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

[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
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文件数目： 257个文件
文件大小： 6.72 GB
收录时间： 2018-6-5 01:13
已经下载： 3次
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最近下载： 2024-11-29 09:19

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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/003 Installing R and R Studio MAC Windows.mp423.21MB

01 Welcome to the course/004 Installing Python and Anaconda MAC Windows.mp423.96MB

02 -------------------------- Part 1 Data Preprocessing --------------------------/006 Welcome to Part 1 - Data Preprocessing.mp43.52MB

02 -------------------------- Part 1 Data Preprocessing --------------------------/007 Get the dataset.mp421.15MB

02 -------------------------- Part 1 Data Preprocessing --------------------------/008 Importing the Libraries.mp413.56MB

02 -------------------------- Part 1 Data Preprocessing --------------------------/009 Importing the Dataset.mp428.64MB

02 -------------------------- Part 1 Data Preprocessing --------------------------/011 Missing Data.mp439.32MB

02 -------------------------- Part 1 Data Preprocessing --------------------------/012 Categorical Data.mp452.88MB

02 -------------------------- Part 1 Data Preprocessing --------------------------/013 Splitting the Dataset into the Training set and Test set.mp450.91MB

02 -------------------------- Part 1 Data Preprocessing --------------------------/014 Feature Scaling.mp444.59MB

02 -------------------------- Part 1 Data Preprocessing --------------------------/015 And here is our Data Preprocessing Template.mp425.86MB

