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种子名称： [UdemyCourseDownloader] Machine Learning A-Z™ Hands-On Python & R In Data Science
文件类型： 视频
文件数目： 261个文件
文件大小： 6.82 GB
收录时间： 2019-6-9 09:24
已经下载： 3次
资源热度： 170
最近下载： 2024-6-7 19:49

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

12 Logistic Regression/096 Logistic Regression in R - Step 5.mp493.76MB

01 Welcome to the course/001 Applications of Machine Learning.mp49.81MB

01 Welcome to the course/002 Why Machine Learning is the Future.mp414.48MB

01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows).mp423.96MB

01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows).mp423.21MB

02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing.mp43.52MB

02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset.mp421.15MB

02 -------------------- Part 1 Data Preprocessing --------------------/011 Importing the Libraries.mp413.56MB

02 -------------------- Part 1 Data Preprocessing --------------------/012 Importing the Dataset.mp428.64MB

02 -------------------- Part 1 Data Preprocessing --------------------/014 Missing Data.mp432.16MB

02 -------------------- Part 1 Data Preprocessing --------------------/015 Categorical Data.mp452.88MB

02 -------------------- Part 1 Data Preprocessing --------------------/017 Splitting the Dataset into the Training set and Test set.mp450.91MB

02 -------------------- Part 1 Data Preprocessing --------------------/018 Feature Scaling.mp444.59MB

