp4 13.9 MB
19 Evaluating Classification Models Performance/132 CAP Curve Analysis.mp4 13.6 MB
05 Multiple Linear Regression/034 Dataset Business Problem Description.mp4 13.2 MB
27 Upper Confidence Bound (UCB)/175 Upper Confidence Bound in Python - Step 4.mp4 13.0 MB
32 Convolutional Neural Networks/252 CNN in Python - Step 5.mp4 13.0 MB
32 Convolutional Neural Networks/253 CNN in Python - Step 6.mp4 12.5 MB
31 Artificial Neural Networks/229 ANN in Python - Step 6.mp4 12.5 MB
12 Logistic Regression/095 Logistic Regression in R - Step 4.mp4 12.3 MB
04 Simple Linear Regression/021 How to get the dataset.mp4 12.3 MB
05 Multiple Linear Regression/033 How to get the dataset.mp4 12.3 MB
06 Polynomial Regression/055 How to get the dataset.mp4 12.3 MB
07 Support Vector Regression (SVR)/066 How to get the dataset.mp4 12.3 MB
08 Decision Tree Regression/071 How to get the dataset.mp4 12.3 MB
09 Random Forest Regression/075 How to get the dataset.mp4 12.3 MB
12 Logistic Regression/085 How to get the dataset.mp4 12.3 MB
13 K-Nearest Neighbors (K-NN)/099 How to get the dataset.mp4 12.3 MB
14 Support Vector Machine (SVM)/103 How to get the dataset.mp4 12.3 MB
15 Kernel SVM/110 How to get the dataset.mp4 12.3 MB
16 Naive Bayes/117 How to get the dataset.mp4 12.3 MB
17 Decision Tree Classification/121 How to get the dataset.mp4 12.3 MB
18 Random Forest Classification/125 How to get the dataset.mp4 12.3 MB
21 K-Means Clustering/138 How to get the dataset.mp4 12.3 MB
22 Hierarchical Clustering/144 How to get the dataset.mp4 12.3 MB
24 Apriori/158 How to get the dataset.mp4 12.3 MB
25 Eclat/166 How to get the dataset.mp4 12.3 MB
27 Upper Confidence Bound (UCB)/171 How to get the dataset.mp4 12.3 MB
28 Thompson Sampling/182 How to get the dataset.mp4 12.3 MB
29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset.mp4 12.3 MB
31 Artificial Neural Networks/222 How to get the dataset.mp4 12.3 MB
32 Convolutional Neural Networks/247 How to get the dataset.mp4 12.3 MB
34 Principal Component Analysis (PCA)/261 How to get the dataset.mp4 12.3 MB
35 Linear Discriminant Analysis (LDA)/269 How to get the dataset.mp4 12.3 MB
36 Kernel PCA/272 How to get the dataset.mp4 12.3 MB
38 Model Selection/276 How to get the dataset.mp4 12.3 MB
39 XGBoost/282 How to get the dataset.mp4 12.3 MB
04 Simple Linear Regression/029 Simple Linear Regression in R - Step 1.mp4 12.1 MB
04 Simple Linear Regression/031 Simple Linear Regression in R - Step 3.mp4 12.0 MB
28 Thompson Sampling/184 Thompson Sampling in Python - Step 2.mp4 11.8 MB
12 Logistic Regression/087 Logistic Regression in Python - Step 2.mp4 11.6 MB
31 Artificial Neural Networks/221 Backpropagation.mp4 11.5 MB
25 Eclat/165 Eclat Intuition.mp4 11.2 MB
04 Simple Linear Regression/023 Simple Linear Regression Intuition - Step 1.mp4 11.0 MB
13 K-Nearest Neighbors (K-NN)/098 K-Nearest Neighbor Intuition.mp4 11.0 MB
22 Hierarchical Clustering/153 HC in R - Step 4.mp4 10.7 MB
22 Hierarchical Clustering/152 HC in R - Step 3.mp4 10.4 MB
22 Hierarchical Clustering/149 HC in Python - Step 5.mp4 10.4 MB
05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2.mp4 10.3 MB
01 Welcome to the course/001 Applications of Machine Learning.mp4 10.3 MB
10 Evaluating Regression Models Performance/078 R-Squared Intuition.mp4 10.3 MB
31 Artificial Neural Networks/227 ANN in Python - Step 4.mp4 10.2 MB
29 -------------------- Part 7 Natural Language Processing --------------------/207 Natural Language Processing in R - Step 7.mp4 10.1 MB
28 Thompson Sampling/186 Thompson Sampling in R - Step 2.mp4 10.0 MB
27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4.mp4 10.0 MB
06 Polynomial Regression/054 Polynomial Regression Intuition.mp4 9.9 MB
