UdemyCourseDownloader Introduction to Machine Learning Deep Learning in Python

UdemyCourseDownloader Introduction to Machine Learning Deep Learning in Python

Name	[UdemyCourseDownloader] Introduction to Machine Learning & Deep Learning in Python	DOWNLOAD Copy Link
Total Size	2.0 GB
Total Files	300
Hash	E5CD7A86473F94416CFBD436C50A552335331427

/17. Convolutional Neural Networks/
8. Convolutional neural networks - illustration.vtt	74.0 MB
1. ----- CNN THEORY -----.html	0.1 KB
2. Convolutional neural networks basics.mp4	10.0 MB
2. Convolutional neural networks basics.vtt	7.1 KB
3. Feature selection.mp4	7.3 MB
3. Feature selection.vtt	4.9 KB
4. Convolutional neural networks - kernel.mp4	6.7 MB
4. Convolutional neural networks - kernel.vtt	4.9 KB
5. Convolutional neural networks - kernel II.mp4	8.2 MB
5. Convolutional neural networks - kernel II.vtt	6.5 KB
6. Convolutional neural networks - pooling.mp4	10.3 MB
6. Convolutional neural networks - pooling.vtt	6.9 KB
7. Convolutional neural networks - flattening.mp4	8.8 MB
7. Convolutional neural networks - flattening.vtt	5.7 KB
8. Convolutional neural networks - illustration.mp4	6.3 MB
9. ----- HANDWRITTEN DIGITS -----.html	0.2 KB
10. Handwritten digit classification I.mp4	17.3 MB
10. Handwritten digit classification I.vtt	7.1 KB
11. Handwritten digit classification II.mp4	16.4 MB
11. Handwritten digit classification II.vtt	9.4 KB
12. Handwritten digit classification III.mp4	10.9 MB
12. Handwritten digit classification III.vtt	5.6 KB
13. ARTICLE Regularization (L1, L2 and dropout).html	0.2 KB
/
udemycoursedownloader.com.url	0.1 KB
Udemy Course downloader.txt	0.1 KB
/01. Introduction/
1. Introduction.mp4	3.6 MB
1. Introduction.vtt	2.5 KB
2. Introduction to machine learning.mp4	8.4 MB
2. Introduction to machine learning.vtt	6.4 KB
/02. Installations/
1. Installing Anaconda.mp4	4.5 MB
1. Installing Anaconda.vtt	2.3 KB
2. Installing Spyder.mp4	2.9 MB
2. Installing Spyder.vtt	1.9 KB
3. Installing Keras and TensorFlow.mp4	6.2 MB
3. Installing Keras and TensorFlow.vtt	68.5 MB
/03. Linear Regression/
1. Linear regression introduction.mp4	27.7 MB
1. Linear regression introduction.vtt	9.6 KB
2. Linear regression theory - optimization.mp4	44.3 MB
2. Linear regression theory - optimization.vtt	8.4 KB
3. Linear regression theory - gradient descent.mp4	11.6 MB
3. Linear regression theory - gradient descent.vtt	8.1 KB
4. Linear regression implementation I.mp4	17.5 MB
4. Linear regression implementation I.vtt	7.6 KB
5. Linear regression implementation II.mp4	9.2 MB
5. Linear regression implementation II.vtt	5.5 KB
/04. Logistic Regression/
1. Logistic regression introduction.mp4	18.5 MB
1. Logistic regression introduction.vtt	14.1 KB
2. Logistic regression introduction II.mp4	7.0 MB
2. Logistic regression introduction II.vtt	4.5 KB
3. Logistic regression example I - sigmoid function.mp4	13.7 MB
3. Logistic regression example I - sigmoid function.vtt	8.2 KB
4. Logistic regression example II- credit scoring.mp4	22.4 MB
4. Logistic regression example II- credit scoring.vtt	8.4 KB
5. Logistic regression example III - credit scoring.mp4	11.4 MB