04 Simple Linear Regression/017 How to get the dataset.mp411.71MB

04 Simple Linear Regression/018 Dataset Business Problem Description.mp47.77MB

04 Simple Linear Regression/019 Simple Linear Regression Intuition - Step 1.mp410.52MB

04 Simple Linear Regression/020 Simple Linear Regression Intuition - Step 2.mp45.99MB

04 Simple Linear Regression/021 Simple Linear Regression in Python - Step 1.mp427.92MB

04 Simple Linear Regression/022 Simple Linear Regression in Python - Step 2.mp424.62MB

04 Simple Linear Regression/023 Simple Linear Regression in Python - Step 3.mp420.55MB

04 Simple Linear Regression/024 Simple Linear Regression in Python - Step 4.mp439.37MB

04 Simple Linear Regression/025 Simple Linear Regression in R - Step 1.mp411.52MB

04 Simple Linear Regression/026 Simple Linear Regression in R - Step 2.mp424.87MB

04 Simple Linear Regression/027 Simple Linear Regression in R - Step 3.mp411.42MB

04 Simple Linear Regression/028 Simple Linear Regression in R - Step 4.mp449.16MB

05 Multiple Linear Regression/029 How to get the dataset.mp411.71MB

05 Multiple Linear Regression/030 Dataset Business Problem Description.mp412.56MB

05 Multiple Linear Regression/031 Multiple Linear Regression Intuition - Step 1.mp42MB

05 Multiple Linear Regression/032 Multiple Linear Regression Intuition - Step 2.mp42.03MB

05 Multiple Linear Regression/033 Multiple Linear Regression Intuition - Step 3.mp416.59MB

05 Multiple Linear Regression/034 Multiple Linear Regression Intuition - Step 4.mp45.34MB

05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 5.mp432.8MB

05 Multiple Linear Regression/036 Multiple Linear Regression in Python - Step 1.mp452.18MB

05 Multiple Linear Regression/037 Multiple Linear Regression in Python - Step 2.mp49.84MB

05 Multiple Linear Regression/038 Multiple Linear Regression in Python - Step 3.mp425.48MB

05 Multiple Linear Regression/039 Multiple Linear Regression in Python - Backward Elimination - Preparation.mp454.54MB

05 Multiple Linear Regression/040 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK.mp459.14MB

05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp454.26MB

05 Multiple Linear Regression/042 Multiple Linear Regression in R - Step 1.mp423.44MB

05 Multiple Linear Regression/043 Multiple Linear Regression in R - Step 2.mp445.22MB

05 Multiple Linear Regression/044 Multiple Linear Regression in R - Step 3.mp413.85MB

05 Multiple Linear Regression/045 Multiple Linear Regression in R - Backward Elimination - HOMEWORK.mp450.78MB

05 Multiple Linear Regression/046 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp421.95MB

06 Polynomial Regression/047 Polynomial Regression Intuition.mp49.44MB

06 Polynomial Regression/048 How to get the dataset.mp411.71MB

06 Polynomial Regression/049 Polynomial Regression in Python - Step 1.mp431.64MB

06 Polynomial Regression/050 Polynomial Regression in Python - Step 2.mp435.11MB

06 Polynomial Regression/051 Polynomial Regression in Python - Step 3.mp454.5MB

06 Polynomial Regression/052 Polynomial Regression in Python - Step 4.mp417.65MB

06 Polynomial Regression/053 Python Regression Template.mp436.78MB

06 Polynomial Regression/054 Polynomial Regression in R - Step 1.mp421.21MB

06 Polynomial Regression/055 Polynomial Regression in R - Step 2.mp432.28MB

06 Polynomial Regression/056 Polynomial Regression in R - Step 3.mp454.8MB

06 Polynomial Regression/057 Polynomial Regression in R - Step 4.mp428.52MB

06 Polynomial Regression/058 R Regression Template.mp431.33MB

07 Support Vector Regression SVR/059 How to get the dataset.mp411.71MB

07 Support Vector Regression SVR/060 SVR in Python.mp460.22MB

07 Support Vector Regression SVR/061 SVR in R.mp433.73MB

08 Decision Tree Regression/062 Decision Tree Regression Intuition.mp425.33MB

08 Decision Tree Regression/063 How to get the dataset.mp411.71MB

08 Decision Tree Regression/064 Decision Tree Regression in Python.mp443.44MB

08 Decision Tree Regression/065 Decision Tree Regression in R.mp456.23MB

09 Random Forest Regression/066 Random Forest Regression Intuition.mp415.65MB

09 Random Forest Regression/067 How to get the dataset.mp411.71MB

09 Random Forest Regression/068 Random Forest Regression in Python.mp452.69MB

09 Random Forest Regression/069 Random Forest Regression in R.mp451.86MB

10 Evaluating Regression Models Performance/070 R-Squared Intuition.mp49.8MB

10 Evaluating Regression Models Performance/071 Adjusted R-Squared Intuition.mp421.41MB

10 Evaluating Regression Models Performance/072 Evaluating Regression Models Performance - Homeworks Final Part.mp428.35MB

