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

Creation Time	Feb. 9, 2026, 7:46 a.m.
Last Access Time	March 27, 2026, 11:25 p.m.
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17. Convolutional Neural Networks/8. Convolutional neural networks - illustration.vtt 70.5 MB
02. Installations/3. Installing Keras and TensorFlow.vtt 65.3 MB
08. Decision Trees/3. Decision trees introduction - information gain.mp4 47.0 MB
03. Linear Regression/2. Linear regression theory - optimization.mp4 42.3 MB
12. Neural Networks/29. Neural network example II - iris dataset.mp4 35.6 MB
03. Linear Regression/1. Linear regression introduction.mp4 26.4 MB
12. Neural Networks/12. Optimization - cost function.mp4 25.9 MB
06. Naive Bayes Classifier/7. Naive Bayes example - clustering news.mp4 23.3 MB
06. Naive Bayes Classifier/5. Text clustering - basics.mp4 22.1 MB
19. Course Materials (DOWNLOADS)/1.1 PythonMachineLearning.zip.zip 21.9 MB
07. Support Vector Machine (SVM)/5. Support vector machine example II - iris dataset.mp4 21.7 MB
04. Logistic Regression/4. Logistic regression example II- credit scoring.mp4 21.3 MB
14. Computer Vision - Face Detection/2. Viola-Jones algorithm.mp4 20.9 MB
07. Support Vector Machine (SVM)/1. Support vector machine introduction I - linear case.mp4 20.8 MB
12. Neural Networks/17. Gradient calculation I - output layer.mp4 20.3 MB
18. Recurrent Neural Networks/4. Vanishing and exploding gradients problem.mp4 19.6 MB
12. Neural Networks/13. Simplified feedforward network.mp4 19.4 MB
08. Decision Trees/2. Decision trees introduction - entropy.mp4 19.3 MB
12. Neural Networks/2. Axons and neurons in the human brain.mp4 19.2 MB
11. Clustering/6. K-means clustering - text clustering.mp4 18.9 MB
08. Decision Trees/7. The Gini-index approach.mp4 18.8 MB
12. Neural Networks/11. Feedforward neural networks.mp4 18.4 MB
16. Deep Neural Networks/9. Deep neural network implementation III.mp4 18.4 MB
18. Recurrent Neural Networks/9. Stock price prediction example II.mp4 18.4 MB
04. Logistic Regression/1. Logistic regression introduction.mp4 17.6 MB
12. Neural Networks/28. Neural network example I - XOR problem.mp4 17.6 MB
06. Naive Bayes Classifier/1. Naive Bayes classifier introduction I.mp4 17.4 MB
07. Support Vector Machine (SVM)/2. Support vector machine introduction II - non-linear case.mp4 17.2 MB
18. Recurrent Neural Networks/5. Long-short term memory (LTSM) model.mp4 17.0 MB
03. Linear Regression/4. Linear regression implementation I.mp4 16.7 MB
12. Neural Networks/5. Artificial neurons - the model.mp4 16.5 MB
17. Convolutional Neural Networks/10. Handwritten digit classification I.mp4 16.5 MB
07. Support Vector Machine (SVM)/6. Support vector machine example III - digit recognition.mp4 16.4 MB
12. Neural Networks/3. Modeling human brain.mp4 16.2 MB
14. Computer Vision - Face Detection/8. Face detection implementation II - CascadeClassifier.mp4 15.9 MB
16. Deep Neural Networks/8. Deep neural network implementation II.mp4 15.8 MB
17. Convolutional Neural Networks/11. Handwritten digit classification II.mp4 15.6 MB
16. Deep Neural Networks/2. Activation functions revisited.mp4 15.4 MB
18. Recurrent Neural Networks/13. Stock price prediction example VI.mp4 15.2 MB
