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Name:	[UdemyCourseDownloader] Introduction to Machine Learning & Deep Learning in Python
Usenet:	Downloads Anonymously to Unlimited data Access! Get Usenet Free Trial
Hash:	E5CD7A86473F94416CFBD436C50A552335331427
Category:	Other
Peers:	Seeds: 3 & Leechers: 2
Size:	1.83 GB
Stream:	Watch Full HD Movies @ LimeMovies
Date:	10 July 2019

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Torrent Files Size: 1.83 GB
[UdemyCourseDownloader] Introduction to Machine Learning & Deep Learning in Python 17. Convolutional Neural Networks 8. Convolutional neural networks - illustration.vtt - 70.54 MB udemycoursedownloader.com.url - 132 bytes 01. Introduction 1. Introduction.mp4 - 3.48 MB 1. Introduction.vtt - 2.41 KB 2. Introduction to machine learning.mp4 - 8.05 MB 2. Introduction to machine learning.vtt - 6.29 KB 02. Installations 1. Installing Anaconda.mp4 - 4.32 MB 1. Installing Anaconda.vtt - 2.28 KB 2. Installing Spyder.mp4 - 2.8 MB 2. Installing Spyder.vtt - 1.83 KB 3. Installing Keras and TensorFlow.mp4 - 5.95 MB 3. Installing Keras and TensorFlow.vtt - 65.3 MB 03. Linear Regression 1. Linear regression introduction.mp4 - 26.43 MB 1. Linear regression introduction.vtt - 9.38 KB 2. Linear regression theory - optimization.mp4 - 42.28 MB 2. Linear regression theory - optimization.vtt - 8.22 KB 3. Linear regression theory - gradient descent.mp4 - 11.1 MB 3. Linear regression theory - gradient descent.vtt - 7.86 KB 4. Linear regression implementation I.mp4 - 16.69 MB 4. Linear regression implementation I.vtt - 7.45 KB 5. Linear regression implementation II.mp4 - 8.78 MB 5. Linear regression implementation II.vtt - 5.4 KB 04. Logistic Regression 1. Logistic regression introduction.mp4 - 17.63 MB 1. Logistic regression introduction.vtt - 13.76 KB 2. Logistic regression introduction II.mp4 - 6.67 MB 2. Logistic regression introduction II.vtt - 4.38 KB 3. Logistic regression example I - sigmoid function.mp4 - 13.04 MB 3. Logistic regression example I - sigmoid function.vtt - 7.99 KB 4. Logistic regression example II- credit scoring.mp4 - 21.33 MB 4. Logistic regression example II- credit scoring.vtt - 8.19 KB 5. Logistic regression example III - credit scoring.mp4 - 10.87 MB 5. Logistic regression example III - credit scoring.vtt - 6.38 KB 6. Cross validation introduction.mp4 - 11.72 MB 6. Cross validation introduction.vtt - 6.02 KB 7. Cross validation example.mp4 - 4.15 MB 7. Cross validation example.vtt - 2.6 KB 05. K-Nearest Neighbor Classifier 1. K-nearest neighbor introduction.mp4 - 9.48 MB 1. K-nearest neighbor introduction.vtt - 6.46 KB 2. K-nearest neighbor introduction - lazy learning.mp4 - 8.11 MB 2. K-nearest neighbor introduction - lazy learning.vtt - 4.68 KB 3. K-nearest neighbor introduction - Euclidean-distance.mp4 - 8.61 MB 3. K-nearest neighbor introduction - Euclidean-distance.vtt - 6.29 KB 4. UPDATE bias and variance.html - 333 bytes 