32 Convolutional Neural Networks/255 CNN in Python - Step 8.mp4 9.4 MB
19 Evaluating Classification Models Performance/129 Confusion Matrix.mp4 9.3 MB
22 Hierarchical Clustering/150 HC in R - Step 1.mp4 9.0 MB
29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6.mp4 8.7 MB
29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4.mp4 8.6 MB
12 Logistic Regression/088 Logistic Regression in Python - Step 3.mp4 8.4 MB
32 Convolutional Neural Networks/245 Summary.mp4 8.3 MB
04 Simple Linear Regression/022 Dataset Business Problem Description.mp4 8.1 MB
32 Convolutional Neural Networks/249 CNN in Python - Step 2.mp4 7.6 MB
15 Kernel SVM/106 Kernel SVM Intuition.mp4 6.7 MB
04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2.mp4 6.3 MB
32 Convolutional Neural Networks/238 Plan of attack.mp4 6.2 MB
29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5.mp4 6.1 MB
05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4.mp4 5.6 MB
31 Artificial Neural Networks/214 Plan of attack.mp4 5.0 MB
19 Evaluating Classification Models Performance/130 Accuracy Paradox.mp4 4.4 MB
29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3.mp4 4.4 MB
02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing.mp4 3.7 MB
32 Convolutional Neural Networks/243 Step 3 - Flattening.mp4 3.4 MB
32 Convolutional Neural Networks/250 CNN in Python - Step 3.mp4 2.9 MB
01 Welcome to the course/004 Machine-Learning-A-Z-Q-A.pdf 2.4 MB
05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2.mp4 2.1 MB
05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1.mp4 2.1 MB
25 Eclat/167 Eclat.zip 49.7 kB
16 Naive Bayes/113 Bayes Theorem-ja.srt 38.2 kB
18 Random Forest Classification/127 Random Forest Classification in R-ja.srt 38.2 kB
36 Kernel PCA/274 Kernel PCA in R-ja.srt 37.7 kB
08 Decision Tree Regression/073 Decision Tree Regression in R-ja.srt 37.5 kB
32 Convolutional Neural Networks/256 CNN in Python - Step 9-ja.srt 36.5 kB
24 Apriori/161 Apriori in R - Step 3-ja.srt 36.5 kB
35 Linear Discriminant Analysis (LDA)/271 LDA in R-ja.srt 36.4 kB
18 Random Forest Classification/126 Random Forest Classification in Python-ja.srt 36.3 kB
31 Artificial Neural Networks/225 ANN in Python - Step 2-ja.srt 36.3 kB
07 Support Vector Regression (SVR)/068 SVR in Python-ja.srt 36.3 kB
24 Apriori/159 Apriori in R - Step 1-ja.srt 36.0 kB
16 Naive Bayes/113 Bayes Theorem-es.srt 35.5 kB
06 Polynomial Regression/058 Polynomial Regression in Python - Step 3-ja.srt 35.2 kB
16 Naive Bayes/113 Bayes Theorem-pt.srt 34.8 kB
16 Naive Bayes/113 Bayes Theorem-it.srt 34.8 kB
28 Thompson Sampling/183 Thompson Sampling in Python - Step 1-ja.srt 34.8 kB
06 Polynomial Regression/063 Polynomial Regression in R - Step 3-ja.srt 34.5 kB
32 Convolutional Neural Networks/244 Step 4 - Full Connection-ja.srt 34.4 kB
17 Decision Tree Classification/123 Decision Tree Classification in R-ja.srt 34.3 kB
18 Random Forest Classification/127 Random Forest Classification in R-es.srt 34.2 kB
12 Logistic Regression/090 Logistic Regression in Python - Step 5-ja.srt 34.1 kB
36 Kernel PCA/274 Kernel PCA in R-es.srt 34.1 kB
18 Random Forest Classification/127 Random Forest Classification in R-pt.srt 34.0 kB
16 Naive Bayes/113 Bayes Theorem-en.srt 33.9 kB
28 Thompson Sampling/185 Thompson Sampling in R - Step 1-ja.srt 33.8 kB
38 Model Selection/278 k-Fold Cross Validation in R-ja.srt 33.8 kB
15 Kernel SVM/111 Kernel SVM in Python-ja.srt 33.8 kB
18 Random Forest Classification/127 Random Forest Classification in R-it.srt 33.7 kB
36 Kernel PCA/274 Kernel PCA in R-pt.srt 33.7 kB
28 Thompson Sampling/180 Thompson Sampling Intuition-ja.srt 33.7 kB
08 Decision Tree Regression/073 Decision Tree Regression in R-pt.srt 33.6 kB