16. Deep Neural Networks/7. Deep neural network implementation I.mp4 15.1 MB
12. Neural Networks/14. Feedforward neural network topology.mp4 14.7 MB
18. Recurrent Neural Networks/11. Stock price prediction example IV.mp4 14.5 MB
12. Neural Networks/6. Artificial neurons - activation functions.mp4 14.2 MB
11. Clustering/2. Principal component analysis example.mp4 14.0 MB
12. Neural Networks/16. Error calculation.mp4 13.7 MB
10. Boosting/3. Boosting introduction - equations.mp4 13.7 MB
11. Clustering/3. K-means clustering introduction I.mp4 13.7 MB
11. Clustering/9. Hierarchical clustering introduction.mp4 13.7 MB
08. Decision Trees/5. Decision trees implementation.mp4 13.6 MB
12. Neural Networks/15. The learning algorithm.mp4 13.3 MB
04. Logistic Regression/3. Logistic regression example I - sigmoid function.mp4 13.0 MB
10. Boosting/4. Boosting introduction - final formula.mp4 13.0 MB
18. Recurrent Neural Networks/3. Recurrent neural networks basics.mp4 12.9 MB
12. Neural Networks/25. Building networks.mp4 12.8 MB
12. Neural Networks/19. Backpropagation.mp4 12.7 MB
14. Computer Vision - Face Detection/3. Haar-features.mp4 12.6 MB
10. Boosting/5. Boosting implementation I - iris dataset.mp4 12.3 MB
14. Computer Vision - Face Detection/5. Boosting in computer vision.mp4 12.3 MB
16. Deep Neural Networks/4. Gradient descent stochastic gradient descent.mp4 12.3 MB
12. Neural Networks/26. Building networks II.mp4 12.0 MB
11. Clustering/10. Hierarchical clustering example.mp4 12.0 MB
08. Decision Trees/1. Decision trees introduction - basics.mp4 11.7 MB
04. Logistic Regression/6. Cross validation introduction.mp4 11.7 MB
09. Random Forest Classifier/2. Bagging introduction.mp4 11.7 MB
12. Neural Networks/7. Artificial neurons - an example.mp4 11.4 MB
09. Random Forest Classifier/4. Random forests example I - iris dataset.mp4 11.4 MB
03. Linear Regression/3. Linear regression theory - gradient descent.mp4 11.1 MB
16. Deep Neural Networks/11. Multiclass classification implementation I.mp4 11.1 MB
18. Recurrent Neural Networks/8. Stock price prediction example I.mp4 11.1 MB
11. Clustering/7. DBSCAN introduction.mp4 11.1 MB
04. Logistic Regression/5. Logistic regression example III - credit scoring.mp4 10.9 MB
12. Neural Networks/8. Neural networks - the big picture.mp4 10.8 MB
13. Machine Learning in Finance/3. Predicting stock prices logistic regression.mp4 10.8 MB
14. Computer Vision - Face Detection/7. Face detection implementation I - installing OpenCV.mp4 10.6 MB
07. Support Vector Machine (SVM)/4. Support vector machine example I - simple.mp4 10.5 MB
17. Convolutional Neural Networks/12. Handwritten digit classification III.mp4 10.4 MB
16. Deep Neural Networks/3. Loss functions.mp4 10.4 MB
10. Boosting/6. Boosting implementation II -tuning.mp4 10.4 MB
16. Deep Neural Networks/12. Multiclass classification implementation II.mp4 10.3 MB
06. Naive Bayes Classifier/6. Text clustering - inverse document frequency (TF-IDF).mp4 10.0 MB
05. K-Nearest Neighbor Classifier/6. K-nearest neighbor implementation II.mp4 10.0 MB
07. Support Vector Machine (SVM)/3. Support vector machine introduction III - kernels.mp4 9.9 MB
17. Convolutional Neural Networks/6. Convolutional neural networks - pooling.mp4 9.9 MB