5. Logistic regression example III - credit scoring.vtt	6.5 KB
6. Cross validation introduction.mp4	12.3 MB
6. Cross validation introduction.vtt	6.2 KB
7. Cross validation example.mp4	4.4 MB
7. Cross validation example.vtt	2.7 KB
/05. K-Nearest Neighbor Classifier/
1. K-nearest neighbor introduction.mp4	9.9 MB
1. K-nearest neighbor introduction.vtt	6.6 KB
2. K-nearest neighbor introduction - lazy learning.mp4	8.5 MB
2. K-nearest neighbor introduction - lazy learning.vtt	4.8 KB
3. K-nearest neighbor introduction - Euclidean-distance.mp4	9.0 MB
3. K-nearest neighbor introduction - Euclidean-distance.vtt	6.4 KB
4. UPDATE bias and variance.html	0.3 KB
5. K-nearest neighbor implementation I.mp4	7.3 MB
5. K-nearest neighbor implementation I.vtt	3.4 KB
6. K-nearest neighbor implementation II.mp4	10.4 MB
6. K-nearest neighbor implementation II.vtt	6.8 KB
7. K-nearest neighbor implementation III.mp4	8.3 MB
7. K-nearest neighbor implementation III.vtt	4.7 KB
/06. Naive Bayes Classifier/
1. Naive Bayes classifier introduction I.mp4	18.3 MB
1. Naive Bayes classifier introduction I.vtt	9.7 KB
2. Naive Bayes classifier introduction II - illustration.mp4	8.8 MB
2. Naive Bayes classifier introduction II - illustration.vtt	4.9 KB
3. Naive Bayes classifier implementation.mp4	8.4 MB
3. Naive Bayes classifier implementation.vtt	5.2 KB
4. ----- TEXT CLASSIFICATION -----.html	0.2 KB
5. Text clustering - basics.mp4	23.2 MB
5. Text clustering - basics.vtt	9.7 KB
6. Text clustering - inverse document frequency (TF-IDF).mp4	10.5 MB
6. Text clustering - inverse document frequency (TF-IDF).vtt	5.3 KB
7. Naive Bayes example - clustering news.mp4	24.5 MB
7. Naive Bayes example - clustering news.vtt	10.7 KB
/07. Support Vector Machine (SVM)/
1. Support vector machine introduction I - linear case.mp4	21.8 MB
1. Support vector machine introduction I - linear case.vtt	10.1 KB
2. Support vector machine introduction II - non-linear case.mp4	18.1 MB
2. Support vector machine introduction II - non-linear case.vtt	8.3 KB
3. Support vector machine introduction III - kernels.mp4	10.4 MB
3. Support vector machine introduction III - kernels.vtt	5.1 KB
4. Support vector machine example I - simple.mp4	11.0 MB
4. Support vector machine example I - simple.vtt	4.6 KB
5. Support vector machine example II - iris dataset.mp4	22.7 MB
5. Support vector machine example II - iris dataset.vtt	8.7 KB
6. Support vector machine example III - digit recognition.mp4	17.2 MB
6. Support vector machine example III - digit recognition.vtt	7.6 KB
/08. Decision Trees/
1. Decision trees introduction - basics.mp4	12.3 MB
1. Decision trees introduction - basics.vtt	9.0 KB
2. Decision trees introduction - entropy.mp4	20.2 MB
2. Decision trees introduction - entropy.vtt	10.1 KB
3. Decision trees introduction - information gain.mp4	49.2 MB
3. Decision trees introduction - information gain.vtt	9.0 KB
4. Decision trees introduction - pros and cons.mp4	4.4 MB
4. Decision trees introduction - pros and cons.vtt	2.9 KB
5. Decision trees implementation.mp4	14.3 MB
5. Decision trees implementation.vtt	8.6 KB
6. Decision trees implementation II.mp4	7.0 MB
