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

Magnet Download Download Torrent
File Name
Size
17. Convolutional Neural Networks/8. Convolutional neural networks - illustration.vtt
70 MB
udemycoursedownloader.com.url
132 B
01. Introduction/1. Introduction.mp4
3.5 MB
01. Introduction/1. Introduction.vtt
2.4 kB
01. Introduction/2. Introduction to machine learning.mp4
8.1 MB
01. Introduction/2. Introduction to machine learning.vtt
6.3 kB
02. Installations/1. Installing Anaconda.mp4
4.3 MB
02. Installations/1. Installing Anaconda.vtt
2.3 kB
02. Installations/2. Installing Spyder.mp4
2.8 MB
02. Installations/2. Installing Spyder.vtt
1.8 kB
02. Installations/3. Installing Keras and TensorFlow.mp4
6.0 MB
02. Installations/3. Installing Keras and TensorFlow.vtt
65 MB
03. Linear Regression/1. Linear regression introduction.mp4
26 MB
03. Linear Regression/1. Linear regression introduction.vtt
9.4 kB
03. Linear Regression/2. Linear regression theory - optimization.mp4
42 MB
03. Linear Regression/2. Linear regression theory - optimization.vtt
8.2 kB
03. Linear Regression/3. Linear regression theory - gradient descent.mp4
11 MB
03. Linear Regression/3. Linear regression theory - gradient descent.vtt
7.9 kB
03. Linear Regression/4. Linear regression implementation I.mp4
17 MB
03. Linear Regression/4. Linear regression implementation I.vtt
7.4 kB
03. Linear Regression/5. Linear regression implementation II.mp4
8.8 MB
03. Linear Regression/5. Linear regression implementation II.vtt
5.4 kB
04. Logistic Regression/1. Logistic regression introduction.mp4
18 MB
04. Logistic Regression/1. Logistic regression introduction.vtt
14 kB
04. Logistic Regression/2. Logistic regression introduction II.mp4
6.7 MB
04. Logistic Regression/2. Logistic regression introduction II.vtt
4.4 kB
04. Logistic Regression/3. Logistic regression example I - sigmoid function.mp4
13 MB
04. Logistic Regression/3. Logistic regression example I - sigmoid function.vtt
8.0 kB
04. Logistic Regression/4. Logistic regression example II- credit scoring.mp4
21 MB
04. Logistic Regression/4. Logistic regression example II- credit scoring.vtt
8.2 kB
04. Logistic Regression/5. Logistic regression example III - credit scoring.mp4
11 MB
04. Logistic Regression/5. Logistic regression example III - credit scoring.vtt
6.4 kB
04. Logistic Regression/6. Cross validation introduction.mp4
12 MB
04. Logistic Regression/6. Cross validation introduction.vtt
6.0 kB
04. Logistic Regression/7. Cross validation example.mp4
4.2 MB
04. Logistic Regression/7. Cross validation example.vtt
2.6 kB
05. K-Nearest Neighbor Classifier/1. K-nearest neighbor introduction.mp4
9.5 MB
05. K-Nearest Neighbor Classifier/1. K-nearest neighbor introduction.vtt
6.5 kB
05. K-Nearest Neighbor Classifier/2. K-nearest neighbor introduction - lazy learning.mp4
8.1 MB
05. K-Nearest Neighbor Classifier/2. K-nearest neighbor introduction - lazy learning.vtt
4.7 kB
05. K-Nearest Neighbor Classifier/3. K-nearest neighbor introduction - Euclidean-distance.mp4
8.6 MB
05. K-Nearest Neighbor Classifier/3. K-nearest neighbor introduction - Euclidean-distance.vtt
6.3 kB
05. K-Nearest Neighbor Classifier/4. UPDATE bias and variance.html
333 B
05. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.mp4
6.9 MB
05. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.vtt
3.3 kB
05. K-Nearest Neighbor Classifier/6. K-nearest neighbor implementation II.mp4
10 MB
05. K-Nearest Neighbor Classifier/6. K-nearest neighbor implementation II.vtt
6.6 kB
05. K-Nearest Neighbor Classifier/7. K-nearest neighbor implementation III.mp4
7.9 MB
05. K-Nearest Neighbor Classifier/7. K-nearest neighbor implementation III.vtt
4.5 kB