09. Random Forest Classifier/1. Pruning introduction.mp4 9.8 MB
17. Convolutional Neural Networks/2. Convolutional neural networks basics.mp4 9.6 MB
14. Computer Vision - Face Detection/4. Integral images.mp4 9.6 MB
12. Neural Networks/22. Applications of neural networks II - stock market forecast.mp4 9.5 MB
05. K-Nearest Neighbor Classifier/1. K-nearest neighbor introduction.mp4 9.5 MB
11. Clustering/4. K-means clustering introduction II.mp4 9.5 MB
12. Neural Networks/23. Deep learning.mp4 9.5 MB
11. Clustering/5. K-means clustering example.mp4 9.4 MB
09. Random Forest Classifier/6. Random forests example III - parameter tuning.mp4 9.2 MB
12. Neural Networks/18. Gradient calculation II - hidden layer.mp4 9.2 MB
12. Neural Networks/21. Applications of neural networks I - character recognition.mp4 8.8 MB
03. Linear Regression/5. Linear regression implementation II.mp4 8.8 MB
14. Computer Vision - Face Detection/10. Face detection implementation IV - tuning the parameters.mp4 8.7 MB
09. Random Forest Classifier/3. Random forest classifier introduction.mp4 8.7 MB
13. Machine Learning in Finance/5. Predicting stock prices support vector machine.mp4 8.7 MB
05. K-Nearest Neighbor Classifier/3. K-nearest neighbor introduction - Euclidean-distance.mp4 8.6 MB
14. Computer Vision - Face Detection/9. Face detection implementation III - CascadeClassifier parameters.mp4 8.6 MB
11. Clustering/1. Principal component anlysis introduction.mp4 8.6 MB
06. Naive Bayes Classifier/2. Naive Bayes classifier introduction II - illustration.mp4 8.4 MB
17. Convolutional Neural Networks/7. Convolutional neural networks - flattening.mp4 8.4 MB
10. Boosting/1. Boosting introduction - basics.mp4 8.4 MB
16. Deep Neural Networks/5. Hyperparameters.mp4 8.3 MB
10. Boosting/2. Boosting introduction - illustration.mp4 8.2 MB
05. K-Nearest Neighbor Classifier/2. K-nearest neighbor introduction - lazy learning.mp4 8.1 MB
01. Introduction/2. Introduction to machine learning.mp4 8.1 MB
06. Naive Bayes Classifier/3. Naive Bayes classifier implementation.mp4 8.0 MB
13. Machine Learning in Finance/2. Fetching data from Yahoo Finance.mp4 8.0 MB
05. K-Nearest Neighbor Classifier/7. K-nearest neighbor implementation III.mp4 7.9 MB
11. Clustering/8. DBSCAN example.mp4 7.9 MB
17. Convolutional Neural Networks/5. Convolutional neural networks - kernel II.mp4 7.8 MB
16. Deep Neural Networks/1. Deep neural networks.mp4 7.6 MB
18. Recurrent Neural Networks/2. Why do recurrent neural networks are important.mp4 7.5 MB
18. Recurrent Neural Networks/14. Stock price prediction example VII.mp4 7.2 MB
13. Machine Learning in Finance/4. Predicting stock prices k-nearest neighbor.mp4 7.1 MB
05. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.mp4 6.9 MB
17. Convolutional Neural Networks/3. Feature selection.mp4 6.9 MB
18. Recurrent Neural Networks/12. Stock price prediction example V.mp4 6.7 MB
04. Logistic Regression/2. Logistic regression introduction II.mp4 6.7 MB
08. Decision Trees/6. Decision trees implementation II.mp4 6.7 MB
08. Decision Trees/6. Decision trees implementation II.vtt 6.7 MB
12. Neural Networks/4. Learning paradigms.mp4 6.5 MB
17. Convolutional Neural Networks/4. Convolutional neural networks - kernel.mp4 6.3 MB