02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template.mp425.86MB

04 Simple Linear Regression/021 How to get the dataset.mp411.71MB

04 Simple Linear Regression/022 Dataset Business Problem Description.mp47.77MB

04 Simple Linear Regression/023 Simple Linear Regression Intuition - Step 1.mp410.52MB

04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2.mp45.99MB

04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1.mp427.92MB

04 Simple Linear Regression/026 Simple Linear Regression in Python - Step 2.mp424.62MB

04 Simple Linear Regression/027 Simple Linear Regression in Python - Step 3.mp420.55MB

04 Simple Linear Regression/028 Simple Linear Regression in Python - Step 4.mp439.37MB

04 Simple Linear Regression/029 Simple Linear Regression in R - Step 1.mp411.52MB

04 Simple Linear Regression/030 Simple Linear Regression in R - Step 2.mp424.87MB

04 Simple Linear Regression/031 Simple Linear Regression in R - Step 3.mp411.42MB

04 Simple Linear Regression/032 Simple Linear Regression in R - Step 4.mp449.16MB

05 Multiple Linear Regression/033 How to get the dataset.mp411.71MB

05 Multiple Linear Regression/034 Dataset Business Problem Description.mp412.56MB

05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1.mp42MB

05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2.mp42.03MB

05 Multiple Linear Regression/037 Multiple Linear Regression Intuition - Step 3.mp416.59MB

05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4.mp45.34MB

05 Multiple Linear Regression/040 Multiple Linear Regression Intuition - Step 5.mp432.8MB

05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Step 1.mp452.18MB

05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2.mp49.84MB

05 Multiple Linear Regression/043 Multiple Linear Regression in Python - Step 3.mp425.48MB

05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation.mp423.82MB

05 Multiple Linear Regression/045 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK.mp459.14MB

05 Multiple Linear Regression/046 Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp454.26MB

05 Multiple Linear Regression/048 Multiple Linear Regression in R - Step 1.mp423.44MB

05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2.mp445.22MB

05 Multiple Linear Regression/050 Multiple Linear Regression in R - Step 3.mp413.85MB

05 Multiple Linear Regression/051 Multiple Linear Regression in R - Backward Elimination - HOMEWORK.mp450.78MB

05 Multiple Linear Regression/052 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp421.95MB

06 Polynomial Regression/054 Polynomial Regression Intuition.mp49.44MB

06 Polynomial Regression/055 How to get the dataset.mp411.71MB

06 Polynomial Regression/056 Polynomial Regression in Python - Step 1.mp431.64MB

06 Polynomial Regression/057 Polynomial Regression in Python - Step 2.mp435.11MB

06 Polynomial Regression/058 Polynomial Regression in Python - Step 3.mp454.5MB

06 Polynomial Regression/059 Polynomial Regression in Python - Step 4.mp417.65MB

06 Polynomial Regression/060 Python Regression Template.mp436.78MB

06 Polynomial Regression/061 Polynomial Regression in R - Step 1.mp421.21MB

06 Polynomial Regression/062 Polynomial Regression in R - Step 2.mp432.28MB

06 Polynomial Regression/063 Polynomial Regression in R - Step 3.mp454.8MB

06 Polynomial Regression/064 Polynomial Regression in R - Step 4.mp428.52MB

06 Polynomial Regression/065 R Regression Template.mp431.33MB

07 Support Vector Regression (SVR)/066 How to get the dataset.mp411.71MB

07 Support Vector Regression (SVR)/067 SVR Intuition.mp446.59MB

07 Support Vector Regression (SVR)/068 SVR in Python.mp460.22MB

07 Support Vector Regression (SVR)/069 SVR in R.mp433.73MB

08 Decision Tree Regression/070 Decision Tree Regression Intuition.mp425.33MB

08 Decision Tree Regression/071 How to get the dataset.mp411.71MB

08 Decision Tree Regression/072 Decision Tree Regression in Python.mp443.44MB

08 Decision Tree Regression/073 Decision Tree Regression in R.mp456.23MB

09 Random Forest Regression/074 Random Forest Regression Intuition.mp415.65MB

09 Random Forest Regression/075 How to get the dataset.mp411.71MB

09 Random Forest Regression/076 Random Forest Regression in Python.mp452.69MB

09 Random Forest Regression/077 Random Forest Regression in R.mp451.86MB

10 Evaluating Regression Models Performance/078 R-Squared Intuition.mp49.8MB

10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition.mp421.41MB

10 Evaluating Regression Models Performance/080 Evaluating Regression Models Performance - Homeworks Final Part.mp428.35MB

