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[FreeCourseSite.com] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science

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种子名称： [FreeCourseSite.com] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science
文件类型：视频
文件数目： 257个文件
文件大小： 5.47 GB
收录时间： 2019-1-4 16:11
已经下载： 3次
资源热度： 267
最近下载： 2024-11-14 10:36

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

01 Welcome to the course!/001 Applications of Machine Learning.mp47.99MB

01 Welcome to the course!/002 Why Machine Learning is the Future.mp412.81MB

01 Welcome to the course!/003 Installing R and R Studio (MAC & Windows).mp417.55MB

01 Welcome to the course!/005 Installing Python and Anaconda (MAC & Windows).mp419.52MB

02 -------------------- Part 1_ Data Preprocessing --------------------/007 Welcome to Part 1 - Data Preprocessing.mp42.98MB

02 -------------------- Part 1_ Data Preprocessing --------------------/008 Get the dataset.mp421.15MB

02 -------------------- Part 1_ Data Preprocessing --------------------/009 Importing the Libraries.mp411.07MB

02 -------------------- Part 1_ Data Preprocessing --------------------/010 Importing the Dataset.mp423.31MB

02 -------------------- Part 1_ Data Preprocessing --------------------/012 Missing Data.mp432.16MB

02 -------------------- Part 1_ Data Preprocessing --------------------/013 Categorical Data.mp440.79MB

02 -------------------- Part 1_ Data Preprocessing --------------------/014 Splitting the Dataset into the Training set and Test set.mp439.03MB

02 -------------------- Part 1_ Data Preprocessing --------------------/015 Feature Scaling.mp434.62MB

02 -------------------- Part 1_ Data Preprocessing --------------------/016 And here is our Data Preprocessing Template!.mp419.67MB