06. Naive Bayes Classifier/1. Naive Bayes classifier introduction I.mp4
17 MB
06. Naive Bayes Classifier/1. Naive Bayes classifier introduction I.vtt
9.5 kB
06. Naive Bayes Classifier/2. Naive Bayes classifier introduction II - illustration.mp4
8.4 MB
06. Naive Bayes Classifier/2. Naive Bayes classifier introduction II - illustration.vtt
4.8 kB
06. Naive Bayes Classifier/3. Naive Bayes classifier implementation.mp4
8.0 MB
06. Naive Bayes Classifier/3. Naive Bayes classifier implementation.vtt
5.0 kB
06. Naive Bayes Classifier/4. ----- TEXT CLASSIFICATION -----.html
193 B
06. Naive Bayes Classifier/5. Text clustering - basics.mp4
22 MB
06. Naive Bayes Classifier/5. Text clustering - basics.vtt
9.5 kB
06. Naive Bayes Classifier/6. Text clustering - inverse document frequency (TF-IDF).mp4
10 MB
06. Naive Bayes Classifier/6. Text clustering - inverse document frequency (TF-IDF).vtt
5.2 kB
06. Naive Bayes Classifier/7. Naive Bayes example - clustering news.mp4
23 MB
06. Naive Bayes Classifier/7. Naive Bayes example - clustering news.vtt
10 kB
07. Support Vector Machine (SVM)/1. Support vector machine introduction I - linear case.mp4
21 MB
07. Support Vector Machine (SVM)/1. Support vector machine introduction I - linear case.vtt
9.9 kB
07. Support Vector Machine (SVM)/2. Support vector machine introduction II - non-linear case.mp4
17 MB
07. Support Vector Machine (SVM)/2. Support vector machine introduction II - non-linear case.vtt
8.1 kB
07. Support Vector Machine (SVM)/3. Support vector machine introduction III - kernels.mp4
9.9 MB
07. Support Vector Machine (SVM)/3. Support vector machine introduction III - kernels.vtt
5.0 kB
07. Support Vector Machine (SVM)/4. Support vector machine example I - simple.mp4
10 MB
07. Support Vector Machine (SVM)/4. Support vector machine example I - simple.vtt
4.5 kB
07. Support Vector Machine (SVM)/5. Support vector machine example II - iris dataset.mp4
22 MB
07. Support Vector Machine (SVM)/5. Support vector machine example II - iris dataset.vtt
8.5 kB
07. Support Vector Machine (SVM)/6. Support vector machine example III - digit recognition.mp4
16 MB
07. Support Vector Machine (SVM)/6. Support vector machine example III - digit recognition.vtt
7.4 kB
08. Decision Trees/1. Decision trees introduction - basics.mp4
12 MB
08. Decision Trees/1. Decision trees introduction - basics.vtt
8.8 kB
08. Decision Trees/2. Decision trees introduction - entropy.mp4
19 MB
08. Decision Trees/2. Decision trees introduction - entropy.vtt
9.8 kB
08. Decision Trees/3. Decision trees introduction - information gain.mp4
47 MB
08. Decision Trees/3. Decision trees introduction - information gain.vtt
8.8 kB
08. Decision Trees/4. Decision trees introduction - pros and cons.mp4
4.2 MB
08. Decision Trees/4. Decision trees introduction - pros and cons.vtt
2.9 kB
08. Decision Trees/5. Decision trees implementation.mp4
14 MB
08. Decision Trees/5. Decision trees implementation.vtt
8.4 kB
08. Decision Trees/6. Decision trees implementation II.mp4
6.7 MB
08. Decision Trees/6. Decision trees implementation II.vtt
6.7 MB
08. Decision Trees/7. The Gini-index approach.mp4
19 MB
08. Decision Trees/7. The Gini-index approach.vtt
10 kB
09. Random Forest Classifier/1. Pruning introduction.mp4
9.8 MB
09. Random Forest Classifier/1. Pruning introduction.vtt
7.4 kB
09. Random Forest Classifier/2. Bagging introduction.mp4
12 MB
09. Random Forest Classifier/2. Bagging introduction.vtt
9.1 kB
09. Random Forest Classifier/3. Random forest classifier introduction.mp4
8.7 MB
09. Random Forest Classifier/3. Random forest classifier introduction.vtt
6.3 kB
09. Random Forest Classifier/4. Random forests example I - iris dataset.mp4
11 MB
