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[UdemyCourseDownloader] Introduction to Machine Learning & Deep Learning in Python
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17. Convolutional Neural Networks (Size: 1.83 GB) (Files: 300)
17. Convolutional Neural Networks
8. Convolutional neural networks - illustration.vtt
1. ----- CNN THEORY -----.html
2. Convolutional neural networks basics.mp4
2. Convolutional neural networks basics.vtt
3. Feature selection.mp4
3. Feature selection.vtt
4. Convolutional neural networks - kernel.mp4
4. Convolutional neural networks - kernel.vtt
5. Convolutional neural networks - kernel II.mp4
5. Convolutional neural networks - kernel II.vtt
6. Convolutional neural networks - pooling.mp4
6. Convolutional neural networks - pooling.vtt
7. Convolutional neural networks - flattening.mp4
7. Convolutional neural networks - flattening.vtt
8. Convolutional neural networks - illustration.mp4
9. ----- HANDWRITTEN DIGITS -----.html
10. Handwritten digit classification I.mp4
10. Handwritten digit classification I.vtt
11. Handwritten digit classification II.mp4
11. Handwritten digit classification II.vtt
12. Handwritten digit classification III.mp4
12. Handwritten digit classification III.vtt
13. ARTICLE Regularization (L1, L2 and dropout).html
udemycoursedownloader.com.url
01. Introduction
1. Introduction.mp4
1. Introduction.vtt
2. Introduction to machine learning.mp4
2. Introduction to machine learning.vtt
02. Installations
1. Installing Anaconda.mp4
1. Installing Anaconda.vtt
2. Installing Spyder.mp4
2. Installing Spyder.vtt
3. Installing Keras and TensorFlow.mp4
3. Installing Keras and TensorFlow.vtt
03. Linear Regression
1. Linear regression introduction.mp4
1. Linear regression introduction.vtt
2. Linear regression theory - optimization.mp4
2. Linear regression theory - optimization.vtt
3. Linear regression theory - gradient descent.mp4
3. Linear regression theory - gradient descent.vtt
4. Linear regression implementation I.mp4
4. Linear regression implementation I.vtt
5. Linear regression implementation II.mp4
5. Linear regression implementation II.vtt
04. Logistic Regression
1. Logistic regression introduction.mp4
1. Logistic regression introduction.vtt
2. Logistic regression introduction II.mp4
2. Logistic regression introduction II.vtt
3. Logistic regression example I - sigmoid function.mp4
3. Logistic regression example I - sigmoid function.vtt
4. Logistic regression example II- credit scoring.mp4
4. Logistic regression example II- credit scoring.vtt
5. Logistic regression example III - credit scoring.mp4
5. Logistic regression example III - credit scoring.vtt
6. Cross validation introduction.mp4
6. Cross validation introduction.vtt
7. Cross validation example.mp4
7. Cross validation example.vtt
05. K-Nearest Neighbor Classifier
1. K-nearest neighbor introduction.mp4
1. K-nearest neighbor introduction.vtt
2. K-nearest neighbor introduction - lazy learning.mp4
2. K-nearest neighbor introduction - lazy learning.vtt
3. K-nearest neighbor introduction - Euclidean-distance.mp4
3. K-nearest neighbor introduction - Euclidean-distance.vtt
4. UPDATE bias and variance.html
5. K-nearest neighbor implementation I.mp4
5. K-nearest neighbor implementation I.vtt
6. K-nearest neighbor implementation II.mp4
6. K-nearest neighbor implementation II.vtt
7. K-nearest neighbor implementation III.mp4
7. K-nearest neighbor implementation III.vtt
06. Naive Bayes Classifier
1. Naive Bayes classifier introduction I.mp4
1. Naive Bayes classifier introduction I.vtt
2. Naive Bayes classifier introduction II - illustration.mp4
2. Naive Bayes classifier introduction II - illustration.vtt
3. Naive Bayes classifier implementation.mp4
3. Naive Bayes classifier implementation.vtt
4. ----- TEXT CLASSIFICATION -----.html
5. Text clustering - basics.mp4
5. Text clustering - basics.vtt
6. Text clustering - inverse document frequency (TF-IDF).mp4
6. Text clustering - inverse document frequency (TF-IDF).vtt
7. Naive Bayes example - clustering news.mp4
7. Naive Bayes example - clustering news.vtt
07. Support Vector Machine (SVM)
1. Support vector machine introduction I - linear case.mp4
1. Support vector machine introduction I - linear case.vtt
2. Support vector machine introduction II - non-linear case.mp4
2. Support vector machine introduction II - non-linear case.vtt
3. Support vector machine introduction III - kernels.mp4
3. Support vector machine introduction III - kernels.vtt
