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Udemy - Machine Learning and Deep Learning Bootcamp in Python (1.2025)
- Date: 2026-04-11
- Size: 5.9 GB
- Files: 625
File Name
Size
01. Introduction/1. Introduction.mp4
8.0 MB
01. Introduction/1. Introduction.vtt
5.3 kB
02. Environment Setup/1. Installing Python.mp4
4.8 MB
02. Environment Setup/1. Installing Python.vtt
3.3 kB
02. Environment Setup/2. Installing PyCharm.mp4
23 MB
02. Environment Setup/2. Installing PyCharm.vtt
5.3 kB
02. Environment Setup/3. Installing TensorFlow and Keras.mp4
9.7 MB
02. Environment Setup/3. Installing TensorFlow and Keras.vtt
2.9 kB
03. Artificial Intelligence Basics/1. Why to learn artificial intelligence and machine learning.mp4
41 MB
03. Artificial Intelligence Basics/1. Why to learn artificial intelligence and machine learning.vtt
8.1 kB
03. Artificial Intelligence Basics/2. Types of artificial intelligence learning.mp4
41 MB
03. Artificial Intelligence Basics/2. Types of artificial intelligence learning.vtt
10 kB
03. Artificial Intelligence Basics/3. Fundamentals of statistics.mp4
36 MB
03. Artificial Intelligence Basics/3. Fundamentals of statistics.vtt
8.8 kB
04. ### MACHINE LEARNING ###/1. Machine learning section.html
471 B
05. Linear Regression/1. What is linear regression.mp4
34 MB
05. Linear Regression/1. What is linear regression.vtt
11 kB
05. Linear Regression/2. Linear regression theory - optimization.mp4
35 MB
05. Linear Regression/2. Linear regression theory - optimization.vtt
12 kB
05. Linear Regression/3. Linear regression theory - gradient descent.mp4
32 MB
05. Linear Regression/3. Linear regression theory - gradient descent.vtt
11 kB
05. Linear Regression/4. Linear regression implementation I.mp4
92 MB
05. Linear Regression/4. Linear regression implementation I.vtt
15 kB
05. Linear Regression/5. Linear regression implementation II.mp4
13 MB
05. Linear Regression/5. Linear regression implementation II.vtt
5.0 kB
05. Linear Regression/6. Mathematical formulation of linear regression.html
275 B
05. Linear Regression/7.1 Linear Regression Quiz.html
17 kB
06. Logistic Regression/1. What is logistic regression.mp4
48 MB
06. Logistic Regression/1. What is logistic regression.vtt
16 kB
06. Logistic Regression/2. Logistic regression and maximum likelihood estimation.mp4
23 MB
06. Logistic Regression/2. Logistic regression and maximum likelihood estimation.vtt
6.9 kB
06. Logistic Regression/3. Logistic regression example I - sigmoid function.mp4
66 MB
06. Logistic Regression/3. Logistic regression example I - sigmoid function.vtt
13 kB
06. Logistic Regression/4. Logistic regression example II- credit scoring.mp4
72 MB
06. Logistic Regression/4. Logistic regression example II- credit scoring.vtt
12 kB
06. Logistic Regression/5. Logistic regression example III - credit scoring.mp4
21 MB
06. Logistic Regression/5. Logistic regression example III - credit scoring.vtt
6.1 kB
06. Logistic Regression/6. Why is logistic regression linear.mp4
11 MB
06. Logistic Regression/6. Why is logistic regression linear.vtt
5.9 kB
06. Logistic Regression/7. Mathematical formulation of logistic regression.html
263 B
06. Logistic Regression/8.2 Logistic Regression Quiz.html
17 kB
07. Cross Validation/1. What is cross validation.mp4
24 MB
07. Cross Validation/1. What is cross validation.vtt
7.0 kB
07. Cross Validation/2. Cross validation example.mp4
15 MB
07. Cross Validation/2. Cross validation example.vtt