09. Random Forest Classifier/4. Random forests example I - iris dataset.vtt
5.2 kB
09. Random Forest Classifier/5. Random forests example II - credit scoring.mp4
4.2 MB
09. Random Forest Classifier/5. Random forests example II - credit scoring.vtt
1.9 kB
09. Random Forest Classifier/6. Random forests example III - parameter tuning.mp4
9.2 MB
09. Random Forest Classifier/6. Random forests example III - parameter tuning.vtt
5.1 kB
10. Boosting/1. Boosting introduction - basics.mp4
8.4 MB
10. Boosting/1. Boosting introduction - basics.vtt
4.9 kB
10. Boosting/2. Boosting introduction - illustration.mp4
8.2 MB
10. Boosting/2. Boosting introduction - illustration.vtt
6.3 kB
10. Boosting/3. Boosting introduction - equations.mp4
14 MB
10. Boosting/3. Boosting introduction - equations.vtt
7.8 kB
10. Boosting/4. Boosting introduction - final formula.mp4
13 MB
10. Boosting/4. Boosting introduction - final formula.vtt
9.0 kB
10. Boosting/5. Boosting implementation I - iris dataset.mp4
12 MB
10. Boosting/5. Boosting implementation I - iris dataset.vtt
6.3 kB
10. Boosting/6. Boosting implementation II -tuning.mp4
10 MB
10. Boosting/6. Boosting implementation II -tuning.vtt
5.2 kB
10. Boosting/7. Boosting vs. bagging.mp4
5.2 MB
10. Boosting/7. Boosting vs. bagging.vtt
3.5 kB
11. Clustering/1. Principal component anlysis introduction.mp4
8.6 MB
11. Clustering/1. Principal component anlysis introduction.vtt
4.2 kB
11. Clustering/2. Principal component analysis example.mp4
14 MB
11. Clustering/2. Principal component analysis example.vtt
6.5 kB
11. Clustering/3. K-means clustering introduction I.mp4
14 MB
11. Clustering/3. K-means clustering introduction I.vtt
6.9 kB
11. Clustering/4. K-means clustering introduction II.mp4
9.5 MB
11. Clustering/4. K-means clustering introduction II.vtt
4.5 kB
11. Clustering/5. K-means clustering example.mp4
9.4 MB
11. Clustering/5. K-means clustering example.vtt
5.4 kB
11. Clustering/6. K-means clustering - text clustering.mp4
19 MB
11. Clustering/6. K-means clustering - text clustering.vtt
7.7 kB
11. Clustering/7. DBSCAN introduction.mp4
11 MB
11. Clustering/7. DBSCAN introduction.vtt
5.4 kB
11. Clustering/8. DBSCAN example.mp4
7.9 MB
11. Clustering/8. DBSCAN example.vtt
5.0 kB
11. Clustering/9. Hierarchical clustering introduction.mp4
14 MB
11. Clustering/9. Hierarchical clustering introduction.vtt
7.0 kB
11. Clustering/10. Hierarchical clustering example.mp4
12 MB
11. Clustering/10. Hierarchical clustering example.vtt
6.2 kB
12. Neural Networks/1. ---- NEURAL NETWORKS INTRODUCTION ----.html
35 B
12. Neural Networks/2. Axons and neurons in the human brain.mp4
19 MB
12. Neural Networks/2. Axons and neurons in the human brain.vtt
9.4 kB
12. Neural Networks/3. Modeling human brain.mp4
16 MB
12. Neural Networks/3. Modeling human brain.vtt
8.3 kB
12. Neural Networks/4. Learning paradigms.mp4
6.5 MB
12. Neural Networks/4. Learning paradigms.vtt
3.0 kB
12. Neural Networks/5. Artificial neurons - the model.mp4
16 MB
12. Neural Networks/5. Artificial neurons - the model.vtt
7.4 kB
12. Neural Networks/6. Artificial neurons - activation functions.mp4
14 MB
12. Neural Networks/6. Artificial neurons - activation functions.vtt
6.6 kB
12. Neural Networks/7. Artificial neurons - an example.mp4
11 MB
12. Neural Networks/7. Artificial neurons - an example.vtt
4.8 kB
12. Neural Networks/8. Neural networks - the big picture.mp4
11 MB
12. Neural Networks/8. Neural networks - the big picture.vtt
4.8 kB
12. Neural Networks/9. Applications of neural networks.mp4
5.2 MB
12. Neural Networks/9. Applications of neural networks.vtt
2.4 kB
12. Neural Networks/10. ---- BACKPROPAGATION ----.html
42 B
12. Neural Networks/11. Feedforward neural networks.mp4
18 MB