6. Decision trees implementation II.vtt	7.0 MB
7. The Gini-index approach.mp4	19.7 MB
7. The Gini-index approach.vtt	10.3 KB
/09. Random Forest Classifier/
1. Pruning introduction.mp4	10.3 MB
1. Pruning introduction.vtt	7.6 KB
2. Bagging introduction.mp4	12.3 MB
2. Bagging introduction.vtt	9.3 KB
3. Random forest classifier introduction.mp4	9.1 MB
3. Random forest classifier introduction.vtt	6.5 KB
4. Random forests example I - iris dataset.mp4	11.9 MB
4. Random forests example I - iris dataset.vtt	5.3 KB
5. Random forests example II - credit scoring.mp4	4.4 MB
5. Random forests example II - credit scoring.vtt	2.0 KB
6. Random forests example III - parameter tuning.mp4	9.6 MB
6. Random forests example III - parameter tuning.vtt	5.2 KB
/10. Boosting/
1. Boosting introduction - basics.mp4	8.8 MB
1. Boosting introduction - basics.vtt	5.1 KB
2. Boosting introduction - illustration.mp4	8.6 MB
2. Boosting introduction - illustration.vtt	6.4 KB
3. Boosting introduction - equations.mp4	14.4 MB
3. Boosting introduction - equations.vtt	7.9 KB
4. Boosting introduction - final formula.mp4	13.6 MB
4. Boosting introduction - final formula.vtt	9.2 KB
5. Boosting implementation I - iris dataset.mp4	12.9 MB
5. Boosting implementation I - iris dataset.vtt	6.4 KB
6. Boosting implementation II -tuning.mp4	10.9 MB
6. Boosting implementation II -tuning.vtt	5.3 KB
7. Boosting vs. bagging.mp4	5.5 MB
7. Boosting vs. bagging.vtt	3.6 KB
/11. Clustering/
1. Principal component anlysis introduction.mp4	9.0 MB
1. Principal component anlysis introduction.vtt	4.3 KB
2. Principal component analysis example.mp4	14.7 MB
2. Principal component analysis example.vtt	6.6 KB
3. K-means clustering introduction I.mp4	14.3 MB
3. K-means clustering introduction I.vtt	7.1 KB
4. K-means clustering introduction II.mp4	9.9 MB
4. K-means clustering introduction II.vtt	4.6 KB
5. K-means clustering example.mp4	9.9 MB
5. K-means clustering example.vtt	5.6 KB
6. K-means clustering - text clustering.mp4	19.8 MB
6. K-means clustering - text clustering.vtt	7.9 KB
7. DBSCAN introduction.mp4	11.6 MB
7. DBSCAN introduction.vtt	5.5 KB
8. DBSCAN example.mp4	8.3 MB
8. DBSCAN example.vtt	5.1 KB
9. Hierarchical clustering introduction.mp4	14.3 MB
9. Hierarchical clustering introduction.vtt	7.2 KB
10. Hierarchical clustering example.mp4	12.5 MB
10. Hierarchical clustering example.vtt	6.3 KB
/12. Neural Networks/
1. ---- NEURAL NETWORKS INTRODUCTION ----.html	0.0 KB
2. Axons and neurons in the human brain.mp4	20.2 MB
2. Axons and neurons in the human brain.vtt	9.6 KB
3. Modeling human brain.mp4	17.0 MB
3. Modeling human brain.vtt	8.5 KB
4. Learning paradigms.mp4	6.8 MB
4. Learning paradigms.vtt	3.1 KB
5. Artificial neurons - the model.mp4	17.4 MB
5. Artificial neurons - the model.vtt	7.6 KB
6. Artificial neurons - activation functions.mp4	14.9 MB
6. Artificial neurons - activation functions.vtt	6.7 KB
7. Artificial neurons - an example.mp4	11.9 MB
7. Artificial neurons - an example.vtt	4.9 KB
8. Neural networks - the big picture.mp4	11.3 MB
8. Neural networks - the big picture.vtt	4.9 KB
9. Applications of neural networks.mp4	5.5 MB