6.0 kB
07. Cross Validation/3.3 Cross Validation Quiz.html
17 kB
08. K-Nearest Neighbor Classifier/1. What is the k-nearest neighbor classifier.mp4
14 MB
08. K-Nearest Neighbor Classifier/1. What is the k-nearest neighbor classifier.vtt
8.2 kB
08. K-Nearest Neighbor Classifier/2. Concept of lazy learning.mp4
16 MB
08. K-Nearest Neighbor Classifier/2. Concept of lazy learning.vtt
4.4 kB
08. K-Nearest Neighbor Classifier/3. Distance metrics - Euclidean-distance.mp4
21 MB
08. K-Nearest Neighbor Classifier/3. Distance metrics - Euclidean-distance.vtt
8.9 kB
08. K-Nearest Neighbor Classifier/4. Bias and variance trade-off.mp4
15 MB
08. K-Nearest Neighbor Classifier/4. Bias and variance trade-off.vtt
5.2 kB
08. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.mp4
16 MB
08. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.vtt
7.8 kB
08. K-Nearest Neighbor Classifier/6. K-nearest neighbor implementation II.mp4
56 MB
08. K-Nearest Neighbor Classifier/6. K-nearest neighbor implementation II.vtt
9.7 kB
08. K-Nearest Neighbor Classifier/7. K-nearest neighbor implementation III.mp4
13 MB
08. K-Nearest Neighbor Classifier/7. K-nearest neighbor implementation III.vtt
4.6 kB
08. K-Nearest Neighbor Classifier/8. Mathematical formulation of k-nearest neighbor classifier.html
276 B
08. K-Nearest Neighbor Classifier/9.4 K-Nearest Neighbor Quiz.html
18 kB
09. Naive Bayes Classifier/1. What is the naive Bayes classifier.mp4
55 MB
09. Naive Bayes Classifier/1. What is the naive Bayes classifier.vtt
12 kB
09. Naive Bayes Classifier/2. Naive Bayes classifier illustration.mp4
9.2 MB
09. Naive Bayes Classifier/2. Naive Bayes classifier illustration.vtt
6.3 kB
09. Naive Bayes Classifier/3. Naive Bayes classifier implementation.mp4
11 MB
09. Naive Bayes Classifier/3. Naive Bayes classifier implementation.vtt
5.4 kB
09. Naive Bayes Classifier/4. What is text clustering.mp4
32 MB
09. Naive Bayes Classifier/4. What is text clustering.vtt
13 kB
09. Naive Bayes Classifier/5. Text clustering - inverse document frequency (TF-IDF).mp4
15 MB
09. Naive Bayes Classifier/5. Text clustering - inverse document frequency (TF-IDF).vtt
5.9 kB
09. Naive Bayes Classifier/6. Naive Bayes example - clustering news.mp4
80 MB
09. Naive Bayes Classifier/6. Naive Bayes example - clustering news.vtt
16 kB
09. Naive Bayes Classifier/7. Mathematical formulation of naive Bayes classifier.html
246 B
09. Naive Bayes Classifier/8.5 Naive Bayes Classifier Quiz.html
17 kB
10. Support Vector Machines (SVMs)/1. What are Support Vector Machines (SVMs).mp4
20 MB
10. Support Vector Machines (SVMs)/1. What are Support Vector Machines (SVMs).vtt
7.8 kB
10. Support Vector Machines (SVMs)/10. Advantages and disadvantages.mp4
6.0 MB
10. Support Vector Machines (SVMs)/10. Advantages and disadvantages.vtt
3.9 kB
10. Support Vector Machines (SVMs)/11. Mathematical formulation of Support Vector Machines (SVMs).html
419 B
10. Support Vector Machines (SVMs)/12.6 Support Vector Machines Quiz.html
17 kB
10. Support Vector Machines (SVMs)/2. Linearly separable problems.mp4
30 MB
10. Support Vector Machines (SVMs)/2. Linearly separable problems.vtt
17 kB
10. Support Vector Machines (SVMs)/3. Non-linearly separable problems.mp4
23 MB
10. Support Vector Machines (SVMs)/3. Non-linearly separable problems.vtt
9.6 kB
10. Support Vector Machines (SVMs)/4. Kernel functions.mp4
34 MB
10. Support Vector Machines (SVMs)/4. Kernel functions.vtt
14 kB