12. Neural Networks/11. Feedforward neural networks.vtt
8.9 kB
12. Neural Networks/12. Optimization - cost function.mp4
26 MB
12. Neural Networks/12. Optimization - cost function.vtt
12 kB
12. Neural Networks/13. Simplified feedforward network.mp4
19 MB
12. Neural Networks/13. Simplified feedforward network.vtt
9.0 kB
12. Neural Networks/14. Feedforward neural network topology.mp4
15 MB
12. Neural Networks/14. Feedforward neural network topology.vtt
6.6 kB
12. Neural Networks/15. The learning algorithm.mp4
13 MB
12. Neural Networks/15. The learning algorithm.vtt
6.0 kB
12. Neural Networks/16. Error calculation.mp4
14 MB
12. Neural Networks/16. Error calculation.vtt
6.5 kB
12. Neural Networks/17. Gradient calculation I - output layer.mp4
20 MB
12. Neural Networks/17. Gradient calculation I - output layer.vtt
9.3 kB
12. Neural Networks/18. Gradient calculation II - hidden layer.mp4
9.2 MB
12. Neural Networks/18. Gradient calculation II - hidden layer.vtt
4.1 kB
12. Neural Networks/19. Backpropagation.mp4
13 MB
12. Neural Networks/19. Backpropagation.vtt
5.7 kB
12. Neural Networks/20. Backpropagation II.mp4
4.7 MB
12. Neural Networks/20. Backpropagation II.vtt
2.0 kB
12. Neural Networks/21. Applications of neural networks I - character recognition.mp4
8.8 MB
12. Neural Networks/21. Applications of neural networks I - character recognition.vtt
4.4 kB
12. Neural Networks/22. Applications of neural networks II - stock market forecast.mp4
9.5 MB
12. Neural Networks/22. Applications of neural networks II - stock market forecast.vtt
4.7 kB
12. Neural Networks/23. Deep learning.mp4
9.5 MB
12. Neural Networks/23. Deep learning.vtt
4.6 kB
12. Neural Networks/24. ----- IMPLEMENTATION -----.html
53 B
12. Neural Networks/25. Building networks.mp4
13 MB
12. Neural Networks/25. Building networks.vtt
6.5 kB
12. Neural Networks/26. Building networks II.mp4
12 MB
12. Neural Networks/26. Building networks II.vtt
5.9 kB
12. Neural Networks/27. Handling datasets.mp4
6.2 MB
12. Neural Networks/27. Handling datasets.vtt
3.1 kB
12. Neural Networks/28. Neural network example I - XOR problem.mp4
18 MB
12. Neural Networks/28. Neural network example I - XOR problem.vtt
7.8 kB
12. Neural Networks/29. Neural network example II - iris dataset.mp4
36 MB
12. Neural Networks/29. Neural network example II - iris dataset.vtt
8.1 kB
13. Machine Learning in Finance/1. Stock market basics.mp4
5.6 MB
13. Machine Learning in Finance/1. Stock market basics.vtt
3.5 kB
13. Machine Learning in Finance/2. Fetching data from Yahoo Finance.mp4
8.0 MB
13. Machine Learning in Finance/2. Fetching data from Yahoo Finance.vtt
4.3 kB
13. Machine Learning in Finance/3. Predicting stock prices logistic regression.mp4
11 MB
13. Machine Learning in Finance/3. Predicting stock prices logistic regression.vtt
4.3 kB
13. Machine Learning in Finance/4. Predicting stock prices k-nearest neighbor.mp4
7.1 MB
13. Machine Learning in Finance/4. Predicting stock prices k-nearest neighbor.vtt
3.3 kB
13. Machine Learning in Finance/5. Predicting stock prices support vector machine.mp4
8.7 MB
13. Machine Learning in Finance/5. Predicting stock prices support vector machine.vtt
3.6 kB
13. Machine Learning in Finance/6. Predicting stock prices - conclusion.mp4
3.5 MB
13. Machine Learning in Finance/6. Predicting stock prices - conclusion.vtt
1.9 kB
14. Computer Vision - Face Detection/1. Computer vision introduction.mp4
5.8 MB
14. Computer Vision - Face Detection/1. Computer vision introduction.vtt
4.4 kB
14. Computer Vision - Face Detection/2. Viola-Jones algorithm.mp4
21 MB
14. Computer Vision - Face Detection/2. Viola-Jones algorithm.vtt
13 kB
14. Computer Vision - Face Detection/3. Haar-features.mp4