5. K-nearest neighbor implementation I.mp4 - 6.95 MB 5. K-nearest neighbor implementation I.vtt - 3.3 KB 6. K-nearest neighbor implementation II.mp4 - 9.96 MB 6. K-nearest neighbor implementation II.vtt - 6.61 KB 7. K-nearest neighbor implementation III.mp4 - 7.93 MB 7. K-nearest neighbor implementation III.vtt - 4.54 KB 06. Naive Bayes Classifier 1. Naive Bayes classifier introduction I.mp4 - 17.44 MB 1. Naive Bayes classifier introduction I.vtt - 9.45 KB 2. Naive Bayes classifier introduction II - illustration.mp4 - 8.43 MB 2. Naive Bayes classifier introduction II - illustration.vtt - 4.83 KB 3. Naive Bayes classifier implementation.mp4 - 8 MB 3. Naive Bayes classifier implementation.vtt - 5.04 KB 4. ----- TEXT CLASSIFICATION -----.html - 193 bytes 5. Text clustering - basics.mp4 - 22.12 MB 5. Text clustering - basics.vtt - 9.5 KB 6. Text clustering - inverse document frequency (TF-IDF).mp4 - 10.02 MB 6. Text clustering - inverse document frequency (TF-IDF).vtt - 5.18 KB 7. Naive Bayes example - clustering news.mp4 - 23.33 MB 7. Naive Bayes example - clustering news.vtt - 10.45 KB 07. Support Vector Machine (SVM) 1. Support vector machine introduction I - linear case.mp4 - 20.76 MB 1. Support vector machine introduction I - linear case.vtt - 9.88 KB 2. Support vector machine introduction II - non-linear case.mp4 - 17.22 MB 2. Support vector machine introduction II - non-linear case.vtt - 8.09 KB 3. Support vector machine introduction III - kernels.mp4 - 9.9 MB 3. Support vector machine introduction III - kernels.vtt - 4.95 KB 4. Support vector machine example I - simple.mp4 - 10.48 MB 4. Support vector machine example I - simple.vtt - 4.51 KB 5. Support vector machine example II - iris dataset.mp4 - 21.7 MB 5. Support vector machine example II - iris dataset.vtt - 8.48 KB 6. Support vector machine example III - digit recognition.mp4 - 16.43 MB 6. Support vector machine example III - digit recognition.vtt - 7.43 KB 08. Decision Trees 1. Decision trees introduction - basics.mp4 - 11.73 MB 1. Decision trees introduction - basics.vtt - 8.83 KB 2. Decision trees introduction - entropy.mp4 - 19.29 MB 2. Decision trees introduction - entropy.vtt - 9.84 KB 3. Decision trees introduction - information gain.mp4 - 46.96 MB 3. Decision trees introduction - information gain.vtt - 8.77 KB 4. Decision trees introduction - pros and cons.mp4 - 4.19 MB 4. Decision trees introduction - pros and cons.vtt - 2.88 KB 5. Decision trees implementation.mp4 - 13.6 MB 5. Decision trees implementation.vtt - 8.43 KB 6. Decision trees implementation II.mp4 - 6.66 MB 6. Decision trees implementation II.vtt - 6.66 MB 7. The Gini-index approach.mp4 - 18.75 MB 7. The Gini-index approach.vtt - 10.09 KB 09. Random Forest Classifier 1. Pruning introduction.mp4 - 9.83 MB 1. Pruning introduction.vtt - 7.4 KB 2. Bagging introduction.mp4 - 11.72 MB 2. Bagging introduction.vtt - 9.1 KB 3. Random forest classifier introduction.mp4 - 8.72 MB 3. Random forest classifier introduction.vtt - 6.33 KB 4. Random forests example I - iris dataset.mp4 - 11.36 MB 4. Random forests example I - iris dataset.vtt - 5.22 KB 5. Random forests example II - credit scoring.mp4 - 4.21 MB 5. Random forests example II - credit scoring.vtt - 1.95 KB 6. Random forests example III - parameter tuning.mp4 - 9.19 MB 6. Random forests example III - parameter tuning.vtt - 5.09 KB 10. Boosting 1. Boosting introduction - basics.mp4 - 8.39 MB 1. Boosting introduction - basics.vtt - 4.95 KB 2. Boosting introduction - illustration.mp4 - 8.17 MB 2. Boosting introduction - illustration.vtt - 6.27 KB 3. Boosting introduction - equations.mp4 - 13.71 MB 3. Boosting introduction - equations.vtt - 7.76 KB 4. Boosting introduction - final formula.mp4 - 13.01 MB 4. Boosting introduction - final formula.vtt - 9.02 KB 5. Boosting implementation I - iris dataset.mp4 - 12.33 MB 5. Boosting implementation I - iris dataset.vtt - 6.28 KB 6. Boosting implementation II -tuning.mp4 - 10.35 MB 6. Boosting implementation II -tuning.vtt - 5.19 KB 7. Boosting vs. bagging.mp4 - 5.21 MB 7. Boosting vs. bagging.vtt - 3.51 KB 11. Clustering 1. Principal component anlysis introduction.mp4 - 8.58 MB 1. Principal component anlysis introduction.vtt - 4.2 KB 2. Principal component analysis example.mp4 - 14 MB 2. Principal component analysis example.vtt - 6.46 KB 3. K-means clustering introduction I.mp4 - 13.67 MB 3. K-means clustering introduction I.vtt - 6.9 KB 4. K-means clustering introduction II.mp4 - 9.47 MB 4. K-means clustering introduction II.vtt - 4.53 KB 5. K-means clustering example.mp4 - 9.43 MB 5. K-means clustering example.vtt - 5.44 KB 6. K-means clustering - text clustering.mp4 - 18.86 MB 6. K-means clustering - text clustering.vtt - 7.73 KB 7. DBSCAN introduction.mp4 - 11.05 MB 7. DBSCAN introduction.vtt - 5.41 KB 8. DBSCAN example.mp4 - 7.88 MB 8. DBSCAN example.vtt - 5.02 KB 9. Hierarchical clustering introduction.mp4 - 13.66 MB 9. Hierarchical clustering introduction.vtt - 7 KB 10. Hierarchical clustering example.mp4 - 11.96 MB 10. Hierarchical clustering example.vtt - 6.19 KB 12. Neural Networks 1. ---- NEURAL NETWORKS INTRODUCTION ----.html - 35 bytes 2. Axons and neurons in the human brain.mp4 - 19.24 MB 2. Axons and neurons in the human brain.vtt - 9.37 KB 3. Modeling human brain.mp4 - 16.17 MB 3. Modeling human brain.vtt - 8.31 KB 4. Learning paradigms.mp4 - 6.51 MB 4. Learning paradigms.vtt - 3 KB 5. Artificial neurons - the model.mp4 - 16.55 MB 5. Artificial neurons - the model.vtt - 7.41 KB 6. Artificial neurons - activation functions.mp4 - 14.24 MB 6. Artificial neurons - activation functions.vtt - 6.55 KB 7. Artificial neurons - an example.mp4 - 11.37 MB 7. Artificial neurons - an example.vtt - 4.81 KB 8. Neural networks - the big picture.mp4 - 10.78 MB 8. Neural networks - the big picture.vtt - 4.83 KB 9. Applications of neural networks.mp4 - 5.23 MB