04 Simple Linear Regression/018 How to get the dataset.mp411.71MB

04 Simple Linear Regression/019 Dataset + Business Problem Description.mp46.62MB

04 Simple Linear Regression/020 Simple Linear Regression Intuition - Step 1.mp49.47MB

04 Simple Linear Regression/021 Simple Linear Regression Intuition - Step 2.mp45.37MB

04 Simple Linear Regression/022 Simple Linear Regression in Python - Step 1.mp421.72MB

04 Simple Linear Regression/023 Simple Linear Regression in Python - Step 2.mp418.75MB

04 Simple Linear Regression/024 Simple Linear Regression in Python - Step 3.mp415.61MB

04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 4.mp430.82MB

04 Simple Linear Regression/026 Simple Linear Regression in R - Step 1.mp49.53MB

04 Simple Linear Regression/027 Simple Linear Regression in R - Step 2.mp414.35MB

04 Simple Linear Regression/028 Simple Linear Regression in R - Step 3.mp48.63MB

04 Simple Linear Regression/029 Simple Linear Regression in R - Step 4.mp437.37MB

05 Multiple Linear Regression/030 How to get the dataset.mp411.71MB

05 Multiple Linear Regression/031 Dataset + Business Problem Description.mp49.98MB

05 Multiple Linear Regression/032 Multiple Linear Regression Intuition - Step 1.mp41.82MB

05 Multiple Linear Regression/033 Multiple Linear Regression Intuition - Step 2.mp41.78MB

05 Multiple Linear Regression/034 Multiple Linear Regression Intuition - Step 3.mp414.28MB

05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 4.mp44.51MB

05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 5.mp428.83MB

05 Multiple Linear Regression/037 Multiple Linear Regression in Python - Step 1.mp439.56MB

05 Multiple Linear Regression/038 Multiple Linear Regression in Python - Step 2.mp47.22MB

05 Multiple Linear Regression/039 Multiple Linear Regression in Python - Step 3.mp414.29MB

05 Multiple Linear Regression/040 Multiple Linear Regression in Python - Backward Elimination - Preparation.mp432.82MB

05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp432.59MB

05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp427.17MB

05 Multiple Linear Regression/043 Multiple Linear Regression in R - Step 1.mp417.94MB

05 Multiple Linear Regression/044 Multiple Linear Regression in R - Step 2.mp425.93MB

05 Multiple Linear Regression/045 Multiple Linear Regression in R - Step 3.mp410.41MB

05 Multiple Linear Regression/046 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp439.73MB

05 Multiple Linear Regression/047 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp417.24MB

06 Polynomial Regression/048 Polynomial Regression Intuition.mp49.44MB

06 Polynomial Regression/049 How to get the dataset.mp411.71MB

06 Polynomial Regression/050 Polynomial Regression in Python - Step 1.mp424.89MB

06 Polynomial Regression/051 Polynomial Regression in Python - Step 2.mp427.1MB

06 Polynomial Regression/052 Polynomial Regression in Python - Step 3.mp442.98MB

06 Polynomial Regression/053 Polynomial Regression in Python - Step 4.mp413.5MB

06 Polynomial Regression/054 Python Regression Template.mp427.43MB

06 Polynomial Regression/055 Polynomial Regression in R - Step 1.mp417.78MB

06 Polynomial Regression/056 Polynomial Regression in R - Step 2.mp423.87MB

06 Polynomial Regression/057 Polynomial Regression in R - Step 3.mp443.31MB

06 Polynomial Regression/058 Polynomial Regression in R - Step 4.mp422.34MB

06 Polynomial Regression/059 R Regression Template.mp425.41MB

07 Support Vector Regression (SVR)/060 How to get the dataset.mp411.71MB

07 Support Vector Regression (SVR)/061 SVR in Python.mp446.18MB

07 Support Vector Regression (SVR)/062 SVR in R.mp425.87MB

08 Decision Tree Regression/063 Decision Tree Regression Intuition.mp422.69MB

08 Decision Tree Regression/064 How to get the dataset.mp411.71MB

08 Decision Tree Regression/065 Decision Tree Regression in Python.mp433.54MB

08 Decision Tree Regression/066 Decision Tree Regression in R.mp444.37MB

09 Random Forest Regression/067 Random Forest Regression Intuition.mp413.82MB

09 Random Forest Regression/068 How to get the dataset.mp411.71MB

09 Random Forest Regression/069 Random Forest Regression in Python.mp439.47MB

09 Random Forest Regression/070 Random Forest Regression in R.mp440.34MB

10 Evaluating Regression Models Performance/071 R-Squared Intuition.mp48.85MB

10 Evaluating Regression Models Performance/072 Adjusted R-Squared Intuition.mp419.28MB

10 Evaluating Regression Models Performance/073 Evaluating Regression Models Performance - Homework's Final Part.mp421.89MB