10. Support Vector Machines (SVMs)/5. Support vector machine example I - simple.mp4
38 MB
10. Support Vector Machines (SVMs)/5. Support vector machine example I - simple.vtt
12 kB
10. Support Vector Machines (SVMs)/6. Support vector machine example II - iris dataset.mp4
18 MB
10. Support Vector Machines (SVMs)/6. Support vector machine example II - iris dataset.vtt
7.2 kB
10. Support Vector Machines (SVMs)/7. Support vector machines example III - parameter tuning.mp4
25 MB
10. Support Vector Machines (SVMs)/7. Support vector machines example III - parameter tuning.vtt
8.1 kB
10. Support Vector Machines (SVMs)/8. Support vector machine example IV - digit recognition.mp4
22 MB
10. Support Vector Machines (SVMs)/8. Support vector machine example IV - digit recognition.vtt
11 kB
10. Support Vector Machines (SVMs)/9. Support vector machine example V - digit recognition.mp4
15 MB
10. Support Vector Machines (SVMs)/9. Support vector machine example V - digit recognition.vtt
6.2 kB
11. Decision Trees/1. What are decision trees.mp4
21 MB
11. Decision Trees/1. What are decision trees.vtt
6.8 kB
11. Decision Trees/10.7 Decision Trees Quiz.html
17 kB
11. Decision Trees/2. Entropy and information gain.mp4
27 MB
11. Decision Trees/2. Entropy and information gain.vtt
10 kB
11. Decision Trees/3. Example of how to construct decision trees.mp4
18 MB
11. Decision Trees/3. Example of how to construct decision trees.vtt
8.1 kB
11. Decision Trees/4. The Gini-index approach.mp4
27 MB
11. Decision Trees/4. The Gini-index approach.vtt
7.5 kB
11. Decision Trees/5. Decision trees introduction - pros and cons.mp4
7.8 MB
11. Decision Trees/5. Decision trees introduction - pros and cons.vtt
4.5 kB
11. Decision Trees/6. Decision trees implementation I.mp4
34 MB
11. Decision Trees/6. Decision trees implementation I.vtt
8.6 kB
11. Decision Trees/7. Decision trees implementation II - parameter tuning.mp4
14 MB
11. Decision Trees/7. Decision trees implementation II - parameter tuning.vtt
6.1 kB
11. Decision Trees/8. Decision tree implementation III - identifying cancer.mp4
16 MB
11. Decision Trees/8. Decision tree implementation III - identifying cancer.vtt
6.3 kB
11. Decision Trees/9. Mathematical formulation of decision trees.html
356 B
12. Random Forest Classifier/1. What is the bias-variance tradeoff.mp4
15 MB
12. Random Forest Classifier/1. What is the bias-variance tradeoff.vtt
9.0 kB
12. Random Forest Classifier/2. Pruning introduction.mp4
3.7 MB
12. Random Forest Classifier/2. Pruning introduction.vtt
2.7 kB
12. Random Forest Classifier/3. Bagging introduction.mp4
26 MB
12. Random Forest Classifier/3. Bagging introduction.vtt
6.9 kB
12. Random Forest Classifier/4. Random forest classifier introduction.mp4
11 MB
12. Random Forest Classifier/4. Random forest classifier introduction.vtt
6.3 kB
12. Random Forest Classifier/5. Random forests example I - iris dataset.mp4
20 MB
12. Random Forest Classifier/5. Random forests example I - iris dataset.vtt
7.6 kB
12. Random Forest Classifier/6. Random forests example II - credit scoring.mp4
33 MB
12. Random Forest Classifier/6. Random forests example II - credit scoring.vtt
7.4 kB
12. Random Forest Classifier/7. Random forests example III - OCR parameter tuning.mp4
32 MB
12. Random Forest Classifier/7. Random forests example III - OCR parameter tuning.vtt
13 kB
12. Random Forest Classifier/8. Mathematical formulation of random forest classifiers.html
263 B