14. Computer Vision - Face Detection/6. Cascading.mp4 6.2 MB
12. Neural Networks/27. Handling datasets.mp4 6.2 MB
17. Convolutional Neural Networks/8. Convolutional neural networks - illustration.mp4 6.0 MB
02. Installations/3. Installing Keras and TensorFlow.mp4 6.0 MB
14. Computer Vision - Face Detection/1. Computer vision introduction.mp4 5.8 MB
13. Machine Learning in Finance/1. Stock market basics.mp4 5.6 MB
15. Deep Learning/1. Types of neural networks.mp4 5.5 MB
12. Neural Networks/9. Applications of neural networks.mp4 5.2 MB
10. Boosting/7. Boosting vs. bagging.mp4 5.2 MB
18. Recurrent Neural Networks/6. Gated recurrent units (GRUs).mp4 5.0 MB
18. Recurrent Neural Networks/10. Stock price prediction example III.mp4 5.0 MB
12. Neural Networks/20. Backpropagation II.mp4 4.7 MB
02. Installations/1. Installing Anaconda.mp4 4.3 MB
09. Random Forest Classifier/5. Random forests example II - credit scoring.mp4 4.2 MB
08. Decision Trees/4. Decision trees introduction - pros and cons.mp4 4.2 MB
04. Logistic Regression/7. Cross validation example.mp4 4.2 MB
13. Machine Learning in Finance/6. Predicting stock prices - conclusion.mp4 3.5 MB
01. Introduction/1. Introduction.mp4 3.5 MB
02. Installations/2. Installing Spyder.mp4 2.8 MB
04. Logistic Regression/1. Logistic regression introduction.vtt 13.8 KB
14. Computer Vision - Face Detection/2. Viola-Jones algorithm.vtt 12.7 KB
18. Recurrent Neural Networks/5. Long-short term memory (LTSM) model.vtt 12.3 KB
12. Neural Networks/12. Optimization - cost function.vtt 11.9 KB
16. Deep Neural Networks/2. Activation functions revisited.vtt 10.8 KB
18. Recurrent Neural Networks/4. Vanishing and exploding gradients problem.vtt 10.6 KB
06. Naive Bayes Classifier/7. Naive Bayes example - clustering news.vtt 10.5 KB
08. Decision Trees/7. The Gini-index approach.vtt 10.1 KB
18. Recurrent Neural Networks/3. Recurrent neural networks basics.vtt 9.9 KB
07. Support Vector Machine (SVM)/1. Support vector machine introduction I - linear case.vtt 9.9 KB
08. Decision Trees/2. Decision trees introduction - entropy.vtt 9.8 KB
06. Naive Bayes Classifier/5. Text clustering - basics.vtt 9.5 KB
06. Naive Bayes Classifier/1. Naive Bayes classifier introduction I.vtt 9.5 KB
03. Linear Regression/1. Linear regression introduction.vtt 9.4 KB
12. Neural Networks/2. Axons and neurons in the human brain.vtt 9.4 KB
12. Neural Networks/17. Gradient calculation I - output layer.vtt 9.3 KB
17. Convolutional Neural Networks/11. Handwritten digit classification II.vtt 9.2 KB
09. Random Forest Classifier/2. Bagging introduction.vtt 9.1 KB
12. Neural Networks/13. Simplified feedforward network.vtt 9.0 KB
10. Boosting/4. Boosting introduction - final formula.vtt 9.0 KB
14. Computer Vision - Face Detection/3. Haar-features.vtt 8.9 KB
12. Neural Networks/11. Feedforward neural networks.vtt 8.9 KB
08. Decision Trees/1. Decision trees introduction - basics.vtt 8.8 KB
08. Decision Trees/3. Decision trees introduction - information gain.vtt 8.8 KB
07. Support Vector Machine (SVM)/5. Support vector machine example II - iris dataset.vtt 8.5 KB
08. Decision Trees/5. Decision trees implementation.vtt 8.4 KB
12. Neural Networks/3. Modeling human brain.vtt 8.3 KB
