[] machinelearning

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文件列表

12 Logistic Regression/088 Logistic Regression in R - Step 5.mp4 94MB
31 Artificial Neural Networks/216 ANN in Python - Step 2.mp4 85MB
17 Decision Tree Classification/115 Decision Tree Classification in R.mp4 68MB
14 Support Vector Machine SVM/097 SVM in R.mp4 65MB
18 Random Forest Classification/119 Random Forest Classification in R.mp4 64MB
32 Convolutional Neural Networks/247 CNN in Python - Step 9.mp4 62MB
18 Random Forest Classification/118 Random Forest Classification in Python.mp4 62MB
07 Support Vector Regression SVR/060 SVR in Python.mp4 60MB
05 Multiple Linear Regression/040 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK.mp4 59MB
27 Upper Confidence Bound UCB/170 Upper Confidence Bound in R - Step 3.mp4 58MB
36 Kernel PCA/263 Kernel PCA in R.mp4 57MB
24 Apriori/153 Apriori in R - Step 3.mp4 57MB
08 Decision Tree Regression/065 Decision Tree Regression in R.mp4 56MB
13 K-Nearest Neighbors K-NN/093 K-NN in R.mp4 56MB
28 Thompson Sampling/175 Thompson Sampling in Python - Step 1.mp4 56MB
15 Kernel SVM/103 Kernel SVM in Python.mp4 55MB
06 Polynomial Regression/056 Polynomial Regression in R - Step 3.mp4 55MB
05 Multiple Linear Regression/039 Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 55MB
06 Polynomial Regression/051 Polynomial Regression in Python - Step 3.mp4 55MB
05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 54MB
29 --------------------- Part 7 Natural Language Processing ---------------------/201 Natural Language Processing in R - Step 10.mp4 54MB
27 Upper Confidence Bound UCB/166 Upper Confidence Bound in Python - Step 3.mp4 54MB
12 Logistic Regression/082 Logistic Regression in Python - Step 5.mp4 53MB
02 -------------------------- Part 1 Data Preprocessing --------------------------/012 Categorical Data.mp4 53MB
24 Apriori/151 Apriori in R - Step 1.mp4 53MB
15 Kernel SVM/104 Kernel SVM in R.mp4 53MB
09 Random Forest Regression/068 Random Forest Regression in Python.mp4 53MB
05 Multiple Linear Regression/036 Multiple Linear Regression in Python - Step 1.mp4 52MB
29 --------------------- Part 7 Natural Language Processing ---------------------/188 Natural Language Processing in Python - Step 8.mp4 52MB
09 Random Forest Regression/069 Random Forest Regression in R.mp4 52MB
35 Linear Discriminant Analysis LDA/260 LDA in R.mp4 51MB
29 --------------------- Part 7 Natural Language Processing ---------------------/192 Natural Language Processing in R - Step 1.mp4 51MB
28 Thompson Sampling/177 Thompson Sampling in R - Step 1.mp4 51MB
02 -------------------------- Part 1 Data Preprocessing --------------------------/013 Splitting the Dataset into the Training set and Test set.mp4 51MB
05 Multiple Linear Regression/045 Multiple Linear Regression in R - Backward Elimination - HOMEWORK.mp4 51MB
16 Naive Bayes/105 Bayes Theorem.mp4 50MB
31 Artificial Neural Networks/225 ANN in R - Step 1.mp4 50MB
21 K-Means Clustering/131 K-Means Clustering in Python.mp4 50MB
16 Naive Bayes/111 Naive Bayes in R.mp4 50MB
04 Simple Linear Regression/028 Simple Linear Regression in R - Step 4.mp4 49MB
24 Apriori/154 Apriori in Python - Step 1.mp4 47MB
39 XGBoost/274 XGBoost in R.mp4 47MB
13 K-Nearest Neighbors K-NN/092 K-NN in Python.mp4 47MB
29 --------------------- Part 7 Natural Language Processing ---------------------/181 Natural Language Processing in Python - Step 1.mp4 46MB
35 Linear Discriminant Analysis LDA/259 LDA in Python.mp4 45MB
05 Multiple Linear Regression/043 Multiple Linear Regression in R - Step 2.mp4 45MB
02 -------------------------- Part 1 Data Preprocessing --------------------------/014 Feature Scaling.mp4 45MB
27 Upper Confidence Bound UCB/165 Upper Confidence Bound in Python - Step 2.mp4 44MB