10 Evaluating Regression Models Performance/074 Interpreting Linear Regression Coefficients.mp424.21MB

12 Logistic Regression/077 Logistic Regression Intuition.mp429.17MB

12 Logistic Regression/078 How to get the dataset.mp411.71MB

12 Logistic Regression/079 Logistic Regression in Python - Step 1.mp412.93MB

12 Logistic Regression/080 Logistic Regression in Python - Step 2.mp48.23MB

12 Logistic Regression/081 Logistic Regression in Python - Step 3.mp45.97MB

12 Logistic Regression/082 Logistic Regression in Python - Step 4.mp410.37MB

12 Logistic Regression/083 Logistic Regression in Python - Step 5.mp442.55MB

12 Logistic Regression/084 Python Classification Template.mp412.06MB

12 Logistic Regression/085 Logistic Regression in R - Step 1.mp412.58MB

12 Logistic Regression/086 Logistic Regression in R - Step 2.mp47.84MB

12 Logistic Regression/087 Logistic Regression in R - Step 3.mp414.59MB

12 Logistic Regression/088 Logistic Regression in R - Step 4.mp46.9MB

12 Logistic Regression/089 Logistic Regression in R - Step 5.mp451.68MB

12 Logistic Regression/090 R Classification Template.mp412.47MB

13 K-Nearest Neighbors (K-NN)/091 K-Nearest Neighbor Intuition.mp49.27MB

13 K-Nearest Neighbors (K-NN)/092 How to get the dataset.mp411.71MB

13 K-Nearest Neighbors (K-NN)/093 K-NN in Python.mp435.21MB

13 K-Nearest Neighbors (K-NN)/094 K-NN in R.mp441.37MB

14 Support Vector Machine (SVM)/095 SVM Intuition.mp418.01MB

14 Support Vector Machine (SVM)/096 How to get the dataset.mp411.71MB

14 Support Vector Machine (SVM)/097 SVM in Python.mp431.16MB

14 Support Vector Machine (SVM)/098 SVM in R.mp432.26MB

15 Kernel SVM/099 Kernel SVM Intuition.mp45.79MB

15 Kernel SVM/100 Mapping to a higher dimension.mp413.74MB

15 Kernel SVM/101 The Kernel Trick.mp429.28MB

15 Kernel SVM/102 Types of Kernel Functions.mp412.3MB

15 Kernel SVM/103 How to get the dataset.mp411.71MB

15 Kernel SVM/104 Kernel SVM in Python.mp441.62MB

15 Kernel SVM/105 Kernel SVM in R.mp440.45MB

16 Naive Bayes/106 Bayes Theorem.mp443.9MB

16 Naive Bayes/107 Naive Bayes Intuition.mp427.79MB

16 Naive Bayes/108 Naive Bayes Intuition (Challenge Reveal).mp413.27MB

16 Naive Bayes/109 Naive Bayes Intuition (Extras).mp418.94MB

16 Naive Bayes/110 How to get the dataset.mp411.71MB

16 Naive Bayes/111 Naive Bayes in Python.mp423.38MB

16 Naive Bayes/112 Naive Bayes in R.mp437.31MB

17 Decision Tree Classification/113 Decision Tree Classification Intuition.mp418.79MB

17 Decision Tree Classification/114 How to get the dataset.mp411.71MB

17 Decision Tree Classification/115 Decision Tree Classification in Python.mp429.8MB

17 Decision Tree Classification/116 Decision Tree Classification in R.mp451.18MB

18 Random Forest Classification/117 Random Forest Classification Intuition.mp419.43MB

18 Random Forest Classification/118 How to get the dataset.mp411.71MB

18 Random Forest Classification/119 Random Forest Classification in Python.mp447.15MB

18 Random Forest Classification/120 Random Forest Classification in R.mp449.39MB

19 Evaluating Classification Models Performance/121 False Positives & False Negatives.mp413.65MB

19 Evaluating Classification Models Performance/122 Confusion Matrix.mp48.21MB

19 Evaluating Classification Models Performance/123 Accuracy Paradox.mp43.8MB

19 Evaluating Classification Models Performance/124 CAP Curve.mp418.68MB

19 Evaluating Classification Models Performance/125 CAP Curve Analysis.mp411.51MB

21 K-Means Clustering/128 K-Means Clustering Intuition.mp426.86MB

21 K-Means Clustering/129 K-Means Random Initialization Trap.mp415.36MB

21 K-Means Clustering/130 K-Means Selecting The Number Of Clusters.mp423.13MB

21 K-Means Clustering/131 How to get the dataset.mp411.71MB