10 Evaluating Regression Models Performance/081 Interpreting Linear Regression Coefficients.mp427.38MB

12 Logistic Regression/084 Logistic Regression Intuition.mp429.17MB

12 Logistic Regression/085 How to get the dataset.mp411.71MB

12 Logistic Regression/086 Logistic Regression in Python - Step 1.mp416.84MB

12 Logistic Regression/087 Logistic Regression in Python - Step 2.mp411.1MB

12 Logistic Regression/088 Logistic Regression in Python - Step 3.mp47.98MB

12 Logistic Regression/089 Logistic Regression in Python - Step 4.mp413.87MB

12 Logistic Regression/090 Logistic Regression in Python - Step 5.mp453.15MB

12 Logistic Regression/091 Python Classification Template.mp417.58MB

12 Logistic Regression/092 Logistic Regression in R - Step 1.mp415.72MB

12 Logistic Regression/093 Logistic Regression in R - Step 2.mp414.85MB

12 Logistic Regression/094 Logistic Regression in R - Step 3.mp427.44MB

12 Logistic Regression/095 Logistic Regression in R - Step 4.mp411.73MB

12 Logistic Regression/097 R Classification Template.mp417.5MB

13 K-Nearest Neighbors (K-NN)/098 K-Nearest Neighbor Intuition.mp410.48MB

13 K-Nearest Neighbors (K-NN)/099 How to get the dataset.mp411.71MB

13 K-Nearest Neighbors (K-NN)/100 K-NN in Python.mp446.98MB

13 K-Nearest Neighbors (K-NN)/101 K-NN in R.mp455.77MB

14 Support Vector Machine (SVM)/102 SVM Intuition.mp419.92MB

14 Support Vector Machine (SVM)/103 How to get the dataset.mp411.71MB

14 Support Vector Machine (SVM)/104 SVM in Python.mp441.71MB

14 Support Vector Machine (SVM)/105 SVM in R.mp465.31MB

15 Kernel SVM/106 Kernel SVM Intuition.mp46.42MB

15 Kernel SVM/107 Mapping to a higher dimension.mp415.39MB

15 Kernel SVM/108 The Kernel Trick.mp434.72MB

15 Kernel SVM/109 Types of Kernel Functions.mp415.71MB

15 Kernel SVM/110 How to get the dataset.mp411.71MB

15 Kernel SVM/111 Kernel SVM in Python.mp454.86MB

15 Kernel SVM/112 Kernel SVM in R.mp452.82MB

16 Naive Bayes/113 Bayes Theorem.mp450.43MB

16 Naive Bayes/114 Naive Bayes Intuition.mp431.1MB

16 Naive Bayes/115 Naive Bayes Intuition (Challenge Reveal).mp413.27MB

16 Naive Bayes/116 Naive Bayes Intuition (Extras).mp418.94MB

16 Naive Bayes/117 How to get the dataset.mp411.71MB

16 Naive Bayes/118 Naive Bayes in Python.mp431.14MB

16 Naive Bayes/119 Naive Bayes in R.mp449.79MB

17 Decision Tree Classification/120 Decision Tree Classification Intuition.mp421.63MB

17 Decision Tree Classification/121 How to get the dataset.mp411.71MB

17 Decision Tree Classification/122 Decision Tree Classification in Python.mp438.85MB

17 Decision Tree Classification/123 Decision Tree Classification in R.mp468.18MB

18 Random Forest Classification/124 Random Forest Classification Intuition.mp425.66MB

18 Random Forest Classification/125 How to get the dataset.mp411.71MB

18 Random Forest Classification/126 Random Forest Classification in Python.mp462.04MB

18 Random Forest Classification/127 Random Forest Classification in R.mp464.11MB

19 Evaluating Classification Models Performance/128 False Positives False Negatives.mp415.12MB

19 Evaluating Classification Models Performance/129 Confusion Matrix.mp48.91MB

19 Evaluating Classification Models Performance/130 Accuracy Paradox.mp44.21MB

19 Evaluating Classification Models Performance/131 CAP Curve.mp420.31MB

19 Evaluating Classification Models Performance/132 CAP Curve Analysis.mp412.94MB

21 K-Means Clustering/135 K-Means Clustering Intuition.mp429.97MB

21 K-Means Clustering/136 K-Means Random Initialization Trap.mp415.36MB

21 K-Means Clustering/137 K-Means Selecting The Number Of Clusters.mp425.68MB

21 K-Means Clustering/138 How to get the dataset.mp411.71MB

21 K-Means Clustering/139 K-Means Clustering in Python.mp449.81MB

21 K-Means Clustering/140 K-Means Clustering in R.mp436.91MB

22 Hierarchical Clustering/141 Hierarchical Clustering Intuition.mp416.52MB

22 Hierarchical Clustering/142 Hierarchical Clustering How Dendrograms Work.mp417.46MB

22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms.mp422.81MB

22 Hierarchical Clustering/144 How to get the dataset.mp411.71MB

22 Hierarchical Clustering/145 HC in Python - Step 1.mp413.77MB

22 Hierarchical Clustering/146 HC in Python - Step 2.mp415.51MB

22 Hierarchical Clustering/147 HC in Python - Step 3.mp416.17MB

22 Hierarchical Clustering/148 HC in Python - Step 4.mp421.32MB

22 Hierarchical Clustering/149 HC in Python - Step 5.mp49.92MB

22 Hierarchical Clustering/150 HC in R - Step 1.mp48.59MB

22 Hierarchical Clustering/151 HC in R - Step 2.mp413.87MB

22 Hierarchical Clustering/152 HC in R - Step 3.mp49.95MB

22 Hierarchical Clustering/153 HC in R - Step 4.mp410.17MB

22 Hierarchical Clustering/154 HC in R - Step 5.mp413.68MB

24 Apriori/157 Apriori Intuition.mp435.02MB

24 Apriori/158 How to get the dataset.mp411.71MB

24 Apriori/159 Apriori in R - Step 1.mp452.83MB

24 Apriori/160 Apriori in R - Step 2.mp438.81MB

24 Apriori/161 Apriori in R - Step 3.mp456.51MB

24 Apriori/162 Apriori in Python - Step 1.mp447.41MB

24 Apriori/163 Apriori in Python - Step 2.mp437.32MB

24 Apriori/164 Apriori in Python - Step 3.mp435.3MB

25 Eclat/165 Eclat Intuition.mp410.65MB

25 Eclat/166 How to get the dataset.mp411.71MB

25 Eclat/167 Eclat in R.mp425.26MB

27 Upper Confidence Bound (UCB)/169 The Multi-Armed Bandit Problem.mp430.19MB

27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound (UCB) Intuition.mp429.32MB

27 Upper Confidence Bound (UCB)/171 How to get the dataset.mp411.71MB

27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in Python - Step 1.mp439.01MB