10 Evaluating Regression Models Performance/073 Interpreting Linear Regression Coefficients.mp427.38MB

12 Logistic Regression/076 Logistic Regression Intuition.mp429.17MB

12 Logistic Regression/077 How to get the dataset.mp411.71MB

12 Logistic Regression/078 Logistic Regression in Python - Step 1.mp416.84MB

12 Logistic Regression/079 Logistic Regression in Python - Step 2.mp411.1MB

12 Logistic Regression/080 Logistic Regression in Python - Step 3.mp47.98MB

12 Logistic Regression/081 Logistic Regression in Python - Step 4.mp413.87MB

12 Logistic Regression/082 Logistic Regression in Python - Step 5.mp453.15MB

12 Logistic Regression/083 Python Classification Template.mp417.58MB

12 Logistic Regression/084 Logistic Regression in R - Step 1.mp415.72MB

12 Logistic Regression/085 Logistic Regression in R - Step 2.mp414.85MB

12 Logistic Regression/086 Logistic Regression in R - Step 3.mp427.44MB

12 Logistic Regression/087 Logistic Regression in R - Step 4.mp411.73MB

12 Logistic Regression/088 Logistic Regression in R - Step 5.mp493.76MB

12 Logistic Regression/089 R Classification Template.mp417.5MB

13 K-Nearest Neighbors K-NN/090 K-Nearest Neighbor Intuition.mp410.48MB

13 K-Nearest Neighbors K-NN/091 How to get the dataset.mp411.71MB

13 K-Nearest Neighbors K-NN/092 K-NN in Python.mp446.98MB

13 K-Nearest Neighbors K-NN/093 K-NN in R.mp455.77MB

14 Support Vector Machine SVM/094 SVM Intuition.mp419.92MB

14 Support Vector Machine SVM/095 How to get the dataset.mp411.71MB

14 Support Vector Machine SVM/096 SVM in Python.mp441.71MB

14 Support Vector Machine SVM/097 SVM in R.mp465.31MB

15 Kernel SVM/098 Kernel SVM Intuition.mp46.42MB

15 Kernel SVM/099 Mapping to a higher dimension.mp415.39MB

15 Kernel SVM/100 The Kernel Trick.mp434.72MB

15 Kernel SVM/101 Types of Kernel Functions.mp415.71MB

15 Kernel SVM/102 How to get the dataset.mp411.71MB

15 Kernel SVM/103 Kernel SVM in Python.mp454.86MB

15 Kernel SVM/104 Kernel SVM in R.mp452.82MB

16 Naive Bayes/105 Bayes Theorem.mp450.43MB

16 Naive Bayes/106 Naive Bayes Intuition.mp431.1MB

16 Naive Bayes/107 Naive Bayes Intuition Challenge Reveal.mp413.27MB

16 Naive Bayes/108 Naive Bayes Intuition Extras.mp418.94MB

16 Naive Bayes/109 How to get the dataset.mp411.71MB

16 Naive Bayes/110 Naive Bayes in Python.mp431.14MB

16 Naive Bayes/111 Naive Bayes in R.mp449.79MB

17 Decision Tree Classification/112 Decision Tree Classification Intuition.mp421.63MB

17 Decision Tree Classification/113 How to get the dataset.mp411.71MB

17 Decision Tree Classification/114 Decision Tree Classification in Python.mp438.85MB

17 Decision Tree Classification/115 Decision Tree Classification in R.mp468.18MB

18 Random Forest Classification/116 Random Forest Classification Intuition.mp425.66MB

18 Random Forest Classification/117 How to get the dataset.mp411.71MB

18 Random Forest Classification/118 Random Forest Classification in Python.mp462.04MB

18 Random Forest Classification/119 Random Forest Classification in R.mp464.11MB

19 Evaluating Classification Models Performance/120 False Positives False Negatives.mp415.12MB

19 Evaluating Classification Models Performance/121 Confusion Matrix.mp48.91MB

19 Evaluating Classification Models Performance/122 Accuracy Paradox.mp44.21MB

19 Evaluating Classification Models Performance/123 CAP Curve.mp420.31MB

19 Evaluating Classification Models Performance/124 CAP Curve Analysis.mp412.94MB

21 K-Means Clustering/127 K-Means Clustering Intuition.mp429.97MB

21 K-Means Clustering/128 K-Means Random Initialization Trap.mp415.36MB

21 K-Means Clustering/129 K-Means Selecting The Number Of Clusters.mp425.68MB

21 K-Means Clustering/130 How to get the dataset.mp411.71MB