12. Random Forest Classifier/9.8 Random Forests Quiz.html
17 kB
13. Boosting/1. What is boosting.mp4
16 MB
13. Boosting/1. What is boosting.vtt
5.9 kB
13. Boosting/2. Boosting introduction - illustration.mp4
11 MB
13. Boosting/2. Boosting introduction - illustration.vtt
7.3 kB
13. Boosting/3. Boosting introduction - equations.mp4
13 MB
13. Boosting/3. Boosting introduction - equations.vtt
9.2 kB
13. Boosting/4. Boosting introduction - final formula.mp4
37 MB
13. Boosting/4. Boosting introduction - final formula.vtt
11 kB
13. Boosting/5. Boosting implementation I - iris dataset.mp4
31 MB
13. Boosting/5. Boosting implementation I - iris dataset.vtt
8.5 kB
13. Boosting/6. Boosting implementation II -wine classification.mp4
39 MB
13. Boosting/6. Boosting implementation II -wine classification.vtt
15 kB
13. Boosting/7. Boosting vs. bagging.mp4
6.9 MB
13. Boosting/7. Boosting vs. bagging.vtt
4.2 kB
13. Boosting/8. Mathematical formulation of boosting.html
290 B
13. Boosting/9.9 Boosting Quiz.html
17 kB
14. Principal Component Analysis (PCA)/1. Principal component analysis (PCA) introduction.mp4
20 MB
14. Principal Component Analysis (PCA)/1. Principal component analysis (PCA) introduction.vtt
12 kB
14. Principal Component Analysis (PCA)/2. Mathematical formulation of principal components.mp4
13 MB
14. Principal Component Analysis (PCA)/2. Mathematical formulation of principal components.vtt
8.7 kB
14. Principal Component Analysis (PCA)/3. Principal component analysis example I.mp4
38 MB
14. Principal Component Analysis (PCA)/3. Principal component analysis example I.vtt
12 kB
14. Principal Component Analysis (PCA)/4. Principal component analysis example II.mp4
27 MB
14. Principal Component Analysis (PCA)/4. Principal component analysis example II.vtt
10 kB
14. Principal Component Analysis (PCA)/5. Mathematical formulation of principle component analysis (PCA).html
282 B
14. Principal Component Analysis (PCA)/6.10 PCA Quiz.html
17 kB
15. Clustering/1. K-means clustering introduction.mp4
17 MB
15. Clustering/1. K-means clustering introduction.vtt
15 kB
15. Clustering/10.11 Clustering Quiz.html
16 kB
15. Clustering/2. K-means clustering example.mp4
19 MB
15. Clustering/2. K-means clustering example.vtt
9.0 kB
15. Clustering/3. K-means clustering - text clustering.mp4
55 MB
15. Clustering/3. K-means clustering - text clustering.vtt
11 kB
15. Clustering/4. DBSCAN introduction.mp4
11 MB
15. Clustering/4. DBSCAN introduction.vtt
9.8 kB
15. Clustering/5. DBSCAN example.mp4
21 MB
15. Clustering/5. DBSCAN example.vtt
10 kB
15. Clustering/6. Hierarchical clustering introduction.mp4
17 MB
15. Clustering/6. Hierarchical clustering introduction.vtt
9.5 kB
15. Clustering/7. Hierarchical clustering example.mp4
20 MB
15. Clustering/7. Hierarchical clustering example.vtt
8.9 kB
15. Clustering/8. Hierarchical clustering - market segmentation.mp4
34 MB
15. Clustering/8. Hierarchical clustering - market segmentation.vtt
11 kB
15. Clustering/9. Mathematical formulation of clustering.html
629 B
16. Machine Learning Project I - Face Recognition/1. The Olivetti dataset.mp4
27 MB
16. Machine Learning Project I - Face Recognition/1. The Olivetti dataset.vtt
8.8 kB
16. Machine Learning Project I - Face Recognition/2. Understanding the dataset.mp4
22 MB
16. Machine Learning Project I - Face Recognition/2. Understanding the dataset.vtt
6.7 kB
16. Machine Learning Project I - Face Recognition/3. Finding optimal number of principal components (eigenvectors).mp4