31 Artificial Neural Networks/228 ANN in R - Step 4 Last step.mp4 44MB
38 Model Selection/267 k-Fold Cross Validation in R.mp4 44MB
08 Decision Tree Regression/064 Decision Tree Regression in Python.mp4 43MB
32 Convolutional Neural Networks/235 Step 4 - Full Connection.mp4 43MB
14 Support Vector Machine SVM/096 SVM in Python.mp4 42MB
32 Convolutional Neural Networks/233 Step 2 - Pooling.mp4 40MB
04 Simple Linear Regression/024 Simple Linear Regression in Python - Step 4.mp4 39MB
31 Artificial Neural Networks/219 ANN in Python - Step 5.mp4 39MB
02 -------------------------- Part 1 Data Preprocessing --------------------------/011 Missing Data.mp4 39MB
27 Upper Confidence Bound UCB/164 Upper Confidence Bound in Python - Step 1.mp4 39MB
17 Decision Tree Classification/114 Decision Tree Classification in Python.mp4 39MB
24 Apriori/152 Apriori in R - Step 2.mp4 39MB
38 Model Selection/268 Grid Search in Python - Step 1.mp4 38MB
31 Artificial Neural Networks/227 ANN in R - Step 3.mp4 38MB
29 --------------------- Part 7 Natural Language Processing ---------------------/200 Natural Language Processing in R - Step 9.mp4 38MB
31 Artificial Neural Networks/215 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras.mp4 37MB
24 Apriori/155 Apriori in Python - Step 2.mp4 37MB
28 Thompson Sampling/172 Thompson Sampling Intuition.mp4 37MB
21 K-Means Clustering/132 K-Means Clustering in R.mp4 37MB
06 Polynomial Regression/053 Python Regression Template.mp4 37MB
34 Principal Component Analysis PCA/257 PCA in R - Step 3.mp4 37MB
38 Model Selection/270 Grid Search in R.mp4 36MB
24 Apriori/156 Apriori in Python - Step 3.mp4 35MB
06 Polynomial Regression/050 Polynomial Regression in Python - Step 2.mp4 35MB
24 Apriori/149 Apriori Intuition.mp4 35MB
15 Kernel SVM/100 The Kernel Trick.mp4 35MB
32 Convolutional Neural Networks/242 CNN in Python - Step 4.mp4 35MB
27 Upper Confidence Bound UCB/169 Upper Confidence Bound in R - Step 2.mp4 34MB
31 Artificial Neural Networks/222 ANN in Python - Step 8.mp4 34MB
27 Upper Confidence Bound UCB/168 Upper Confidence Bound in R - Step 1.mp4 34MB
07 Support Vector Regression SVR/061 SVR in R.mp4 34MB
36 Kernel PCA/262 Kernel PCA in Python.mp4 33MB
32 Convolutional Neural Networks/237 Softmax Cross-Entropy.mp4 33MB
29 --------------------- Part 7 Natural Language Processing ---------------------/190 Natural Language Processing in Python - Step 10.mp4 33MB
38 Model Selection/266 k-Fold Cross Validation in Python.mp4 33MB
05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 5.mp4 33MB
06 Polynomial Regression/055 Polynomial Regression in R - Step 2.mp4 32MB
39 XGBoost/273 XGBoost in Python - Step 2.mp4 32MB
34 Principal Component Analysis PCA/252 PCA in Python - Step 1.mp4 32MB
06 Polynomial Regression/049 Polynomial Regression in Python - Step 1.mp4 32MB
06 Polynomial Regression/058 R Regression Template.mp4 31MB
30 ---------------------------- Part 8 Deep Learning ----------------------------/204 What is Deep Learning.mp4 31MB
16 Naive Bayes/110 Naive Bayes in Python.mp4 31MB
16 Naive Bayes/106 Naive Bayes Intuition.mp4 31MB
32 Convolutional Neural Networks/231 Step 1 - Convolution Operation.mp4 31MB
34 Principal Component Analysis PCA/255 PCA in R - Step 1.mp4 31MB
32 Convolutional Neural Networks/239 CNN in Python - Step 1.mp4 31MB
27 Upper Confidence Bound UCB/161 The Multi-Armed Bandit Problem.mp4 30MB
21 K-Means Clustering/127 K-Means Clustering Intuition.mp4 30MB
31 Artificial Neural Networks/206 The Neuron.mp4 30MB
29 --------------------- Part 7 Natural Language Processing ---------------------/184 Natural Language Processing in Python - Step 4.mp4 30MB
38 Model Selection/269 Grid Search in Python - Step 2.mp4 30MB