21 K-Means Clustering/131 K-Means Clustering in Python.mp449.81MB

21 K-Means Clustering/132 K-Means Clustering in R.mp436.91MB

22 Hierarchical Clustering/133 Hierarchical Clustering Intuition.mp416.52MB

22 Hierarchical Clustering/134 Hierarchical Clustering How Dendrograms Work.mp417.46MB

22 Hierarchical Clustering/135 Hierarchical Clustering Using Dendrograms.mp422.81MB

22 Hierarchical Clustering/136 How to get the dataset.mp411.71MB

22 Hierarchical Clustering/137 HC in Python - Step 1.mp413.77MB

22 Hierarchical Clustering/138 HC in Python - Step 2.mp415.51MB

22 Hierarchical Clustering/139 HC in Python - Step 3.mp416.17MB

22 Hierarchical Clustering/140 HC in Python - Step 4.mp421.32MB

22 Hierarchical Clustering/141 HC in Python - Step 5.mp49.92MB

22 Hierarchical Clustering/142 HC in R - Step 1.mp48.59MB

22 Hierarchical Clustering/143 HC in R - Step 2.mp413.87MB

22 Hierarchical Clustering/144 HC in R - Step 3.mp49.95MB

22 Hierarchical Clustering/145 HC in R - Step 4.mp410.17MB

22 Hierarchical Clustering/146 HC in R - Step 5.mp413.68MB

24 Apriori/149 Apriori Intuition.mp435.02MB

24 Apriori/150 How to get the dataset.mp411.71MB

24 Apriori/151 Apriori in R - Step 1.mp452.83MB

24 Apriori/152 Apriori in R - Step 2.mp438.81MB

24 Apriori/153 Apriori in R - Step 3.mp456.51MB

24 Apriori/154 Apriori in Python - Step 1.mp447.41MB

24 Apriori/155 Apriori in Python - Step 2.mp437.32MB

24 Apriori/156 Apriori in Python - Step 3.mp435.3MB

25 Eclat/157 Eclat Intuition.mp410.65MB

25 Eclat/158 How to get the dataset.mp411.71MB

25 Eclat/159 Eclat in R.mp425.26MB

27 Upper Confidence Bound UCB/161 The Multi-Armed Bandit Problem.mp430.19MB

27 Upper Confidence Bound UCB/162 Upper Confidence Bound UCB Intuition.mp429.32MB

27 Upper Confidence Bound UCB/163 How to get the dataset.mp411.71MB

27 Upper Confidence Bound UCB/164 Upper Confidence Bound in Python - Step 1.mp439.01MB

27 Upper Confidence Bound UCB/165 Upper Confidence Bound in Python - Step 2.mp444.49MB

27 Upper Confidence Bound UCB/166 Upper Confidence Bound in Python - Step 3.mp453.71MB

27 Upper Confidence Bound UCB/167 Upper Confidence Bound in Python - Step 4.mp412.44MB

27 Upper Confidence Bound UCB/168 Upper Confidence Bound in R - Step 1.mp434.01MB

27 Upper Confidence Bound UCB/169 Upper Confidence Bound in R - Step 2.mp434.1MB

27 Upper Confidence Bound UCB/170 Upper Confidence Bound in R - Step 3.mp457.84MB

27 Upper Confidence Bound UCB/171 Upper Confidence Bound in R - Step 4.mp49.55MB

28 Thompson Sampling/172 Thompson Sampling Intuition.mp437.27MB

28 Thompson Sampling/173 Algorithm Comparison UCB vs Thompson Sampling.mp414.08MB

28 Thompson Sampling/174 How to get the dataset.mp411.71MB

28 Thompson Sampling/175 Thompson Sampling in Python - Step 1.mp455.52MB

28 Thompson Sampling/176 Thompson Sampling in Python - Step 2.mp411.22MB

28 Thompson Sampling/177 Thompson Sampling in R - Step 1.mp451.04MB

28 Thompson Sampling/178 Thompson Sampling in R - Step 2.mp49.56MB

29 --------------------- Part 7 Natural Language Processing ---------------------/180 How to get the dataset.mp411.71MB

29 --------------------- Part 7 Natural Language Processing ---------------------/181 Natural Language Processing in Python - Step 1.mp446.06MB

29 --------------------- Part 7 Natural Language Processing ---------------------/182 Natural Language Processing in Python - Step 2.mp427.44MB

29 --------------------- Part 7 Natural Language Processing ---------------------/183 Natural Language Processing in Python - Step 3.mp44.16MB

29 --------------------- Part 7 Natural Language Processing ---------------------/184 Natural Language Processing in Python - Step 4.mp429.75MB