16. Deep Neural Networks/4. Gradient descent stochastic gradient descent.vtt 8.3 KB
03. Linear Regression/2. Linear regression theory - optimization.vtt 8.2 KB
04. Logistic Regression/4. Logistic regression example II- credit scoring.vtt 8.2 KB
12. Neural Networks/29. Neural network example II - iris dataset.vtt 8.1 KB
07. Support Vector Machine (SVM)/2. Support vector machine introduction II - non-linear case.vtt 8.1 KB
04. Logistic Regression/3. Logistic regression example I - sigmoid function.vtt 8.0 KB
03. Linear Regression/3. Linear regression theory - gradient descent.vtt 7.9 KB
12. Neural Networks/28. Neural network example I - XOR problem.vtt 7.8 KB
10. Boosting/3. Boosting introduction - equations.vtt 7.8 KB
11. Clustering/6. K-means clustering - text clustering.vtt 7.7 KB
14. Computer Vision - Face Detection/8. Face detection implementation II - CascadeClassifier.vtt 7.5 KB
03. Linear Regression/4. Linear regression implementation I.vtt 7.4 KB
07. Support Vector Machine (SVM)/6. Support vector machine example III - digit recognition.vtt 7.4 KB
12. Neural Networks/5. Artificial neurons - the model.vtt 7.4 KB
09. Random Forest Classifier/1. Pruning introduction.vtt 7.4 KB
16. Deep Neural Networks/8. Deep neural network implementation II.vtt 7.4 KB
16. Deep Neural Networks/7. Deep neural network implementation I.vtt 7.1 KB
11. Clustering/9. Hierarchical clustering introduction.vtt 7.0 KB
14. Computer Vision - Face Detection/5. Boosting in computer vision.vtt 7.0 KB
17. Convolutional Neural Networks/2. Convolutional neural networks basics.vtt 7.0 KB
17. Convolutional Neural Networks/10. Handwritten digit classification I.vtt 6.9 KB
11. Clustering/3. K-means clustering introduction I.vtt 6.9 KB
14. Computer Vision - Face Detection/4. Integral images.vtt 6.8 KB
16. Deep Neural Networks/9. Deep neural network implementation III.vtt 6.8 KB
17. Convolutional Neural Networks/6. Convolutional neural networks - pooling.vtt 6.8 KB
16. Deep Neural Networks/3. Loss functions.vtt 6.8 KB
05. K-Nearest Neighbor Classifier/6. K-nearest neighbor implementation II.vtt 6.6 KB
18. Recurrent Neural Networks/8. Stock price prediction example I.vtt 6.6 KB
12. Neural Networks/14. Feedforward neural network topology.vtt 6.6 KB
12. Neural Networks/6. Artificial neurons - activation functions.vtt 6.6 KB
18. Recurrent Neural Networks/11. Stock price prediction example IV.vtt 6.5 KB
12. Neural Networks/25. Building networks.vtt 6.5 KB
12. Neural Networks/16. Error calculation.vtt 6.5 KB
11. Clustering/2. Principal component analysis example.vtt 6.5 KB
05. K-Nearest Neighbor Classifier/1. K-nearest neighbor introduction.vtt 6.5 KB
04. Logistic Regression/5. Logistic regression example III - credit scoring.vtt 6.4 KB
17. Convolutional Neural Networks/5. Convolutional neural networks - kernel II.vtt 6.4 KB
09. Random Forest Classifier/3. Random forest classifier introduction.vtt 6.3 KB
16. Deep Neural Networks/1. Deep neural networks.vtt 6.3 KB
01. Introduction/2. Introduction to machine learning.vtt 6.3 KB
05. K-Nearest Neighbor Classifier/3. K-nearest neighbor introduction - Euclidean-distance.vtt 6.3 KB
10. Boosting/5. Boosting implementation I - iris dataset.vtt 6.3 KB