32 Convolutional Neural Networks/230 What are convolutional neural networks.mp4 29MB
27 Upper Confidence Bound UCB/162 Upper Confidence Bound UCB Intuition.mp4 29MB
31 Artificial Neural Networks/214 Business Problem Description.mp4 29MB
12 Logistic Regression/076 Logistic Regression Intuition.mp4 29MB
34 Principal Component Analysis PCA/256 PCA in R - Step 2.mp4 29MB
02 -------------------------- Part 1 Data Preprocessing --------------------------/009 Importing the Dataset.mp4 29MB
06 Polynomial Regression/057 Polynomial Regression in R - Step 4.mp4 29MB
31 Artificial Neural Networks/223 ANN in Python - Step 9.mp4 28MB
31 Artificial Neural Networks/224 ANN in Python - Step 10.mp4 28MB
10 Evaluating Regression Models Performance/072 Evaluating Regression Models Performance - Homeworks Final Part.mp4 28MB
04 Simple Linear Regression/021 Simple Linear Regression in Python - Step 1.mp4 28MB
32 Convolutional Neural Networks/248 CNN in Python - Step 10.mp4 28MB
12 Logistic Regression/086 Logistic Regression in R - Step 3.mp4 27MB
29 --------------------- Part 7 Natural Language Processing ---------------------/182 Natural Language Processing in Python - Step 2.mp4 27MB
10 Evaluating Regression Models Performance/073 Interpreting Linear Regression Coefficients.mp4 27MB
31 Artificial Neural Networks/209 How do Neural Networks learn.mp4 27MB
02 -------------------------- Part 1 Data Preprocessing --------------------------/015 And here is our Data Preprocessing Template.mp4 26MB
21 K-Means Clustering/129 K-Means Selecting The Number Of Clusters.mp4 26MB
18 Random Forest Classification/116 Random Forest Classification Intuition.mp4 26MB
34 Principal Component Analysis PCA/254 PCA in Python - Step 3.mp4 26MB
05 Multiple Linear Regression/038 Multiple Linear Regression in Python - Step 3.mp4 25MB
08 Decision Tree Regression/062 Decision Tree Regression Intuition.mp4 25MB
25 Eclat/159 Eclat in R.mp4 25MB
04 Simple Linear Regression/026 Simple Linear Regression in R - Step 2.mp4 25MB
04 Simple Linear Regression/022 Simple Linear Regression in Python - Step 2.mp4 25MB
01 Welcome to the course/004 Installing Python and Anaconda MAC Windows.mp4 24MB
31 Artificial Neural Networks/208 How do Neural Networks work.mp4 24MB
05 Multiple Linear Regression/042 Multiple Linear Regression in R - Step 1.mp4 23MB
01 Welcome to the course/003 Installing R and R Studio MAC Windows.mp4 23MB
22 Hierarchical Clustering/135 Hierarchical Clustering Using Dendrograms.mp4 23MB
29 --------------------- Part 7 Natural Language Processing ---------------------/187 Natural Language Processing in Python - Step 7.mp4 22MB
34 Principal Component Analysis PCA/253 PCA in Python - Step 2.mp4 22MB
05 Multiple Linear Regression/046 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 22MB
29 --------------------- Part 7 Natural Language Processing ---------------------/193 Natural Language Processing in R - Step 2.mp4 22MB
17 Decision Tree Classification/112 Decision Tree Classification Intuition.mp4 22MB
10 Evaluating Regression Models Performance/071 Adjusted R-Squared Intuition.mp4 21MB
39 XGBoost/272 XGBoost in Python - Step 1.mp4 21MB
22 Hierarchical Clustering/140 HC in Python - Step 4.mp4 21MB
06 Polynomial Regression/054 Polynomial Regression in R - Step 1.mp4 21MB
02 -------------------------- Part 1 Data Preprocessing --------------------------/007 Get the dataset.mp4 21MB
04 Simple Linear Regression/023 Simple Linear Regression in Python - Step 3.mp4 21MB
19 Evaluating Classification Models Performance/123 CAP Curve.mp4 20MB
14 Support Vector Machine SVM/094 SVM Intuition.mp4 20MB
16 Naive Bayes/108 Naive Bayes Intuition Extras.mp4 19MB
29 --------------------- Part 7 Natural Language Processing ---------------------/189 Natural Language Processing in Python - Step 9.mp4 19MB