29 --------------------- Part 7 Natural Language Processing ---------------------/185 Natural Language Processing in Python - Step 5.mp418.8MB

29 --------------------- Part 7 Natural Language Processing ---------------------/186 Natural Language Processing in Python - Step 6.mp48.32MB

29 --------------------- Part 7 Natural Language Processing ---------------------/187 Natural Language Processing in Python - Step 7.mp422.13MB

29 --------------------- Part 7 Natural Language Processing ---------------------/188 Natural Language Processing in Python - Step 8.mp452.02MB

29 --------------------- Part 7 Natural Language Processing ---------------------/189 Natural Language Processing in Python - Step 9.mp418.9MB

29 --------------------- Part 7 Natural Language Processing ---------------------/190 Natural Language Processing in Python - Step 10.mp432.91MB

29 --------------------- Part 7 Natural Language Processing ---------------------/192 Natural Language Processing in R - Step 1.mp451.2MB

29 --------------------- Part 7 Natural Language Processing ---------------------/193 Natural Language Processing in R - Step 2.mp421.66MB

29 --------------------- Part 7 Natural Language Processing ---------------------/194 Natural Language Processing in R - Step 3.mp416.89MB

29 --------------------- Part 7 Natural Language Processing ---------------------/195 Natural Language Processing in R - Step 4.mp48.24MB

29 --------------------- Part 7 Natural Language Processing ---------------------/196 Natural Language Processing in R - Step 5.mp45.78MB

29 --------------------- Part 7 Natural Language Processing ---------------------/197 Natural Language Processing in R - Step 6.mp416.09MB

29 --------------------- Part 7 Natural Language Processing ---------------------/198 Natural Language Processing in R - Step 7.mp49.59MB

29 --------------------- Part 7 Natural Language Processing ---------------------/199 Natural Language Processing in R - Step 8.mp417.23MB

29 --------------------- Part 7 Natural Language Processing ---------------------/200 Natural Language Processing in R - Step 9.mp437.69MB

29 --------------------- Part 7 Natural Language Processing ---------------------/201 Natural Language Processing in R - Step 10.mp454.14MB

30 ---------------------------- Part 8 Deep Learning ----------------------------/204 What is Deep Learning.mp431.31MB