22 MB
16. Machine Learning Project I - Face Recognition/3. Finding optimal number of principal components (eigenvectors).vtt
6.6 kB
16. Machine Learning Project I - Face Recognition/4. Understanding eigenfaces.mp4
54 MB
16. Machine Learning Project I - Face Recognition/4. Understanding eigenfaces.vtt
8.6 kB
16. Machine Learning Project I - Face Recognition/5. Constructing the machine learning models.mp4
19 MB
16. Machine Learning Project I - Face Recognition/5. Constructing the machine learning models.vtt
5.0 kB
16. Machine Learning Project I - Face Recognition/6. Using cross-validation.mp4
11 MB
16. Machine Learning Project I - Face Recognition/6. Using cross-validation.vtt
2.9 kB
17. ### NEURAL NETWORKS AND DEEP LEARNING ###/1. Neural networks and deep learning section.html
374 B
18. Feed-Forward Neural Network Theory/1. What are feed-forward neural networks.mp4
11 MB
18. Feed-Forward Neural Network Theory/1. What are feed-forward neural networks.vtt
6.2 kB
18. Feed-Forward Neural Network Theory/10. Mathematical formulation of feed-forward neural networks.html
261 B
18. Feed-Forward Neural Network Theory/11.12 Feed-Forward Neural Networks Quiz.html
18 kB
18. Feed-Forward Neural Network Theory/2. Artificial neural networks - the model.mp4
11 MB
18. Feed-Forward Neural Network Theory/2. Artificial neural networks - the model.vtt
7.1 kB
18. Feed-Forward Neural Network Theory/3. Why to use activation functions.mp4
12 MB
18. Feed-Forward Neural Network Theory/3. Why to use activation functions.vtt
8.3 kB
18. Feed-Forward Neural Network Theory/4. Neural networks - the big picture.mp4
19 MB
18. Feed-Forward Neural Network Theory/4. Neural networks - the big picture.vtt
12 kB
18. Feed-Forward Neural Network Theory/5. Using bias nodes in the neural network.mp4
7.7 MB
18. Feed-Forward Neural Network Theory/5. Using bias nodes in the neural network.vtt
4.4 kB
18. Feed-Forward Neural Network Theory/6. How to measure the error of the network.mp4
12 MB
18. Feed-Forward Neural Network Theory/6. How to measure the error of the network.vtt
7.3 kB
18. Feed-Forward Neural Network Theory/7. Optimization with gradient descent.mp4
15 MB
18. Feed-Forward Neural Network Theory/7. Optimization with gradient descent.vtt
9.0 kB
18. Feed-Forward Neural Network Theory/8. Gradient descent with backpropagation.mp4
29 MB
18. Feed-Forward Neural Network Theory/8. Gradient descent with backpropagation.vtt
13 kB
18. Feed-Forward Neural Network Theory/9. Backpropagation explained.mp4
19 MB
18. Feed-Forward Neural Network Theory/9. Backpropagation explained.vtt
14 kB
19. Simple Feed-Forward Neural Network Implementation/1. Simple neural network implementation - XOR problem.mp4
24 MB
19. Simple Feed-Forward Neural Network Implementation/1. Simple neural network implementation - XOR problem.vtt
10 kB
19. Simple Feed-Forward Neural Network Implementation/2. Linearly and non-linearly separable problems.mp4
13 MB
19. Simple Feed-Forward Neural Network Implementation/2. Linearly and non-linearly separable problems.vtt
2.2 kB
19. Simple Feed-Forward Neural Network Implementation/3. Credit scoring with simple neural networks.mp4
29 MB
19. Simple Feed-Forward Neural Network Implementation/3. Credit scoring with simple neural networks.vtt
11 kB
20. Deep Learning/1. Types of neural networks.mp4
5.8 MB
20. Deep Learning/1. Types of neural networks.vtt
3.0 kB