10. Boosting/2. Boosting introduction - illustration.vtt 6.3 KB
16. Deep Neural Networks/5. Hyperparameters.vtt 6.2 KB
11. Clustering/10. Hierarchical clustering example.vtt 6.2 KB
16. Deep Neural Networks/11. Multiclass classification implementation I.vtt 6.0 KB
12. Neural Networks/15. The learning algorithm.vtt 6.0 KB
04. Logistic Regression/6. Cross validation introduction.vtt 6.0 KB
12. Neural Networks/26. Building networks II.vtt 5.9 KB
12. Neural Networks/19. Backpropagation.vtt 5.7 KB
17. Convolutional Neural Networks/7. Convolutional neural networks - flattening.vtt 5.6 KB
16. Deep Neural Networks/12. Multiclass classification implementation II.vtt 5.6 KB
17. Convolutional Neural Networks/12. Handwritten digit classification III.vtt 5.5 KB
11. Clustering/5. K-means clustering example.vtt 5.4 KB
18. Recurrent Neural Networks/13. Stock price prediction example VI.vtt 5.4 KB
11. Clustering/7. DBSCAN introduction.vtt 5.4 KB
03. Linear Regression/5. Linear regression implementation II.vtt 5.4 KB
09. Random Forest Classifier/4. Random forests example I - iris dataset.vtt 5.2 KB
10. Boosting/6. Boosting implementation II -tuning.vtt 5.2 KB
06. Naive Bayes Classifier/6. Text clustering - inverse document frequency (TF-IDF).vtt 5.2 KB
09. Random Forest Classifier/6. Random forests example III - parameter tuning.vtt 5.1 KB
18. Recurrent Neural Networks/2. Why do recurrent neural networks are important.vtt 5.1 KB
20. DISCOUNT FOR OTHER COURSES!/1. 90% OFF For Other Courses.html 5.1 KB
06. Naive Bayes Classifier/3. Naive Bayes classifier implementation.vtt 5.0 KB
11. Clustering/8. DBSCAN example.vtt 5.0 KB
07. Support Vector Machine (SVM)/3. Support vector machine introduction III - kernels.vtt 5.0 KB
10. Boosting/1. Boosting introduction - basics.vtt 4.9 KB
06. Naive Bayes Classifier/2. Naive Bayes classifier introduction II - illustration.vtt 4.8 KB
12. Neural Networks/8. Neural networks - the big picture.vtt 4.8 KB
17. Convolutional Neural Networks/3. Feature selection.vtt 4.8 KB
17. Convolutional Neural Networks/4. Convolutional neural networks - kernel.vtt 4.8 KB
14. Computer Vision - Face Detection/6. Cascading.vtt 4.8 KB
12. Neural Networks/7. Artificial neurons - an example.vtt 4.8 KB
14. Computer Vision - Face Detection/7. Face detection implementation I - installing OpenCV.vtt 4.8 KB
12. Neural Networks/22. Applications of neural networks II - stock market forecast.vtt 4.7 KB
05. K-Nearest Neighbor Classifier/2. K-nearest neighbor introduction - lazy learning.vtt 4.7 KB
18. Recurrent Neural Networks/9. Stock price prediction example II.vtt 4.6 KB
12. Neural Networks/23. Deep learning.vtt 4.6 KB
05. K-Nearest Neighbor Classifier/7. K-nearest neighbor implementation III.vtt 4.5 KB
11. Clustering/4. K-means clustering introduction II.vtt 4.5 KB
07. Support Vector Machine (SVM)/4. Support vector machine example I - simple.vtt 4.5 KB
14. Computer Vision - Face Detection/9. Face detection implementation III - CascadeClassifier parameters.vtt 4.4 KB
12. Neural Networks/21. Applications of neural networks I - character recognition.vtt 4.4 KB
14. Computer Vision - Face Detection/1. Computer vision introduction.vtt 4.4 KB
04. Logistic Regression/2. Logistic regression introduction II.vtt 4.4 KB