31 Artificial Neural Networks/205 Plan of attack.mp44.74MB

31 Artificial Neural Networks/206 The Neuron.mp429.86MB

31 Artificial Neural Networks/207 The Activation Function.mp414.75MB

31 Artificial Neural Networks/208 How do Neural Networks work.mp423.53MB

31 Artificial Neural Networks/209 How do Neural Networks learn.mp426.55MB

31 Artificial Neural Networks/210 Gradient Descent.mp418.53MB

31 Artificial Neural Networks/211 Stochastic Gradient Descent.mp416.82MB

31 Artificial Neural Networks/212 Backpropagation.mp410.92MB

31 Artificial Neural Networks/213 How to get the dataset.mp411.71MB

31 Artificial Neural Networks/214 Business Problem Description.mp429.23MB

31 Artificial Neural Networks/215 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras.mp437.45MB

31 Artificial Neural Networks/216 ANN in Python - Step 2.mp484.87MB

31 Artificial Neural Networks/217 ANN in Python - Step 3.mp414.62MB

31 Artificial Neural Networks/218 ANN in Python - Step 4.mp49.69MB

31 Artificial Neural Networks/219 ANN in Python - Step 5.mp439.36MB

31 Artificial Neural Networks/220 ANN in Python - Step 6.mp411.93MB

31 Artificial Neural Networks/221 ANN in Python - Step 7.mp414.92MB

31 Artificial Neural Networks/222 ANN in Python - Step 8.mp434.03MB

31 Artificial Neural Networks/223 ANN in Python - Step 9.mp428.47MB

31 Artificial Neural Networks/224 ANN in Python - Step 10.mp428.42MB

31 Artificial Neural Networks/225 ANN in R - Step 1.mp449.89MB

31 Artificial Neural Networks/226 ANN in R - Step 2.mp418.24MB

31 Artificial Neural Networks/227 ANN in R - Step 3.mp437.85MB

31 Artificial Neural Networks/228 ANN in R - Step 4 Last step.mp443.75MB

32 Convolutional Neural Networks/229 Plan of attack.mp45.9MB

32 Convolutional Neural Networks/230 What are convolutional neural networks.mp429.5MB

32 Convolutional Neural Networks/231 Step 1 - Convolution Operation.mp431.02MB

32 Convolutional Neural Networks/232 Step 1b - ReLU Layer.mp414.09MB

32 Convolutional Neural Networks/233 Step 2 - Pooling.mp440.24MB

32 Convolutional Neural Networks/234 Step 3 - Flattening.mp43.27MB

32 Convolutional Neural Networks/235 Step 4 - Full Connection.mp442.74MB

32 Convolutional Neural Networks/236 Summary.mp47.91MB

32 Convolutional Neural Networks/237 Softmax Cross-Entropy.mp433.23MB

32 Convolutional Neural Networks/238 How to get the dataset.mp411.71MB

32 Convolutional Neural Networks/239 CNN in Python - Step 1.mp430.6MB

32 Convolutional Neural Networks/240 CNN in Python - Step 2.mp47.2MB

32 Convolutional Neural Networks/241 CNN in Python - Step 3.mp42.8MB

32 Convolutional Neural Networks/242 CNN in Python - Step 4.mp434.62MB

32 Convolutional Neural Networks/243 CNN in Python - Step 5.mp412.38MB

32 Convolutional Neural Networks/244 CNN in Python - Step 6.mp411.94MB

32 Convolutional Neural Networks/245 CNN in Python - Step 7.mp416.65MB

32 Convolutional Neural Networks/246 CNN in Python - Step 8.mp48.95MB

32 Convolutional Neural Networks/247 CNN in Python - Step 9.mp462.41MB

32 Convolutional Neural Networks/248 CNN in Python - Step 10.mp427.74MB

34 Principal Component Analysis PCA/251 How to get the dataset.mp411.71MB

34 Principal Component Analysis PCA/252 PCA in Python - Step 1.mp431.95MB

34 Principal Component Analysis PCA/253 PCA in Python - Step 2.mp422.07MB

34 Principal Component Analysis PCA/254 PCA in Python - Step 3.mp425.51MB

34 Principal Component Analysis PCA/255 PCA in R - Step 1.mp430.65MB

34 Principal Component Analysis PCA/256 PCA in R - Step 2.mp429.02MB

34 Principal Component Analysis PCA/257 PCA in R - Step 3.mp436.73MB

35 Linear Discriminant Analysis LDA/258 How to get the dataset.mp411.71MB

35 Linear Discriminant Analysis LDA/259 LDA in Python.mp445.42MB

35 Linear Discriminant Analysis LDA/260 LDA in R.mp451.29MB

36 Kernel PCA/261 How to get the dataset.mp411.71MB

36 Kernel PCA/262 Kernel PCA in Python.mp433.38MB

36 Kernel PCA/263 Kernel PCA in R.mp456.57MB

38 Model Selection/265 How to get the dataset.mp411.71MB

38 Model Selection/266 k-Fold Cross Validation in Python.mp432.83MB

38 Model Selection/267 k-Fold Cross Validation in R.mp443.63MB

38 Model Selection/268 Grid Search in Python - Step 1.mp438.21MB

38 Model Selection/269 Grid Search in Python - Step 2.mp429.51MB

38 Model Selection/270 Grid Search in R.mp435.54MB

39 XGBoost/271 How to get the dataset.mp411.71MB

39 XGBoost/272 XGBoost in Python - Step 1.mp421.39MB

39 XGBoost/273 XGBoost in Python - Step 2.mp431.97MB

39 XGBoost/274 XGBoost in R.mp447.26MB