13 MB
14. Computer Vision - Face Detection/3. Haar-features.vtt
8.9 kB
14. Computer Vision - Face Detection/4. Integral images.mp4
9.6 MB
14. Computer Vision - Face Detection/4. Integral images.vtt
6.8 kB
14. Computer Vision - Face Detection/5. Boosting in computer vision.mp4
12 MB
14. Computer Vision - Face Detection/5. Boosting in computer vision.vtt
7.0 kB
14. Computer Vision - Face Detection/6. Cascading.mp4
6.2 MB
14. Computer Vision - Face Detection/6. Cascading.vtt
4.8 kB
14. Computer Vision - Face Detection/7. Face detection implementation I - installing OpenCV.mp4
11 MB
14. Computer Vision - Face Detection/7. Face detection implementation I - installing OpenCV.vtt
4.8 kB
14. Computer Vision - Face Detection/8. Face detection implementation II - CascadeClassifier.mp4
16 MB
14. Computer Vision - Face Detection/8. Face detection implementation II - CascadeClassifier.vtt
7.5 kB
14. Computer Vision - Face Detection/9. Face detection implementation III - CascadeClassifier parameters.mp4
8.6 MB
14. Computer Vision - Face Detection/9. Face detection implementation III - CascadeClassifier parameters.vtt
4.4 kB
14. Computer Vision - Face Detection/10. Face detection implementation IV - tuning the parameters.mp4
8.7 MB
14. Computer Vision - Face Detection/10. Face detection implementation IV - tuning the parameters.vtt
3.3 kB
15. Deep Learning/1. Types of neural networks.mp4
5.5 MB
15. Deep Learning/1. Types of neural networks.vtt
4.4 kB
16. Deep Neural Networks/1. Deep neural networks.mp4
7.6 MB
16. Deep Neural Networks/1. Deep neural networks.vtt
6.3 kB
16. Deep Neural Networks/2. Activation functions revisited.mp4
15 MB
16. Deep Neural Networks/2. Activation functions revisited.vtt
11 kB
16. Deep Neural Networks/3. Loss functions.mp4
10 MB
16. Deep Neural Networks/3. Loss functions.vtt
6.8 kB
16. Deep Neural Networks/4. Gradient descent stochastic gradient descent.mp4
12 MB
16. Deep Neural Networks/4. Gradient descent stochastic gradient descent.vtt
8.3 kB
16. Deep Neural Networks/5. Hyperparameters.mp4
8.3 MB
16. Deep Neural Networks/5. Hyperparameters.vtt
6.2 kB
16. Deep Neural Networks/6. ----- XOR PROBLEM -----.html
117 B
16. Deep Neural Networks/7. Deep neural network implementation I.mp4
15 MB
16. Deep Neural Networks/7. Deep neural network implementation I.vtt
7.1 kB
16. Deep Neural Networks/8. Deep neural network implementation II.mp4
16 MB
16. Deep Neural Networks/8. Deep neural network implementation II.vtt
7.4 kB
16. Deep Neural Networks/9. Deep neural network implementation III.mp4
18 MB
16. Deep Neural Networks/9. Deep neural network implementation III.vtt
6.8 kB
16. Deep Neural Networks/10. ----- IRIS DATASET -----.html
141 B
16. Deep Neural Networks/11. Multiclass classification implementation I.mp4
11 MB
16. Deep Neural Networks/11. Multiclass classification implementation I.vtt
6.0 kB
16. Deep Neural Networks/12. Multiclass classification implementation II.mp4
10 MB
16. Deep Neural Networks/12. Multiclass classification implementation II.vtt
5.6 kB
16. Deep Neural Networks/13. ARTICLE Optimizers Explained (SGD, ADAGrad, ADAM...).html
248 B
17. Convolutional Neural Networks/1. ----- CNN THEORY -----.html
130 B
17. Convolutional Neural Networks/2. Convolutional neural networks basics.mp4
9.6 MB
17. Convolutional Neural Networks/2. Convolutional neural networks basics.vtt
7.0 kB
17. Convolutional Neural Networks/3. Feature selection.mp4
6.9 MB
17. Convolutional Neural Networks/3. Feature selection.vtt
4.8 kB
17. Convolutional Neural Networks/4. Convolutional neural networks - kernel.mp4
6.3 MB
17. Convolutional Neural Networks/4. Convolutional neural networks - kernel.vtt
4.8 kB