15. Deep Learning/1. Types of neural networks.vtt 4.4 KB
13. Machine Learning in Finance/3. Predicting stock prices logistic regression.vtt 4.3 KB
13. Machine Learning in Finance/2. Fetching data from Yahoo Finance.vtt 4.3 KB
11. Clustering/1. Principal component anlysis introduction.vtt 4.2 KB
12. Neural Networks/18. Gradient calculation II - hidden layer.vtt 4.1 KB
18. Recurrent Neural Networks/6. Gated recurrent units (GRUs).vtt 3.9 KB
18. Recurrent Neural Networks/12. Stock price prediction example V.vtt 3.6 KB
13. Machine Learning in Finance/5. Predicting stock prices support vector machine.vtt 3.6 KB
13. Machine Learning in Finance/1. Stock market basics.vtt 3.5 KB
10. Boosting/7. Boosting vs. bagging.vtt 3.5 KB
05. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.vtt 3.3 KB
13. Machine Learning in Finance/4. Predicting stock prices k-nearest neighbor.vtt 3.3 KB
14. Computer Vision - Face Detection/10. Face detection implementation IV - tuning the parameters.vtt 3.3 KB
18. Recurrent Neural Networks/14. Stock price prediction example VII.vtt 3.2 KB
12. Neural Networks/27. Handling datasets.vtt 3.1 KB
12. Neural Networks/4. Learning paradigms.vtt 3.0 KB
08. Decision Trees/4. Decision trees introduction - pros and cons.vtt 2.9 KB
18. Recurrent Neural Networks/10. Stock price prediction example III.vtt 2.6 KB
04. Logistic Regression/7. Cross validation example.vtt 2.6 KB
01. Introduction/1. Introduction.vtt 2.4 KB
12. Neural Networks/9. Applications of neural networks.vtt 2.4 KB
02. Installations/1. Installing Anaconda.vtt 2.3 KB
12. Neural Networks/20. Backpropagation II.vtt 2.0 KB
09. Random Forest Classifier/5. Random forests example II - credit scoring.vtt 1.9 KB
13. Machine Learning in Finance/6. Predicting stock prices - conclusion.vtt 1.9 KB
02. Installations/2. Installing Spyder.vtt 1.8 KB
05. K-Nearest Neighbor Classifier/4. UPDATE bias and variance.html 333 bytes
16. Deep Neural Networks/13. ARTICLE Optimizers Explained (SGD, ADAGrad, ADAM...).html 248 bytes
17. Convolutional Neural Networks/13. ARTICLE Regularization (L1, L2 and dropout).html 232 bytes
06. Naive Bayes Classifier/4. ----- TEXT CLASSIFICATION -----.html 193 bytes
19. Course Materials (DOWNLOADS)/2.1 house_prices.csv.csv 183 bytes
17. Convolutional Neural Networks/9. ----- HANDWRITTEN DIGITS -----.html 164 bytes
18. Recurrent Neural Networks/1. ----- RNN THEORY -----.html 146 bytes
16. Deep Neural Networks/10. ----- IRIS DATASET -----.html 141 bytes
udemycoursedownloader.com.url 132 bytes
17. Convolutional Neural Networks/1. ----- CNN THEORY -----.html 130 bytes
18. Recurrent Neural Networks/7. --- STOCK MAKRET ---.html 124 bytes
16. Deep Neural Networks/6. ----- XOR PROBLEM -----.html 117 bytes
Udemy Course downloader.txt 94 bytes
19. Course Materials (DOWNLOADS)/1. Course materials.html 70 bytes
19. Course Materials (DOWNLOADS)/2. House prices csv file.html 55 bytes
12. Neural Networks/24. ----- IMPLEMENTATION -----.html 53 bytes
12. Neural Networks/10. ---- BACKPROPAGATION ----.html 42 bytes
12. Neural Networks/1. ---- NEURAL NETWORKS INTRODUCTION ----.html 35 bytes

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

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