21 K-Means Clustering/132 K-Means Clustering in Python.mp439.77MB

21 K-Means Clustering/133 K-Means Clustering in R.mp428.99MB

22 Hierarchical Clustering/134 Hierarchical Clustering Intuition.mp416.52MB

22 Hierarchical Clustering/135 Hierarchical Clustering How Dendrograms Work.mp417.46MB

22 Hierarchical Clustering/136 Hierarchical Clustering Using Dendrograms.mp422.81MB

22 Hierarchical Clustering/137 How to get the dataset.mp411.71MB

22 Hierarchical Clustering/138 HC in Python - Step 1.mp410.72MB

22 Hierarchical Clustering/139 HC in Python - Step 2.mp412.64MB

22 Hierarchical Clustering/140 HC in Python - Step 3.mp412.3MB

22 Hierarchical Clustering/141 HC in Python - Step 4.mp412.02MB

22 Hierarchical Clustering/142 HC in Python - Step 5.mp48.39MB

22 Hierarchical Clustering/143 HC in R - Step 1.mp47.38MB

22 Hierarchical Clustering/144 HC in R - Step 2.mp411.15MB

22 Hierarchical Clustering/145 HC in R - Step 3.mp47.8MB

22 Hierarchical Clustering/146 HC in R - Step 4.mp47.44MB

22 Hierarchical Clustering/147 HC in R - Step 5.mp46.88MB

24 Apriori/150 Apriori Intuition.mp435.02MB

24 Apriori/151 How to get the dataset.mp411.71MB

24 Apriori/152 Apriori in R - Step 1.mp442.87MB

24 Apriori/153 Apriori in R - Step 2.mp430.5MB

24 Apriori/154 Apriori in R - Step 3.mp443.84MB

24 Apriori/155 Apriori in Python - Step 1.mp437.97MB

24 Apriori/156 Apriori in Python - Step 2.mp429.52MB

24 Apriori/157 Apriori in Python - Step 3.mp426.96MB

25 Eclat/158 Eclat Intuition.mp410.65MB

25 Eclat/159 How to get the dataset.mp411.71MB

25 Eclat/160 Eclat in R.mp420.68MB

27 Upper Confidence Bound (UCB)/162 The Multi-Armed Bandit Problem.mp430.19MB

27 Upper Confidence Bound (UCB)/163 Upper Confidence Bound (UCB) Intuition.mp429.32MB

27 Upper Confidence Bound (UCB)/164 How to get the dataset.mp411.71MB

27 Upper Confidence Bound (UCB)/165 Upper Confidence Bound in Python - Step 1.mp431.53MB

27 Upper Confidence Bound (UCB)/166 Upper Confidence Bound in Python - Step 2.mp435.44MB

27 Upper Confidence Bound (UCB)/167 Upper Confidence Bound in Python - Step 3.mp441.11MB

27 Upper Confidence Bound (UCB)/168 Upper Confidence Bound in Python - Step 4.mp49.13MB

27 Upper Confidence Bound (UCB)/169 Upper Confidence Bound in R - Step 1.mp428.05MB

27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound in R - Step 2.mp429.01MB

27 Upper Confidence Bound (UCB)/171 Upper Confidence Bound in R - Step 3.mp447.2MB

27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in R - Step 4.mp47.4MB

28 Thompson Sampling/173 Thompson Sampling Intuition.mp437.27MB

28 Thompson Sampling/174 Algorithm Comparison_ UCB vs Thompson Sampling.mp414.08MB

28 Thompson Sampling/175 How to get the dataset.mp411.71MB

28 Thompson Sampling/176 Thompson Sampling in Python - Step 1.mp443.13MB

28 Thompson Sampling/177 Thompson Sampling in Python - Step 2.mp48.41MB

28 Thompson Sampling/178 Thompson Sampling in R - Step 1.mp440.93MB

28 Thompson Sampling/179 Thompson Sampling in R - Step 2.mp47.46MB

29 -------------------- Part 7_ Natural Language Processing --------------------/181 How to get the dataset.mp411.71MB

29 -------------------- Part 7_ Natural Language Processing --------------------/182 Natural Language Processing in Python - Step 1.mp435.2MB

29 -------------------- Part 7_ Natural Language Processing --------------------/183 Natural Language Processing in Python - Step 2.mp421.96MB

29 -------------------- Part 7_ Natural Language Processing --------------------/184 Natural Language Processing in Python - Step 3.mp43.38MB

29 -------------------- Part 7_ Natural Language Processing --------------------/185 Natural Language Processing in Python - Step 4.mp424MB