27 Upper Confidence Bound (UCB)/173 Upper Confidence Bound in Python - Step 2.mp444.49MB

27 Upper Confidence Bound (UCB)/174 Upper Confidence Bound in Python - Step 3.mp453.71MB

27 Upper Confidence Bound (UCB)/175 Upper Confidence Bound in Python - Step 4.mp412.44MB

27 Upper Confidence Bound (UCB)/176 Upper Confidence Bound in R - Step 1.mp434.01MB

27 Upper Confidence Bound (UCB)/177 Upper Confidence Bound in R - Step 2.mp434.1MB

27 Upper Confidence Bound (UCB)/178 Upper Confidence Bound in R - Step 3.mp457.84MB

27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4.mp49.55MB

28 Thompson Sampling/180 Thompson Sampling Intuition.mp437.27MB

28 Thompson Sampling/181 Algorithm Comparison UCB vs Thompson Sampling.mp414.08MB

28 Thompson Sampling/182 How to get the dataset.mp411.71MB

28 Thompson Sampling/183 Thompson Sampling in Python - Step 1.mp455.52MB

28 Thompson Sampling/184 Thompson Sampling in Python - Step 2.mp411.22MB

28 Thompson Sampling/185 Thompson Sampling in R - Step 1.mp451.04MB

28 Thompson Sampling/186 Thompson Sampling in R - Step 2.mp49.56MB

29 -------------------- Part 7 Natural Language Processing --------------------/188 Natural Language Processing Intuition.mp429.69MB

29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset.mp411.71MB

29 -------------------- Part 7 Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 1.mp446.06MB

29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2.mp427.44MB

29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3.mp44.16MB

29 -------------------- Part 7 Natural Language Processing --------------------/193 Natural Language Processing in Python - Step 4.mp429.75MB

29 -------------------- Part 7 Natural Language Processing --------------------/194 Natural Language Processing in Python - Step 5.mp418.8MB

29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6.mp48.32MB

29 -------------------- Part 7 Natural Language Processing --------------------/196 Natural Language Processing in Python - Step 7.mp422.13MB

29 -------------------- Part 7 Natural Language Processing --------------------/197 Natural Language Processing in Python - Step 8.mp452.02MB

29 -------------------- Part 7 Natural Language Processing --------------------/198 Natural Language Processing in Python - Step 9.mp418.9MB

29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - Step 10.mp432.91MB

29 -------------------- Part 7 Natural Language Processing --------------------/201 Natural Language Processing in R - Step 1.mp451.2MB

29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - Step 2.mp421.66MB

29 -------------------- Part 7 Natural Language Processing --------------------/203 Natural Language Processing in R - Step 3.mp416.89MB

29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4.mp48.24MB

29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5.mp45.78MB

29 -------------------- Part 7 Natural Language Processing --------------------/206 Natural Language Processing in R - Step 6.mp416.09MB

29 -------------------- Part 7 Natural Language Processing --------------------/207 Natural Language Processing in R - Step 7.mp49.59MB

29 -------------------- Part 7 Natural Language Processing --------------------/208 Natural Language Processing in R - Step 8.mp417.23MB

29 -------------------- Part 7 Natural Language Processing --------------------/209 Natural Language Processing in R - Step 9.mp437.69MB

29 -------------------- Part 7 Natural Language Processing --------------------/210 Natural Language Processing in R - Step 10.mp454.14MB

30 -------------------- Part 8 Deep Learning --------------------/213 What is Deep Learning.mp431.31MB