08 Decision Tree Regression/073 Decision Tree Regression in R-es.srt 33.6 kB
36 Kernel PCA/274 Kernel PCA in R-it.srt 33.5 kB
12 Logistic Regression/096 Logistic Regression in R - Step 5-ja.srt 33.4 kB
21 K-Means Clustering/139 K-Means Clustering in Python-ja.srt 33.3 kB
08 Decision Tree Regression/073 Decision Tree Regression in R-it.srt 33.3 kB
16 Naive Bayes/113 Bayes Theorem-tr.srt 33.1 kB
31 Artificial Neural Networks/234 ANN in R - Step 1-ja.srt 33.0 kB
18 Random Forest Classification/127 Random Forest Classification in R-tr.srt 33.0 kB
27 Upper Confidence Bound (UCB)/174 Upper Confidence Bound in Python - Step 3-ja.srt 33.0 kB
32 Convolutional Neural Networks/256 CNN in Python - Step 9-es.srt 32.8 kB
35 Linear Discriminant Analysis (LDA)/271 LDA in R-es.srt 32.8 kB
06 Polynomial Regression/058 Polynomial Regression in Python - Step 3-es.srt 32.7 kB
36 Kernel PCA/274 Kernel PCA in R-tr.srt 32.7 kB
24 Apriori/162 Apriori in Python - Step 1-ja.srt 32.7 kB
18 Random Forest Classification/126 Random Forest Classification in Python-es.srt 32.7 kB
02 -------------------- Part 1 Data Preprocessing --------------------/017 Splitting the Dataset into the Training set and Test set-ja.srt 32.5 kB
24 Apriori/161 Apriori in R - Step 3-es.srt 32.5 kB
39 XGBoost/285 XGBoost in R-ja.srt 32.4 kB
09 Random Forest Regression/077 Random Forest Regression in R-ja.srt 32.4 kB
09 Random Forest Regression/076 Random Forest Regression in Python-ja.srt 32.4 kB
18 Random Forest Classification/126 Random Forest Classification in Python-pt.srt 32.4 kB
24 Apriori/159 Apriori in R - Step 1-es.srt 32.3 kB
35 Linear Discriminant Analysis (LDA)/271 LDA in R-pt.srt 32.3 kB
07 Support Vector Regression (SVR)/068 SVR in Python-es.srt 32.3 kB
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06 Polynomial Regression/055 How to get the dataset-es.srt 5.2 kB
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13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-es.srt 5.2 kB
14 Support Vector Machine (SVM)/103 How to get the dataset-es.srt 5.2 kB
15 Kernel SVM/110 How to get the dataset-es.srt 5.2 kB
16 Naive Bayes/117 How to get the dataset-es.srt 5.2 kB
17 Decision Tree Classification/121 How to get the dataset-es.srt 5.2 kB
18 Random Forest Classification/125 How to get the dataset-es.srt 5.2 kB
21 K-Means Clustering/138 How to get the dataset-es.srt 5.2 kB
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24 Apriori/158 How to get the dataset-es.srt 5.2 kB
25 Eclat/166 How to get the dataset-es.srt 5.2 kB
27 Upper Confidence Bound (UCB)/171 How to get the dataset-es.srt 5.2 kB
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36 Kernel PCA/272 How to get the dataset-es.srt 5.2 kB
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13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-pt.srt 5.2 kB
14 Support Vector Machine (SVM)/103 How to get the dataset-pt.srt 5.2 kB
15 Kernel SVM/110 How to get the dataset-pt.srt 5.2 kB
16 Naive Bayes/117 How to get the dataset-pt.srt 5.2 kB
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13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-tr.srt 5.0 kB
14 Support Vector Machine (SVM)/103 How to get the dataset-tr.srt 5.0 kB
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16 Naive Bayes/117 How to get the dataset-en.srt 4.9 kB
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18 Random Forest Classification/125 How to get the dataset-en.srt 4.9 kB
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24 Apriori/158 How to get the dataset-en.srt 4.9 kB
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27 Upper Confidence Bound (UCB)/171 How to get the dataset-en.srt 4.9 kB
28 Thompson Sampling/182 How to get the dataset-en.srt 4.9 kB