4. Support vector machine example I - simple.mp4
4. Support vector machine example I - simple.vtt
5. Support vector machine example II - iris dataset.mp4
5. Support vector machine example II - iris dataset.vtt
6. Support vector machine example III - digit recognition.mp4
6. Support vector machine example III - digit recognition.vtt
08. Decision Trees
1. Decision trees introduction - basics.mp4
1. Decision trees introduction - basics.vtt
2. Decision trees introduction - entropy.mp4
2. Decision trees introduction - entropy.vtt
3. Decision trees introduction - information gain.mp4
3. Decision trees introduction - information gain.vtt
4. Decision trees introduction - pros and cons.mp4
4. Decision trees introduction - pros and cons.vtt
5. Decision trees implementation.mp4
5. Decision trees implementation.vtt
6. Decision trees implementation II.mp4
6. Decision trees implementation II.vtt
7. The Gini-index approach.mp4
7. The Gini-index approach.vtt
09. Random Forest Classifier
1. Pruning introduction.mp4
1. Pruning introduction.vtt
2. Bagging introduction.mp4
2. Bagging introduction.vtt
3. Random forest classifier introduction.mp4
3. Random forest classifier introduction.vtt
4. Random forests example I - iris dataset.mp4
4. Random forests example I - iris dataset.vtt
5. Random forests example II - credit scoring.mp4
5. Random forests example II - credit scoring.vtt
6. Random forests example III - parameter tuning.mp4
6. Random forests example III - parameter tuning.vtt
10. Boosting
1. Boosting introduction - basics.mp4
1. Boosting introduction - basics.vtt
2. Boosting introduction - illustration.mp4
2. Boosting introduction - illustration.vtt
3. Boosting introduction - equations.mp4
3. Boosting introduction - equations.vtt
4. Boosting introduction - final formula.mp4
4. Boosting introduction - final formula.vtt
5. Boosting implementation I - iris dataset.mp4
5. Boosting implementation I - iris dataset.vtt
6. Boosting implementation II -tuning.mp4
6. Boosting implementation II -tuning.vtt
7. Boosting vs. bagging.mp4
7. Boosting vs. bagging.vtt
11. Clustering
1. Principal component anlysis introduction.mp4
1. Principal component anlysis introduction.vtt
2. Principal component analysis example.mp4
2. Principal component analysis example.vtt
3. K-means clustering introduction I.mp4
3. K-means clustering introduction I.vtt
4. K-means clustering introduction II.mp4
4. K-means clustering introduction II.vtt
5. K-means clustering example.mp4
5. K-means clustering example.vtt
6. K-means clustering - text clustering.mp4
6. K-means clustering - text clustering.vtt
7. DBSCAN introduction.mp4
7. DBSCAN introduction.vtt
8. DBSCAN example.mp4
8. DBSCAN example.vtt
9. Hierarchical clustering introduction.mp4
9. Hierarchical clustering introduction.vtt
10. Hierarchical clustering example.mp4
10. Hierarchical clustering example.vtt
12. Neural Networks
1. ---- NEURAL NETWORKS INTRODUCTION ----.html
2. Axons and neurons in the human brain.mp4
2. Axons and neurons in the human brain.vtt
3. Modeling human brain.mp4
3. Modeling human brain.vtt
4. Learning paradigms.mp4
4. Learning paradigms.vtt
5. Artificial neurons - the model.mp4
5. Artificial neurons - the model.vtt
6. Artificial neurons - activation functions.mp4
6. Artificial neurons - activation functions.vtt
7. Artificial neurons - an example.mp4
7. Artificial neurons - an example.vtt
8. Neural networks - the big picture.mp4
8. Neural networks - the big picture.vtt
9. Applications of neural networks.mp4
9. Applications of neural networks.vtt
10. ---- BACKPROPAGATION ----.html
11. Feedforward neural networks.mp4
11. Feedforward neural networks.vtt
12. Optimization - cost function.mp4
12. Optimization - cost function.vtt
13. Simplified feedforward network.mp4
13. Simplified feedforward network.vtt
14. Feedforward neural network topology.mp4
14. Feedforward neural network topology.vtt
15. The learning algorithm.mp4
15. The learning algorithm.vtt
16. Error calculation.mp4
16. Error calculation.vtt
17. Gradient calculation I - output layer.mp4
17. Gradient calculation I - output layer.vtt
18. Gradient calculation II - hidden layer.mp4
18. Gradient calculation II - hidden layer.vtt
19. Backpropagation.mp4
19. Backpropagation.vtt
20. Backpropagation II.mp4
20. Backpropagation II.vtt
21. Applications of neural networks I - character recognition.mp4
21. Applications of neural networks I - character recognition.vtt