31 Artificial Neural Networks/214 Plan of attack.mp44.74MB

31 Artificial Neural Networks/215 The Neuron.mp429.86MB

31 Artificial Neural Networks/216 The Activation Function.mp414.75MB

31 Artificial Neural Networks/217 How do Neural Networks work.mp423.53MB

31 Artificial Neural Networks/218 How do Neural Networks learn.mp426.55MB

31 Artificial Neural Networks/219 Gradient Descent.mp418.53MB

31 Artificial Neural Networks/220 Stochastic Gradient Descent.mp416.82MB

31 Artificial Neural Networks/221 Backpropagation.mp410.92MB

31 Artificial Neural Networks/222 How to get the dataset.mp411.71MB

31 Artificial Neural Networks/223 Business Problem Description.mp429.23MB

31 Artificial Neural Networks/224 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras.mp437.45MB

31 Artificial Neural Networks/225 ANN in Python - Step 2.mp484.87MB

31 Artificial Neural Networks/226 ANN in Python - Step 3.mp414.62MB

31 Artificial Neural Networks/227 ANN in Python - Step 4.mp49.69MB

31 Artificial Neural Networks/228 ANN in Python - Step 5.mp439.36MB

31 Artificial Neural Networks/229 ANN in Python - Step 6.mp411.93MB

31 Artificial Neural Networks/230 ANN in Python - Step 7.mp414.92MB

31 Artificial Neural Networks/231 ANN in Python - Step 8.mp434.03MB

31 Artificial Neural Networks/232 ANN in Python - Step 9.mp428.47MB

31 Artificial Neural Networks/233 ANN in Python - Step 10.mp428.42MB

31 Artificial Neural Networks/234 ANN in R - Step 1.mp449.89MB

31 Artificial Neural Networks/235 ANN in R - Step 2.mp418.24MB

31 Artificial Neural Networks/236 ANN in R - Step 3.mp437.85MB

31 Artificial Neural Networks/237 ANN in R - Step 4 (Last step).mp443.75MB

32 Convolutional Neural Networks/238 Plan of attack.mp45.9MB

32 Convolutional Neural Networks/239 What are convolutional neural networks.mp429.5MB

32 Convolutional Neural Networks/240 Step 1 - Convolution Operation.mp431.02MB

32 Convolutional Neural Networks/241 Step 1(b) - ReLU Layer.mp414.09MB

32 Convolutional Neural Networks/242 Step 2 - Pooling.mp440.24MB

32 Convolutional Neural Networks/243 Step 3 - Flattening.mp43.27MB

32 Convolutional Neural Networks/244 Step 4 - Full Connection.mp442.74MB

32 Convolutional Neural Networks/245 Summary.mp47.91MB

32 Convolutional Neural Networks/246 Softmax Cross-Entropy.mp433.23MB

32 Convolutional Neural Networks/247 How to get the dataset.mp411.71MB

32 Convolutional Neural Networks/248 CNN in Python - Step 1.mp430.6MB

32 Convolutional Neural Networks/249 CNN in Python - Step 2.mp47.2MB

32 Convolutional Neural Networks/250 CNN in Python - Step 3.mp42.8MB

32 Convolutional Neural Networks/251 CNN in Python - Step 4.mp434.62MB

32 Convolutional Neural Networks/252 CNN in Python - Step 5.mp412.38MB

32 Convolutional Neural Networks/253 CNN in Python - Step 6.mp411.94MB

32 Convolutional Neural Networks/254 CNN in Python - Step 7.mp416.65MB

32 Convolutional Neural Networks/255 CNN in Python - Step 8.mp48.95MB

32 Convolutional Neural Networks/256 CNN in Python - Step 9.mp462.41MB

32 Convolutional Neural Networks/257 CNN in Python - Step 10.mp427.74MB

34 Principal Component Analysis (PCA)/260 Principal Component Analysis (PCA) Intuition.mp432.11MB

34 Principal Component Analysis (PCA)/261 How to get the dataset.mp411.71MB

34 Principal Component Analysis (PCA)/262 PCA in Python - Step 1.mp431.95MB

34 Principal Component Analysis (PCA)/263 PCA in Python - Step 2.mp422.07MB

34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3.mp425.51MB

34 Principal Component Analysis (PCA)/265 PCA in R - Step 1.mp430.65MB

34 Principal Component Analysis (PCA)/266 PCA in R - Step 2.mp429.02MB

34 Principal Component Analysis (PCA)/267 PCA in R - Step 3.mp436.73MB

35 Linear Discriminant Analysis (LDA)/268 Linear Discriminant Analysis (LDA) Intuition.mp426.98MB

35 Linear Discriminant Analysis (LDA)/269 How to get the dataset.mp411.71MB

35 Linear Discriminant Analysis (LDA)/270 LDA in Python.mp445.42MB

35 Linear Discriminant Analysis (LDA)/271 LDA in R.mp451.29MB

36 Kernel PCA/272 How to get the dataset.mp411.71MB

36 Kernel PCA/273 Kernel PCA in Python.mp433.38MB

36 Kernel PCA/274 Kernel PCA in R.mp456.57MB

38 Model Selection/276 How to get the dataset.mp411.71MB

38 Model Selection/277 k-Fold Cross Validation in Python.mp432.83MB

38 Model Selection/278 k-Fold Cross Validation in R.mp443.63MB

38 Model Selection/279 Grid Search in Python - Step 1.mp438.21MB

38 Model Selection/280 Grid Search in Python - Step 2.mp429.51MB

38 Model Selection/281 Grid Search in R.mp435.54MB

39 XGBoost/282 How to get the dataset.mp411.71MB

39 XGBoost/283 XGBoost in Python - Step 1.mp421.39MB

39 XGBoost/284 XGBoost in Python - Step 2.mp431.97MB

39 XGBoost/285 XGBoost in R.mp447.26MB