Torrent Files

Size: 1.83 GB

[UdemyCourseDownloader] Introduction to Machine Learning & Deep Learning in Python

17. Convolutional Neural Networks

8. Convolutional neural networks - illustration.vtt -

70.54 MB

udemycoursedownloader.com.url -

132 bytes

01. Introduction

1. Introduction.mp4 -

3.48 MB

1. Introduction.vtt -

2.41 KB

2. Introduction to machine learning.mp4 -

8.05 MB

2. Introduction to machine learning.vtt -

6.29 KB

02. Installations

1. Installing Anaconda.mp4 -

4.32 MB

1. Installing Anaconda.vtt -

2.28 KB

2. Installing Spyder.mp4 -

2.8 MB

2. Installing Spyder.vtt -

1.83 KB

3. Installing Keras and TensorFlow.mp4 -

5.95 MB

3. Installing Keras and TensorFlow.vtt -

65.3 MB

03. Linear Regression

1. Linear regression introduction.mp4 -

26.43 MB

1. Linear regression introduction.vtt -

9.38 KB

2. Linear regression theory - optimization.mp4 -

42.28 MB

2. Linear regression theory - optimization.vtt -

8.22 KB

3. Linear regression theory - gradient descent.mp4 -

11.1 MB

3. Linear regression theory - gradient descent.vtt -

7.86 KB

4. Linear regression implementation I.mp4 -

16.69 MB

4. Linear regression implementation I.vtt -

7.45 KB

5. Linear regression implementation II.mp4 -

8.78 MB

5. Linear regression implementation II.vtt -

5.4 KB

04. Logistic Regression

1. Logistic regression introduction.mp4 -

17.63 MB

1. Logistic regression introduction.vtt -

13.76 KB

2. Logistic regression introduction II.mp4 -

6.67 MB

2. Logistic regression introduction II.vtt -

4.38 KB

3. Logistic regression example I - sigmoid function.mp4 -

13.04 MB

3. Logistic regression example I - sigmoid function.vtt -

7.99 KB

4. Logistic regression example II- credit scoring.mp4 -

21.33 MB

4. Logistic regression example II- credit scoring.vtt -

8.19 KB

5. Logistic regression example III - credit scoring.mp4 -

10.87 MB

5. Logistic regression example III - credit scoring.vtt -

6.38 KB

6. Cross validation introduction.mp4 -

11.72 MB

6. Cross validation introduction.vtt -

6.02 KB

7. Cross validation example.mp4 -

4.15 MB

7. Cross validation example.vtt -

2.6 KB

05. K-Nearest Neighbor Classifier

1. K-nearest neighbor introduction.mp4 -

9.48 MB

1. K-nearest neighbor introduction.vtt -

6.46 KB

2. K-nearest neighbor introduction - lazy learning.mp4 -

8.11 MB

2. K-nearest neighbor introduction - lazy learning.vtt -

4.68 KB

3. K-nearest neighbor introduction - Euclidean-distance.mp4 -

8.61 MB

3. K-nearest neighbor introduction - Euclidean-distance.vtt -

6.29 KB

4. UPDATE bias and variance.html -

333 bytes

5. K-nearest neighbor implementation I.mp4 -

6.95 MB

5. K-nearest neighbor implementation I.vtt -

3.3 KB

6. K-nearest neighbor implementation II.mp4 -

9.96 MB

6. K-nearest neighbor implementation II.vtt -

6.61 KB

7. K-nearest neighbor implementation III.mp4 -

7.93 MB

7. K-nearest neighbor implementation III.vtt -

4.54 KB

06. Naive Bayes Classifier

1. Naive Bayes classifier introduction I.mp4 -

17.44 MB

1. Naive Bayes classifier introduction I.vtt -

9.45 KB

2. Naive Bayes classifier introduction II - illustration.mp4 -

8.43 MB

2. Naive Bayes classifier introduction II - illustration.vtt -

4.83 KB

3. Naive Bayes classifier implementation.mp4 -

8 MB

3. Naive Bayes classifier implementation.vtt -

5.04 KB

4. ----- TEXT CLASSIFICATION -----.html -

193 bytes

5. Text clustering - basics.mp4 -

22.12 MB

5. Text clustering - basics.vtt -

9.5 KB

6. Text clustering - inverse document frequency (TF-IDF).mp4 -

10.02 MB

6. Text clustering - inverse document frequency (TF-IDF).vtt -

5.18 KB

7. Naive Bayes example - clustering news.mp4 -

23.33 MB

7. Naive Bayes example - clustering news.vtt -

10.45 KB

07. Support Vector Machine (SVM)

1. Support vector machine introduction I - linear case.mp4 -

20.76 MB

1. Support vector machine introduction I - linear case.vtt -

9.88 KB

2. Support vector machine introduction II - non-linear case.mp4 -

17.22 MB

2. Support vector machine introduction II - non-linear case.vtt -

8.09 KB

3. Support vector machine introduction III - kernels.mp4 -

9.9 MB

3. Support vector machine introduction III - kernels.vtt -

4.95 KB

4. Support vector machine example I - simple.mp4 -

10.48 MB

4. Support vector machine example I - simple.vtt -

4.51 KB

5. Support vector machine example II - iris dataset.mp4 -