29 --------------------- Part 7 Natural Language Processing ---------------------/185 Natural Language Processing in Python - Step 5.mp4 19MB
31 Artificial Neural Networks/210 Gradient Descent.mp4 19MB
31 Artificial Neural Networks/226 ANN in R - Step 2.mp4 18MB
06 Polynomial Regression/052 Polynomial Regression in Python - Step 4.mp4 18MB
12 Logistic Regression/083 Python Classification Template.mp4 18MB
12 Logistic Regression/089 R Classification Template.mp4 18MB
22 Hierarchical Clustering/134 Hierarchical Clustering How Dendrograms Work.mp4 17MB
29 --------------------- Part 7 Natural Language Processing ---------------------/199 Natural Language Processing in R - Step 8.mp4 17MB
29 --------------------- Part 7 Natural Language Processing ---------------------/194 Natural Language Processing in R - Step 3.mp4 17MB
12 Logistic Regression/078 Logistic Regression in Python - Step 1.mp4 17MB
31 Artificial Neural Networks/211 Stochastic Gradient Descent.mp4 17MB
32 Convolutional Neural Networks/245 CNN in Python - Step 7.mp4 17MB
05 Multiple Linear Regression/033 Multiple Linear Regression Intuition - Step 3.mp4 17MB
22 Hierarchical Clustering/133 Hierarchical Clustering Intuition.mp4 17MB
22 Hierarchical Clustering/139 HC in Python - Step 3.mp4 16MB
29 --------------------- Part 7 Natural Language Processing ---------------------/197 Natural Language Processing in R - Step 6.mp4 16MB
12 Logistic Regression/084 Logistic Regression in R - Step 1.mp4 16MB
15 Kernel SVM/101 Types of Kernel Functions.mp4 16MB
09 Random Forest Regression/066 Random Forest Regression Intuition.mp4 16MB
22 Hierarchical Clustering/138 HC in Python - Step 2.mp4 16MB
15 Kernel SVM/099 Mapping to a higher dimension.mp4 15MB
21 K-Means Clustering/128 K-Means Random Initialization Trap.mp4 15MB
19 Evaluating Classification Models Performance/120 False Positives False Negatives.mp4 15MB
31 Artificial Neural Networks/221 ANN in Python - Step 7.mp4 15MB
12 Logistic Regression/085 Logistic Regression in R - Step 2.mp4 15MB
31 Artificial Neural Networks/207 The Activation Function.mp4 15MB
31 Artificial Neural Networks/217 ANN in Python - Step 3.mp4 15MB
01 Welcome to the course/002 Why Machine Learning is the Future.mp4 14MB
32 Convolutional Neural Networks/232 Step 1b - ReLU Layer.mp4 14MB
28 Thompson Sampling/173 Algorithm Comparison UCB vs Thompson Sampling.mp4 14MB
12 Logistic Regression/081 Logistic Regression in Python - Step 4.mp4 14MB
22 Hierarchical Clustering/143 HC in R - Step 2.mp4 14MB
05 Multiple Linear Regression/044 Multiple Linear Regression in R - Step 3.mp4 14MB
22 Hierarchical Clustering/137 HC in Python - Step 1.mp4 14MB
22 Hierarchical Clustering/146 HC in R - Step 5.mp4 14MB
02 -------------------------- Part 1 Data Preprocessing --------------------------/008 Importing the Libraries.mp4 14MB
16 Naive Bayes/107 Naive Bayes Intuition Challenge Reveal.mp4 13MB
19 Evaluating Classification Models Performance/124 CAP Curve Analysis.mp4 13MB
05 Multiple Linear Regression/030 Dataset Business Problem Description.mp4 13MB
27 Upper Confidence Bound UCB/167 Upper Confidence Bound in Python - Step 4.mp4 12MB
32 Convolutional Neural Networks/243 CNN in Python - Step 5.mp4 12MB
32 Convolutional Neural Networks/244 CNN in Python - Step 6.mp4 12MB
31 Artificial Neural Networks/220 ANN in Python - Step 6.mp4 12MB
12 Logistic Regression/087 Logistic Regression in R - Step 4.mp4 12MB
04 Simple Linear Regression/017 How to get the dataset.mp4 12MB
05 Multiple Linear Regression/029 How to get the dataset.mp4 12MB
06 Polynomial Regression/048 How to get the dataset.mp4 12MB
07 Support Vector Regression SVR/059 How to get the dataset.mp4 12MB