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31 Artificial Neural Networks/222 How to get the dataset-en.srt 4.9 kB
32 Convolutional Neural Networks/247 How to get the dataset-en.srt 4.9 kB
34 Principal Component Analysis (PCA)/261 How to get the dataset-en.srt 4.9 kB
35 Linear Discriminant Analysis (LDA)/269 How to get the dataset-en.srt 4.9 kB
36 Kernel PCA/272 How to get the dataset-en.srt 4.9 kB
38 Model Selection/276 How to get the dataset-en.srt 4.9 kB
39 XGBoost/282 How to get the dataset-en.srt 4.9 kB
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01 Welcome to the course/004 This PDF resource will help you a lot.html 2.4 kB
02 -------------------- Part 1 Data Preprocessing --------------------/013 For Python learners summary of Object-oriented programming classes objects.html 2.4 kB
29 -------------------- Part 7 Natural Language Processing --------------------/211 Homework Challenge.html 2.3 kB
29 -------------------- Part 7 Natural Language Processing --------------------/200 Homework Challenge.html 2.3 kB
01 Welcome to the course/006 Update Recommended Anaconda Version.html 2.2 kB
33 -------------------- Part 9 Dimensionality Reduction --------------------/259 Welcome to Part 9 - Dimensionality Reduction.html 2.2 kB
32 Convolutional Neural Networks/250 CNN in Python - Step 3-ja.srt 2.0 kB
01 Welcome to the course/008 BONUS Meet your instructors.html 1.9 kB
37 -------------------- Part 10 Model Selection Boosting --------------------/275 Welcome to Part 10 - Model Selection Boosting.html 1.8 kB
32 Convolutional Neural Networks/250 CNN in Python - Step 3-tr.srt 1.8 kB
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30 -------------------- Part 8 Deep Learning --------------------/212 Welcome to Part 8 - Deep Learning.html 1.7 kB
03 -------------------- Part 2 Regression --------------------/020 Welcome to Part 2 - Regression.html 1.7 kB
11 -------------------- Part 3 Classification --------------------/083 Welcome to Part 3 - Classification.html 1.7 kB
32 Convolutional Neural Networks/250 CNN in Python - Step 3-en.srt 1.7 kB
26 -------------------- Part 6 Reinforcement Learning --------------------/168 Welcome to Part 6 - Reinforcement Learning.html 1.7 kB
02 -------------------- Part 1 Data Preprocessing --------------------/016 WARNING - Update.html 1.6 kB
05 Multiple Linear Regression/053 Multiple Linear Regression in R - Automatic Backward Elimination.html 1.6 kB
05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-ja.srt 1.6 kB
05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-es.srt 1.6 kB
05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-ja.srt 1.6 kB
20 -------------------- Part 4 Clustering --------------------/134 Welcome to Part 4 - Clustering.html 1.6 kB
05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-tr.srt 1.6 kB
05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-pt.srt 1.6 kB
05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-en.srt 1.6 kB
05 Multiple Linear Regression/039 Prerequisites What is the P-Value.html 1.5 kB
05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-it.srt 1.5 kB
05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-pt.srt 1.5 kB
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05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-it.srt 1.4 kB
05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-en.srt 1.4 kB
22 Hierarchical Clustering/155 Conclusion of Part 4 - Clustering.html 1.4 kB
23 -------------------- Part 5 Association Rule Learning --------------------/156 Welcome to Part 5 - Association Rule Learning.html 1.3 kB
udemycoursedownloader.com.url 132 Bytes
Udemy Course downloader.txt 94 Bytes