21.7 MB

5. Support vector machine example II - iris dataset.vtt -

8.48 KB

6. Support vector machine example III - digit recognition.mp4 -

16.43 MB

6. Support vector machine example III - digit recognition.vtt -

7.43 KB

08. Decision Trees

1. Decision trees introduction - basics.mp4 -

11.73 MB

1. Decision trees introduction - basics.vtt -

8.83 KB

2. Decision trees introduction - entropy.mp4 -

19.29 MB

2. Decision trees introduction - entropy.vtt -

9.84 KB

3. Decision trees introduction - information gain.mp4 -

46.96 MB

3. Decision trees introduction - information gain.vtt -

8.77 KB

4. Decision trees introduction - pros and cons.mp4 -

4.19 MB

4. Decision trees introduction - pros and cons.vtt -

2.88 KB

5. Decision trees implementation.mp4 -

13.6 MB

5. Decision trees implementation.vtt -

8.43 KB

6. Decision trees implementation II.mp4 -

6.66 MB

6. Decision trees implementation II.vtt -

6.66 MB

7. The Gini-index approach.mp4 -

18.75 MB

7. The Gini-index approach.vtt -

10.09 KB

09. Random Forest Classifier

1. Pruning introduction.mp4 -

9.83 MB

1. Pruning introduction.vtt -

7.4 KB

2. Bagging introduction.mp4 -

11.72 MB

2. Bagging introduction.vtt -

9.1 KB

3. Random forest classifier introduction.mp4 -

8.72 MB

3. Random forest classifier introduction.vtt -

6.33 KB

4. Random forests example I - iris dataset.mp4 -

11.36 MB

4. Random forests example I - iris dataset.vtt -

5.22 KB

5. Random forests example II - credit scoring.mp4 -

4.21 MB

5. Random forests example II - credit scoring.vtt -

1.95 KB

6. Random forests example III - parameter tuning.mp4 -

9.19 MB

6. Random forests example III - parameter tuning.vtt -

5.09 KB

10. Boosting

1. Boosting introduction - basics.mp4 -

8.39 MB

1. Boosting introduction - basics.vtt -

4.95 KB

2. Boosting introduction - illustration.mp4 -

8.17 MB

2. Boosting introduction - illustration.vtt -

6.27 KB

3. Boosting introduction - equations.mp4 -

13.71 MB

3. Boosting introduction - equations.vtt -

7.76 KB

4. Boosting introduction - final formula.mp4 -

13.01 MB

4. Boosting introduction - final formula.vtt -

9.02 KB

5. Boosting implementation I - iris dataset.mp4 -

12.33 MB

5. Boosting implementation I - iris dataset.vtt -

6.28 KB

6. Boosting implementation II -tuning.mp4 -

10.35 MB

6. Boosting implementation II -tuning.vtt -

5.19 KB

7. Boosting vs. bagging.mp4 -

5.21 MB

7. Boosting vs. bagging.vtt -

3.51 KB

11. Clustering

1. Principal component anlysis introduction.mp4 -

8.58 MB

1. Principal component anlysis introduction.vtt -

4.2 KB

2. Principal component analysis example.mp4 -

14 MB

2. Principal component analysis example.vtt -

6.46 KB

3. K-means clustering introduction I.mp4 -

13.67 MB

3. K-means clustering introduction I.vtt -

6.9 KB

4. K-means clustering introduction II.mp4 -

9.47 MB

4. K-means clustering introduction II.vtt -

4.53 KB

5. K-means clustering example.mp4 -

9.43 MB

5. K-means clustering example.vtt -

5.44 KB

6. K-means clustering - text clustering.mp4 -

18.86 MB

6. K-means clustering - text clustering.vtt -

7.73 KB

7. DBSCAN introduction.mp4 -

11.05 MB

7. DBSCAN introduction.vtt -

5.41 KB

8. DBSCAN example.mp4 -

7.88 MB

8. DBSCAN example.vtt -

5.02 KB

9. Hierarchical clustering introduction.mp4 -

13.66 MB

9. Hierarchical clustering introduction.vtt -

7 KB

10. Hierarchical clustering example.mp4 -

11.96 MB

10. Hierarchical clustering example.vtt -

6.19 KB

12. Neural Networks

1. ---- NEURAL NETWORKS INTRODUCTION ----.html -

35 bytes

2. Axons and neurons in the human brain.mp4 -

19.24 MB

2. Axons and neurons in the human brain.vtt -

9.37 KB

3. Modeling human brain.mp4 -

16.17 MB

3. Modeling human brain.vtt -

8.31 KB

4. Learning paradigms.mp4 -

6.51 MB

4. Learning paradigms.vtt -

3 KB

5. Artificial neurons - the model.mp4 -

16.55 MB

5. Artificial neurons - the model.vtt -

7.41 KB

6. Artificial neurons - activation functions.mp4 -

14.24 MB

6. Artificial neurons - activation functions.vtt -

6.55 KB

7. Artificial neurons - an example.mp4 -

11.37 MB

7. Artificial neurons - an example.vtt -

4.81 KB

8. Neural networks - the big picture.mp4 -

10.78 MB

8. Neural networks - the big picture.vtt -

4.83 KB

9. Applications of neural networks.mp4 -

5.23 MB

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