29 -------------------- Part 7_ Natural Language Processing --------------------/186 Natural Language Processing in Python - Step 5.mp414.9MB

29 -------------------- Part 7_ Natural Language Processing --------------------/187 Natural Language Processing in Python - Step 6.mp46.49MB

29 -------------------- Part 7_ Natural Language Processing --------------------/188 Natural Language Processing in Python - Step 7.mp417.1MB

29 -------------------- Part 7_ Natural Language Processing --------------------/189 Natural Language Processing in Python - Step 8.mp439.48MB

29 -------------------- Part 7_ Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 9.mp414.01MB

29 -------------------- Part 7_ Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 10.mp424.13MB

29 -------------------- Part 7_ Natural Language Processing --------------------/193 Natural Language Processing in R - Step 1.mp440.37MB

29 -------------------- Part 7_ Natural Language Processing --------------------/194 Natural Language Processing in R - Step 2.mp417.47MB

29 -------------------- Part 7_ Natural Language Processing --------------------/195 Natural Language Processing in R - Step 3.mp413.52MB

29 -------------------- Part 7_ Natural Language Processing --------------------/196 Natural Language Processing in R - Step 4.mp46.5MB

29 -------------------- Part 7_ Natural Language Processing --------------------/197 Natural Language Processing in R - Step 5.mp44.57MB

29 -------------------- Part 7_ Natural Language Processing --------------------/198 Natural Language Processing in R - Step 6.mp412.73MB

29 -------------------- Part 7_ Natural Language Processing --------------------/199 Natural Language Processing in R - Step 7.mp47.51MB

29 -------------------- Part 7_ Natural Language Processing --------------------/200 Natural Language Processing in R - Step 8.mp413.26MB

29 -------------------- Part 7_ Natural Language Processing --------------------/201 Natural Language Processing in R - Step 9.mp428.99MB

29 -------------------- Part 7_ Natural Language Processing --------------------/202 Natural Language Processing in R - Step 10.mp441.19MB

30 -------------------- Part 8_ Deep Learning --------------------/205 What is Deep Learning_.mp431.31MB