21. Deep Neural Networks Theory/1. What are deep neural networks.mp4
8.7 MB
21. Deep Neural Networks Theory/1. What are deep neural networks.vtt
4.9 kB
21. Deep Neural Networks Theory/10.13 Deep Neural Networks Quiz.html
18 kB
21. Deep Neural Networks Theory/2. Vanishing and exploding gradients problem.mp4
11 MB
21. Deep Neural Networks Theory/2. Vanishing and exploding gradients problem.vtt
6.2 kB
21. Deep Neural Networks Theory/3. Activation functions and weight initializations.mp4
17 MB
21. Deep Neural Networks Theory/3. Activation functions and weight initializations.vtt
6.1 kB
21. Deep Neural Networks Theory/4. Softmax activation function.mp4
14 MB
21. Deep Neural Networks Theory/4. Softmax activation function.vtt
8.2 kB
21. Deep Neural Networks Theory/5. Loss functions revisited.mp4
30 MB
21. Deep Neural Networks Theory/5. Loss functions revisited.vtt
8.7 kB
21. Deep Neural Networks Theory/6. Gradient descent and stochastic gradient descent.mp4
15 MB
21. Deep Neural Networks Theory/6. Gradient descent and stochastic gradient descent.vtt
5.2 kB
21. Deep Neural Networks Theory/7. Normalization, batches and epochs.mp4
15 MB
21. Deep Neural Networks Theory/7. Normalization, batches and epochs.vtt
9.1 kB
21. Deep Neural Networks Theory/8. Regularization.mp4
11 MB
21. Deep Neural Networks Theory/8. Regularization.vtt
5.1 kB
21. Deep Neural Networks Theory/9. Mathematical formulation of deep neural networks.html
290 B
22. Deep Neural Networks Implementation/1. Deep neural network implementation I.mp4
18 MB
22. Deep Neural Networks Implementation/1. Deep neural network implementation I.vtt
7.7 kB
22. Deep Neural Networks Implementation/2. Deep neural network implementation II.mp4
42 MB
22. Deep Neural Networks Implementation/2. Deep neural network implementation II.vtt
8.0 kB
22. Deep Neural Networks Implementation/3. Deep neural network implementation III.mp4
26 MB
22. Deep Neural Networks Implementation/3. Deep neural network implementation III.vtt
7.1 kB
22. Deep Neural Networks Implementation/4. Multiclass classification implementation I.mp4
35 MB
22. Deep Neural Networks Implementation/4. Multiclass classification implementation I.vtt
9.3 kB
22. Deep Neural Networks Implementation/5. Multiclass classification implementation II.mp4
32 MB
22. Deep Neural Networks Implementation/5. Multiclass classification implementation II.vtt
6.3 kB
23. Machine Learning Project II - Smile Detector/1. Understanding the classification problem.mp4
3.4 MB
23. Machine Learning Project II - Smile Detector/1. Understanding the classification problem.vtt
2.4 kB
23. Machine Learning Project II - Smile Detector/2. Reading the images and constructing the dataset I.mp4
20 MB
23. Machine Learning Project II - Smile Detector/2. Reading the images and constructing the dataset I.vtt
6.7 kB
23. Machine Learning Project II - Smile Detector/3. Reading the images and constructing the dataset II.mp4
21 MB
23. Machine Learning Project II - Smile Detector/3. Reading the images and constructing the dataset II.vtt
5.1 kB
23. Machine Learning Project II - Smile Detector/4. Building the deep neural network model.mp4
8.6 MB
23. Machine Learning Project II - Smile Detector/4. Building the deep neural network model.vtt
3.5 kB
23. Machine Learning Project II - Smile Detector/5. Evaluating and testing the model.mp4
14 MB
23. Machine Learning Project II - Smile Detector/5. Evaluating and testing the model.vtt
4.1 kB