9. Applications of neural networks.vtt	2.4 KB
10. ---- BACKPROPAGATION ----.html	0.0 KB
11. Feedforward neural networks.mp4	19.3 MB
11. Feedforward neural networks.vtt	9.1 KB
12. Optimization - cost function.mp4	27.2 MB
12. Optimization - cost function.vtt	12.1 KB
13. Simplified feedforward network.mp4	20.4 MB
13. Simplified feedforward network.vtt	9.2 KB
14. Feedforward neural network topology.mp4	15.4 MB
14. Feedforward neural network topology.vtt	6.7 KB
15. The learning algorithm.mp4	13.9 MB
15. The learning algorithm.vtt	6.2 KB
16. Error calculation.mp4	14.4 MB
16. Error calculation.vtt	6.7 KB
17. Gradient calculation I - output layer.mp4	21.3 MB
17. Gradient calculation I - output layer.vtt	9.5 KB
18. Gradient calculation II - hidden layer.mp4	9.6 MB
18. Gradient calculation II - hidden layer.vtt	4.2 KB
19. Backpropagation.mp4	13.3 MB
19. Backpropagation.vtt	5.9 KB
20. Backpropagation II.mp4	4.9 MB
20. Backpropagation II.vtt	2.1 KB
21. Applications of neural networks I - character recognition.mp4	9.2 MB
21. Applications of neural networks I - character recognition.vtt	4.5 KB
22. Applications of neural networks II - stock market forecast.mp4	10.0 MB
22. Applications of neural networks II - stock market forecast.vtt	4.8 KB
23. Deep learning.mp4	9.9 MB
23. Deep learning.vtt	4.7 KB
24. ----- IMPLEMENTATION -----.html	0.1 KB
25. Building networks.mp4	13.4 MB
25. Building networks.vtt	6.7 KB
26. Building networks II.mp4	12.6 MB
26. Building networks II.vtt	6.1 KB
27. Handling datasets.mp4	6.5 MB
27. Handling datasets.vtt	3.2 KB
28. Neural network example I - XOR problem.mp4	18.5 MB
28. Neural network example I - XOR problem.vtt	8.0 KB
29. Neural network example II - iris dataset.mp4	37.3 MB
29. Neural network example II - iris dataset.vtt	8.3 KB
/13. Machine Learning in Finance/
1. Stock market basics.mp4	5.9 MB
1. Stock market basics.vtt	3.6 KB
2. Fetching data from Yahoo Finance.mp4	8.3 MB
2. Fetching data from Yahoo Finance.vtt	4.4 KB
3. Predicting stock prices logistic regression.mp4	11.3 MB
3. Predicting stock prices logistic regression.vtt	4.4 KB
4. Predicting stock prices k-nearest neighbor.mp4	7.4 MB
4. Predicting stock prices k-nearest neighbor.vtt	3.4 KB
5. Predicting stock prices support vector machine.mp4	9.1 MB
5. Predicting stock prices support vector machine.vtt	3.7 KB
6. Predicting stock prices - conclusion.mp4	3.7 MB
6. Predicting stock prices - conclusion.vtt	2.0 KB
/14. Computer Vision - Face Detection/
1. Computer vision introduction.mp4	6.0 MB
1. Computer vision introduction.vtt	4.5 KB
2. Viola-Jones algorithm.mp4	22.0 MB
2. Viola-Jones algorithm.vtt	13.0 KB
3. Haar-features.mp4	13.3 MB
3. Haar-features.vtt	9.1 KB
4. Integral images.mp4	10.0 MB
4. Integral images.vtt	7.0 KB
5. Boosting in computer vision.mp4	12.9 MB
5. Boosting in computer vision.vtt	7.2 KB
6. Cascading.mp4	6.5 MB
6. Cascading.vtt	4.9 KB
7. Face detection implementation I - installing OpenCV.mp4	11.1 MB
7. Face detection implementation I - installing OpenCV.vtt	4.9 KB
8. Face detection implementation II - CascadeClassifier.mp4	16.7 MB
8. Face detection implementation II - CascadeClassifier.vtt	7.6 KB