08 Decision Tree Regression/063 How to get the dataset.mp4 12MB
09 Random Forest Regression/067 How to get the dataset.mp4 12MB
12 Logistic Regression/077 How to get the dataset.mp4 12MB
13 K-Nearest Neighbors K-NN/091 How to get the dataset.mp4 12MB
14 Support Vector Machine SVM/095 How to get the dataset.mp4 12MB
15 Kernel SVM/102 How to get the dataset.mp4 12MB
16 Naive Bayes/109 How to get the dataset.mp4 12MB
17 Decision Tree Classification/113 How to get the dataset.mp4 12MB
18 Random Forest Classification/117 How to get the dataset.mp4 12MB
21 K-Means Clustering/130 How to get the dataset.mp4 12MB
22 Hierarchical Clustering/136 How to get the dataset.mp4 12MB
24 Apriori/150 How to get the dataset.mp4 12MB
25 Eclat/158 How to get the dataset.mp4 12MB
27 Upper Confidence Bound UCB/163 How to get the dataset.mp4 12MB
28 Thompson Sampling/174 How to get the dataset.mp4 12MB
29 --------------------- Part 7 Natural Language Processing ---------------------/180 How to get the dataset.mp4 12MB
31 Artificial Neural Networks/213 How to get the dataset.mp4 12MB
32 Convolutional Neural Networks/238 How to get the dataset.mp4 12MB
34 Principal Component Analysis PCA/251 How to get the dataset.mp4 12MB
35 Linear Discriminant Analysis LDA/258 How to get the dataset.mp4 12MB
36 Kernel PCA/261 How to get the dataset.mp4 12MB
38 Model Selection/265 How to get the dataset.mp4 12MB
39 XGBoost/271 How to get the dataset.mp4 12MB
04 Simple Linear Regression/025 Simple Linear Regression in R - Step 1.mp4 12MB
04 Simple Linear Regression/027 Simple Linear Regression in R - Step 3.mp4 11MB
28 Thompson Sampling/176 Thompson Sampling in Python - Step 2.mp4 11MB
12 Logistic Regression/079 Logistic Regression in Python - Step 2.mp4 11MB
31 Artificial Neural Networks/212 Backpropagation.mp4 11MB
25 Eclat/157 Eclat Intuition.mp4 11MB
04 Simple Linear Regression/019 Simple Linear Regression Intuition - Step 1.mp4 11MB
13 K-Nearest Neighbors K-NN/090 K-Nearest Neighbor Intuition.mp4 10MB
22 Hierarchical Clustering/145 HC in R - Step 4.mp4 10MB
22 Hierarchical Clustering/144 HC in R - Step 3.mp4 10MB
22 Hierarchical Clustering/141 HC in Python - Step 5.mp4 10MB
05 Multiple Linear Regression/037 Multiple Linear Regression in Python - Step 2.mp4 10MB
01 Welcome to the course/001 Applications of Machine Learning.mp4 10MB
10 Evaluating Regression Models Performance/070 R-Squared Intuition.mp4 10MB
31 Artificial Neural Networks/218 ANN in Python - Step 4.mp4 10MB
29 --------------------- Part 7 Natural Language Processing ---------------------/198 Natural Language Processing in R - Step 7.mp4 10MB
28 Thompson Sampling/178 Thompson Sampling in R - Step 2.mp4 10MB
27 Upper Confidence Bound UCB/171 Upper Confidence Bound in R - Step 4.mp4 10MB
06 Polynomial Regression/047 Polynomial Regression Intuition.mp4 9MB
32 Convolutional Neural Networks/246 CNN in Python - Step 8.mp4 9MB
19 Evaluating Classification Models Performance/121 Confusion Matrix.mp4 9MB
22 Hierarchical Clustering/142 HC in R - Step 1.mp4 9MB
29 --------------------- Part 7 Natural Language Processing ---------------------/186 Natural Language Processing in Python - Step 6.mp4 8MB
29 --------------------- Part 7 Natural Language Processing ---------------------/195 Natural Language Processing in R - Step 4.mp4 8MB
12 Logistic Regression/080 Logistic Regression in Python - Step 3.mp4 8MB
32 Convolutional Neural Networks/236 Summary.mp4 8MB
04 Simple Linear Regression/018 Dataset Business Problem Description.mp4 8MB
32 Convolutional Neural Networks/240 CNN in Python - Step 2.mp4 7MB
15 Kernel SVM/098 Kernel SVM Intuition.mp4 6MB
04 Simple Linear Regression/020 Simple Linear Regression Intuition - Step 2.mp4 6MB