31 Artificial Neural Networks/206 Plan of attack.mp44.74MB

31 Artificial Neural Networks/207 The Neuron.mp429.86MB

31 Artificial Neural Networks/208 The Activation Function.mp414.75MB

31 Artificial Neural Networks/209 How do Neural Networks work_.mp423.53MB

31 Artificial Neural Networks/210 How do Neural Networks learn_.mp426.55MB

31 Artificial Neural Networks/211 Gradient Descent.mp418.53MB

31 Artificial Neural Networks/212 Stochastic Gradient Descent.mp416.82MB

31 Artificial Neural Networks/213 Backpropagation.mp410.92MB

31 Artificial Neural Networks/214 How to get the dataset.mp411.71MB

31 Artificial Neural Networks/215 Business Problem Description.mp416.37MB

31 Artificial Neural Networks/216 ANN in Python - Step 1 - Installing Theano_ Tensorflow and Keras.mp429.31MB

31 Artificial Neural Networks/217 ANN in Python - Step 2.mp448.09MB

31 Artificial Neural Networks/218 ANN in Python - Step 3.mp48.37MB

31 Artificial Neural Networks/219 ANN in Python - Step 4.mp45.88MB

31 Artificial Neural Networks/220 ANN in Python - Step 5.mp429.58MB

31 Artificial Neural Networks/221 ANN in Python - Step 6.mp47.05MB

31 Artificial Neural Networks/222 ANN in Python - Step 7.mp48.99MB

31 Artificial Neural Networks/223 ANN in Python - Step 8.mp418.16MB

31 Artificial Neural Networks/224 ANN in Python - Step 9.mp416.89MB

31 Artificial Neural Networks/225 ANN in Python - Step 10.mp417.09MB

31 Artificial Neural Networks/226 ANN in R - Step 1.mp438.55MB

31 Artificial Neural Networks/227 ANN in R - Step 2.mp414.17MB

31 Artificial Neural Networks/228 ANN in R - Step 3.mp428.94MB

31 Artificial Neural Networks/229 ANN in R - Step 4 (Last step).mp433.44MB

32 Convolutional Neural Networks/230 Plan of attack.mp45.9MB

32 Convolutional Neural Networks/231 What are convolutional neural networks_.mp429.5MB

32 Convolutional Neural Networks/232 Step 1 - Convolution Operation.mp431.02MB

32 Convolutional Neural Networks/233 Step 1(b) - ReLU Layer.mp414.09MB

32 Convolutional Neural Networks/234 Step 2 - Pooling.mp440.24MB

32 Convolutional Neural Networks/235 Step 3 - Flattening.mp43.27MB

32 Convolutional Neural Networks/236 Step 4 - Full Connection.mp442.74MB

32 Convolutional Neural Networks/237 Summary.mp47.91MB

32 Convolutional Neural Networks/238 Softmax & Cross-Entropy.mp433.23MB

32 Convolutional Neural Networks/239 How to get the dataset.mp411.71MB

32 Convolutional Neural Networks/240 CNN in Python - Step 1.mp424.92MB

32 Convolutional Neural Networks/241 CNN in Python - Step 2.mp45.85MB

32 Convolutional Neural Networks/242 CNN in Python - Step 3.mp42.22MB

32 Convolutional Neural Networks/243 CNN in Python - Step 4.mp427.18MB

32 Convolutional Neural Networks/244 CNN in Python - Step 5.mp49.91MB

32 Convolutional Neural Networks/245 CNN in Python - Step 6.mp49.7MB

32 Convolutional Neural Networks/246 CNN in Python - Step 7.mp412.93MB

32 Convolutional Neural Networks/247 CNN in Python - Step 8.mp46.79MB

32 Convolutional Neural Networks/248 CNN in Python - Step 9.mp446.85MB

32 Convolutional Neural Networks/249 CNN in Python - Step 10.mp420.6MB

34 Principal Component Analysis (PCA)/252 How to get the dataset.mp411.71MB

34 Principal Component Analysis (PCA)/253 PCA in Python - Step 1.mp431.95MB

34 Principal Component Analysis (PCA)/254 PCA in Python - Step 2.mp422.07MB

34 Principal Component Analysis (PCA)/255 PCA in Python - Step 3.mp425.51MB

34 Principal Component Analysis (PCA)/256 PCA in R - Step 1.mp430.65MB

34 Principal Component Analysis (PCA)/257 PCA in R - Step 2.mp429.02MB

34 Principal Component Analysis (PCA)/258 PCA in R - Step 3.mp436.73MB

35 Linear Discriminant Analysis (LDA)/259 How to get the dataset.mp411.71MB

35 Linear Discriminant Analysis (LDA)/260 LDA in Python.mp445.42MB

35 Linear Discriminant Analysis (LDA)/261 LDA in R.mp451.29MB

36 Kernel PCA/262 How to get the dataset.mp411.71MB

36 Kernel PCA/263 Kernel PCA in Python.mp433.38MB

36 Kernel PCA/264 Kernel PCA in R.mp456.57MB

38 Model Selection/266 How to get the dataset.mp411.71MB

38 Model Selection/267 k-Fold Cross Validation in Python.mp432.83MB

38 Model Selection/268 k-Fold Cross Validation in R.mp443.63MB

38 Model Selection/269 Grid Search in Python - Step 1.mp438.21MB

38 Model Selection/270 Grid Search in Python - Step 2.mp429.51MB

38 Model Selection/271 Grid Search in R.mp435.54MB

39 XGBoost/272 How to get the dataset.mp411.71MB

39 XGBoost/273 XGBoost in Python - Step 1.mp421.39MB

39 XGBoost/274 XGBoost in Python - Step 2.mp431.97MB

39 XGBoost/275 XGBoost in R.mp447.26MB