9. Face detection implementation III - CascadeClassifier parameters.mp4	9.0 MB
9. Face detection implementation III - CascadeClassifier parameters.vtt	4.5 KB
10. Face detection implementation IV - tuning the parameters.mp4	9.2 MB
10. Face detection implementation IV - tuning the parameters.vtt	3.3 KB
/15. Deep Learning/
1. Types of neural networks.mp4	5.8 MB
1. Types of neural networks.vtt	4.5 KB
/16. Deep Neural Networks/
1. Deep neural networks.mp4	8.0 MB
1. Deep neural networks.vtt	6.4 KB
2. Activation functions revisited.mp4	16.2 MB
2. Activation functions revisited.vtt	11.0 KB
3. Loss functions.mp4	10.9 MB
3. Loss functions.vtt	6.9 KB
4. Gradient descent stochastic gradient descent.mp4	12.9 MB
4. Gradient descent stochastic gradient descent.vtt	8.5 KB
5. Hyperparameters.mp4	8.7 MB
5. Hyperparameters.vtt	6.4 KB
6. ----- XOR PROBLEM -----.html	0.1 KB
7. Deep neural network implementation I.mp4	15.8 MB
7. Deep neural network implementation I.vtt	7.3 KB
8. Deep neural network implementation II.mp4	16.6 MB
8. Deep neural network implementation II.vtt	7.5 KB
9. Deep neural network implementation III.mp4	19.3 MB
9. Deep neural network implementation III.vtt	7.0 KB
10. ----- IRIS DATASET -----.html	0.1 KB
11. Multiclass classification implementation I.mp4	11.6 MB
11. Multiclass classification implementation I.vtt	6.2 KB
12. Multiclass classification implementation II.mp4	10.8 MB
12. Multiclass classification implementation II.vtt	5.7 KB
13. ARTICLE Optimizers Explained (SGD, ADAGrad, ADAM...).html	0.2 KB
/18. Recurrent Neural Networks/
1. ----- RNN THEORY -----.html	0.1 KB
2. Why do recurrent neural networks are important.mp4	7.9 MB
2. Why do recurrent neural networks are important.vtt	5.2 KB
3. Recurrent neural networks basics.mp4	13.5 MB
3. Recurrent neural networks basics.vtt	10.2 KB
4. Vanishing and exploding gradients problem.mp4	20.6 MB
4. Vanishing and exploding gradients problem.vtt	10.8 KB
5. Long-short term memory (LTSM) model.mp4	17.9 MB
5. Long-short term memory (LTSM) model.vtt	12.6 KB
6. Gated recurrent units (GRUs).mp4	5.3 MB
6. Gated recurrent units (GRUs).vtt	4.0 KB
7. --- STOCK MAKRET ---.html	0.1 KB
8. Stock price prediction example I.mp4	11.6 MB
8. Stock price prediction example I.vtt	6.7 KB
9. Stock price prediction example II.mp4	19.3 MB
9. Stock price prediction example II.vtt	4.7 KB
10. Stock price prediction example III.mp4	5.2 MB
10. Stock price prediction example III.vtt	2.7 KB
11. Stock price prediction example IV.mp4	15.3 MB
11. Stock price prediction example IV.vtt	6.7 KB
12. Stock price prediction example V.mp4	7.1 MB
12. Stock price prediction example V.vtt	3.7 KB
13. Stock price prediction example VI.mp4	15.9 MB
13. Stock price prediction example VI.vtt	5.6 KB
14. Stock price prediction example VII.mp4	7.6 MB
14. Stock price prediction example VII.vtt	3.3 KB
/19. Course Materials (DOWNLOADS)/
1. Course materials.html	0.1 KB
1.1 PythonMachineLearning.zip.zip	23.0 MB
2. House prices csv file.html	0.1 KB
2.1 house_prices.csv.csv	0.2 KB
/20. DISCOUNT FOR OTHER COURSES!/
1. 90% OFF For Other Courses.html	5.2 KB
Total files 300