32 Convolutional Neural Networks/229 Plan of attack.mp4 6MB
29 --------------------- Part 7 Natural Language Processing ---------------------/196 Natural Language Processing in R - Step 5.mp4 6MB
05 Multiple Linear Regression/034 Multiple Linear Regression Intuition - Step 4.mp4 5MB
31 Artificial Neural Networks/205 Plan of attack.mp4 5MB
19 Evaluating Classification Models Performance/122 Accuracy Paradox.mp4 4MB
29 --------------------- Part 7 Natural Language Processing ---------------------/183 Natural Language Processing in Python - Step 3.mp4 4MB
02 -------------------------- Part 1 Data Preprocessing --------------------------/006 Welcome to Part 1 - Data Preprocessing.mp4 4MB
32 Convolutional Neural Networks/234 Step 3 - Flattening.mp4 3MB
32 Convolutional Neural Networks/241 CNN in Python - Step 3.mp4 3MB
05 Multiple Linear Regression/032 Multiple Linear Regression Intuition - Step 2.mp4 2MB
05 Multiple Linear Regression/031 Multiple Linear Regression Intuition - Step 1.mp4 2MB
25 Eclat/attached_files/159 Eclat in R/Eclat.zip 49KB
14 Support Vector Machine SVM/attached_files/097 SVM in R/SVM.zip 8KB
40 Bonus Lectures/275 YOUR SPECIAL BONUS.html 5KB
05 Multiple Linear Regression/quizzes/003 Multiple Linear Regression.html 5KB
02 -------------------------- Part 1 Data Preprocessing --------------------------/quizzes/001 Data Preprocessing.html 5KB
04 Simple Linear Regression/quizzes/002 Simple Linear Regression.html 4KB
22 Hierarchical Clustering/quizzes/007 Hierarchical Clustering.html 4KB
12 Logistic Regression/quizzes/004 Logistic Regression.html 4KB
21 K-Means Clustering/quizzes/006 K-Means Clustering.html 4KB
13 K-Nearest Neighbors K-NN/quizzes/005 K-Nearest Neighbor.html 4KB
19 Evaluating Classification Models Performance/125 Conclusion of Part 3 - Classification.html 4KB
10 Evaluating Regression Models Performance/074 Conclusion of Part 2 - Regression.html 3KB
32 Convolutional Neural Networks/249 CNN in R.html 3KB
29 --------------------- Part 7 Natural Language Processing ---------------------/179 Welcome to Part 7 - Natural Language Processing.html 2KB
02 -------------------------- Part 1 Data Preprocessing --------------------------/010 For Python learners summary of Object-oriented programming classes objects.html 2KB
29 --------------------- Part 7 Natural Language Processing ---------------------/202 Homework Challenge.html 2KB
29 --------------------- Part 7 Natural Language Processing ---------------------/191 Homework Challenge.html 2KB
33 ----------------------- Part 9 Dimensionality Reduction -----------------------/250 Welcome to Part 9 - Dimensionality Reduction.html 2KB
01 Welcome to the course/005 BONUS Meet your instructors.html 1KB
37 --------------------- Part 10 Model Selection Boosting ---------------------/264 Welcome to Part 10 - Model Selection Boosting.html 1KB
30 ---------------------------- Part 8 Deep Learning ----------------------------/203 Welcome to Part 8 - Deep Learning.html 1KB
03 ------------------------------ Part 2 Regression ------------------------------/016 Welcome to Part 2 - Regression.html 1KB
26 ------------------------ Part 6 Reinforcement Learning ------------------------/160 Welcome to Part 6 - Reinforcement Learning.html 1KB
11 ---------------------------- Part 3 Classification ----------------------------/075 Welcome to Part 3 - Classification.html 1KB
20 ---------------------------- Part 4 Clustering ----------------------------/126 Welcome to Part 4 - Clustering.html 1004B
22 Hierarchical Clustering/147 Conclusion of Part 4 - Clustering.html 809B
23 ---------------------- Part 5 Association Rule Learning ----------------------/148 Welcome to Part 5 - Association Rule Learning.html 713B
Freetutorials.us.url 119B
[FreeTutorials.us].txt 75B