22. Applications of neural networks II - stock market forecast.mp4
22. Applications of neural networks II - stock market forecast.vtt
23. Deep learning.mp4
23. Deep learning.vtt
24. ----- IMPLEMENTATION -----.html
25. Building networks.mp4
25. Building networks.vtt
26. Building networks II.mp4
26. Building networks II.vtt
27. Handling datasets.mp4
27. Handling datasets.vtt
28. Neural network example I - XOR problem.mp4
28. Neural network example I - XOR problem.vtt
29. Neural network example II - iris dataset.mp4
29. Neural network example II - iris dataset.vtt
13. Machine Learning in Finance
1. Stock market basics.mp4
1. Stock market basics.vtt
2. Fetching data from Yahoo Finance.mp4
2. Fetching data from Yahoo Finance.vtt
3. Predicting stock prices logistic regression.mp4
3. Predicting stock prices logistic regression.vtt
4. Predicting stock prices k-nearest neighbor.mp4
4. Predicting stock prices k-nearest neighbor.vtt
5. Predicting stock prices support vector machine.mp4
5. Predicting stock prices support vector machine.vtt
6. Predicting stock prices - conclusion.mp4
6. Predicting stock prices - conclusion.vtt
14. Computer Vision - Face Detection
1. Computer vision introduction.mp4
1. Computer vision introduction.vtt
2. Viola-Jones algorithm.mp4
2. Viola-Jones algorithm.vtt
3. Haar-features.mp4
3. Haar-features.vtt
4. Integral images.mp4
4. Integral images.vtt
5. Boosting in computer vision.mp4
5. Boosting in computer vision.vtt
6. Cascading.mp4
6. Cascading.vtt
7. Face detection implementation I - installing OpenCV.mp4
7. Face detection implementation I - installing OpenCV.vtt
8. Face detection implementation II - CascadeClassifier.mp4
8. Face detection implementation II - CascadeClassifier.vtt
9. Face detection implementation III - CascadeClassifier parameters.mp4
9. Face detection implementation III - CascadeClassifier parameters.vtt
10. Face detection implementation IV - tuning the parameters.mp4
10. Face detection implementation IV - tuning the parameters.vtt
15. Deep Learning
1. Types of neural networks.mp4
1. Types of neural networks.vtt
16. Deep Neural Networks
1. Deep neural networks.mp4
1. Deep neural networks.vtt
2. Activation functions revisited.mp4
2. Activation functions revisited.vtt
3. Loss functions.mp4
3. Loss functions.vtt
4. Gradient descent stochastic gradient descent.mp4
4. Gradient descent stochastic gradient descent.vtt
5. Hyperparameters.mp4
5. Hyperparameters.vtt
6. ----- XOR PROBLEM -----.html
7. Deep neural network implementation I.mp4
7. Deep neural network implementation I.vtt
8. Deep neural network implementation II.mp4
8. Deep neural network implementation II.vtt
9. Deep neural network implementation III.mp4
9. Deep neural network implementation III.vtt
10. ----- IRIS DATASET -----.html
11. Multiclass classification implementation I.mp4
11. Multiclass classification implementation I.vtt
12. Multiclass classification implementation II.mp4
12. Multiclass classification implementation II.vtt
13. ARTICLE Optimizers Explained (SGD, ADAGrad, ADAM...).html
Udemy Course downloader.txt
18. Recurrent Neural Networks
1. ----- RNN THEORY -----.html
2. Why do recurrent neural networks are important.mp4
2. Why do recurrent neural networks are important.vtt
3. Recurrent neural networks basics.mp4
3. Recurrent neural networks basics.vtt
4. Vanishing and exploding gradients problem.mp4
4. Vanishing and exploding gradients problem.vtt
5. Long-short term memory (LTSM) model.mp4
5. Long-short term memory (LTSM) model.vtt
6. Gated recurrent units (GRUs).mp4
6. Gated recurrent units (GRUs).vtt
7. --- STOCK MAKRET ---.html
8. Stock price prediction example I.mp4
8. Stock price prediction example I.vtt
9. Stock price prediction example II.mp4
9. Stock price prediction example II.vtt
10. Stock price prediction example III.mp4
10. Stock price prediction example III.vtt
11. Stock price prediction example IV.mp4
11. Stock price prediction example IV.vtt
12. Stock price prediction example V.mp4
12. Stock price prediction example V.vtt
13. Stock price prediction example VI.mp4
13. Stock price prediction example VI.vtt
14. Stock price prediction example VII.mp4
14. Stock price prediction example VII.vtt
19. Course Materials (DOWNLOADS)
1. Course materials.html
1.1 PythonMachineLearning.zip.zip
2. House prices csv file.html
2.1 house_prices.csv.csv
20. DISCOUNT FOR OTHER COURSES!
1. 90% OFF For Other Courses.html
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