17. Convolutional Neural Networks/5. Convolutional neural networks - kernel II.mp4
7.8 MB
17. Convolutional Neural Networks/5. Convolutional neural networks - kernel II.vtt
6.4 kB
17. Convolutional Neural Networks/6. Convolutional neural networks - pooling.mp4
9.9 MB
17. Convolutional Neural Networks/6. Convolutional neural networks - pooling.vtt
6.8 kB
17. Convolutional Neural Networks/7. Convolutional neural networks - flattening.mp4
8.4 MB
17. Convolutional Neural Networks/7. Convolutional neural networks - flattening.vtt
5.6 kB
17. Convolutional Neural Networks/8. Convolutional neural networks - illustration.mp4
6.0 MB
Udemy Course downloader.txt
94 B
17. Convolutional Neural Networks/9. ----- HANDWRITTEN DIGITS -----.html
164 B
17. Convolutional Neural Networks/10. Handwritten digit classification I.mp4
16 MB
17. Convolutional Neural Networks/10. Handwritten digit classification I.vtt
6.9 kB
17. Convolutional Neural Networks/11. Handwritten digit classification II.mp4
16 MB
17. Convolutional Neural Networks/11. Handwritten digit classification II.vtt
9.2 kB
17. Convolutional Neural Networks/12. Handwritten digit classification III.mp4
10 MB
17. Convolutional Neural Networks/12. Handwritten digit classification III.vtt
5.5 kB
17. Convolutional Neural Networks/13. ARTICLE Regularization (L1, L2 and dropout).html
232 B
18. Recurrent Neural Networks/1. ----- RNN THEORY -----.html
146 B
18. Recurrent Neural Networks/2. Why do recurrent neural networks are important.mp4
7.5 MB
18. Recurrent Neural Networks/2. Why do recurrent neural networks are important.vtt
5.1 kB
18. Recurrent Neural Networks/3. Recurrent neural networks basics.mp4
13 MB
18. Recurrent Neural Networks/3. Recurrent neural networks basics.vtt
9.9 kB
18. Recurrent Neural Networks/4. Vanishing and exploding gradients problem.mp4
20 MB
18. Recurrent Neural Networks/4. Vanishing and exploding gradients problem.vtt
11 kB
18. Recurrent Neural Networks/5. Long-short term memory (LTSM) model.mp4
17 MB
18. Recurrent Neural Networks/5. Long-short term memory (LTSM) model.vtt
12 kB
18. Recurrent Neural Networks/6. Gated recurrent units (GRUs).mp4
5.0 MB
18. Recurrent Neural Networks/6. Gated recurrent units (GRUs).vtt
3.9 kB
18. Recurrent Neural Networks/7. --- STOCK MAKRET ---.html
124 B
18. Recurrent Neural Networks/8. Stock price prediction example I.mp4
11 MB
18. Recurrent Neural Networks/8. Stock price prediction example I.vtt
6.6 kB
18. Recurrent Neural Networks/9. Stock price prediction example II.mp4
18 MB
18. Recurrent Neural Networks/9. Stock price prediction example II.vtt
4.6 kB
18. Recurrent Neural Networks/10. Stock price prediction example III.mp4
5.0 MB
18. Recurrent Neural Networks/10. Stock price prediction example III.vtt
2.6 kB
18. Recurrent Neural Networks/11. Stock price prediction example IV.mp4
14 MB
18. Recurrent Neural Networks/11. Stock price prediction example IV.vtt
6.5 kB
18. Recurrent Neural Networks/12. Stock price prediction example V.mp4
6.7 MB
18. Recurrent Neural Networks/12. Stock price prediction example V.vtt
3.6 kB
18. Recurrent Neural Networks/13. Stock price prediction example VI.mp4
15 MB
18. Recurrent Neural Networks/13. Stock price prediction example VI.vtt
5.4 kB
18. Recurrent Neural Networks/14. Stock price prediction example VII.mp4
7.2 MB
18. Recurrent Neural Networks/14. Stock price prediction example VII.vtt
3.2 kB
19. Course Materials (DOWNLOADS)/1. Course materials.html
70 B
19. Course Materials (DOWNLOADS)/1.1 PythonMachineLearning.zip.zip
22 MB
19. Course Materials (DOWNLOADS)/2. House prices csv file.html
55 B
19. Course Materials (DOWNLOADS)/2.1 house_prices.csv.csv
183 B
20. DISCOUNT FOR OTHER COURSES!/1. 90% OFF For Other Courses.html
5.1 kB