24. Convolutional Neural Networks (CNNs) Theory/1. What are convolutional neural networks.mp4
12 MB
24. Convolutional Neural Networks (CNNs) Theory/1. What are convolutional neural networks.vtt
7.4 kB
24. Convolutional Neural Networks (CNNs) Theory/10. CNN visualization tool.html
126 B
24. Convolutional Neural Networks (CNNs) Theory/11. Mathematical formulation of convolution neural networks.html
290 B
24. Convolutional Neural Networks (CNNs) Theory/12.14 Convolutional Neural Networks (CNNs) Quiz.html
17 kB
24. Convolutional Neural Networks (CNNs) Theory/2. Feature selection with kernels.mp4
17 MB
24. Convolutional Neural Networks (CNNs) Theory/2. Feature selection with kernels.vtt
7.2 kB
24. Convolutional Neural Networks (CNNs) Theory/3. Convolutional operation example.mp4
11 MB
24. Convolutional Neural Networks (CNNs) Theory/3. Convolutional operation example.vtt
8.1 kB
24. Convolutional Neural Networks (CNNs) Theory/4. Convolutional neural networks - pooling.mp4
8.3 MB
24. Convolutional Neural Networks (CNNs) Theory/4. Convolutional neural networks - pooling.vtt
5.0 kB
24. Convolutional Neural Networks (CNNs) Theory/5. Convolutional neural networks - flattening and the neural network layer.mp4
17 MB
24. Convolutional Neural Networks (CNNs) Theory/5. Convolutional neural networks - flattening and the neural network layer.vtt
8.3 kB
24. Convolutional Neural Networks (CNNs) Theory/6. Convolutional neural networks - illustration.mp4
11 MB
24. Convolutional Neural Networks (CNNs) Theory/6. Convolutional neural networks - illustration.vtt
3.1 kB
24. Convolutional Neural Networks (CNNs) Theory/7. How do you update the kernel weights exactly.mp4
11 MB
24. Convolutional Neural Networks (CNNs) Theory/7. How do you update the kernel weights exactly.vtt
9.6 kB
24. Convolutional Neural Networks (CNNs) Theory/8. Colored images and tensors.mp4
23 MB
24. Convolutional Neural Networks (CNNs) Theory/8. Colored images and tensors.vtt
7.9 kB
24. Convolutional Neural Networks (CNNs) Theory/9. Evolution of CNN architectures.mp4
12 MB
24. Convolutional Neural Networks (CNNs) Theory/9. Evolution of CNN architectures.vtt
6.4 kB
25. Convolutional Neural Networks (CNNs) Implementation/1. Handwritten digit classification I.mp4
54 MB
25. Convolutional Neural Networks (CNNs) Implementation/1. Handwritten digit classification I.vtt
15 kB
25. Convolutional Neural Networks (CNNs) Implementation/2. Handwritten digit classification II.mp4
58 MB
25. Convolutional Neural Networks (CNNs) Implementation/2. Handwritten digit classification II.vtt
14 kB
25. Convolutional Neural Networks (CNNs) Implementation/3. Handwritten digit classification III.mp4
35 MB
25. Convolutional Neural Networks (CNNs) Implementation/3. Handwritten digit classification III.vtt
7.8 kB
26. Machine Learning Project III - Identifying Objects with CNNs/1. What is the CIFAR-10 dataset.mp4
19 MB
26. Machine Learning Project III - Identifying Objects with CNNs/1. What is the CIFAR-10 dataset.vtt
7.8 kB
26. Machine Learning Project III - Identifying Objects with CNNs/2. Preprocessing the data.mp4
6.2 MB
26. Machine Learning Project III - Identifying Objects with CNNs/2. Preprocessing the data.vtt
2.1 kB
26. Machine Learning Project III - Identifying Objects with CNNs/3. Fitting the model.mp4
48 MB
26. Machine Learning Project III - Identifying Objects with CNNs/3. Fitting the model.vtt
12 kB
26. Machine Learning Project III - Identifying Objects with CNNs/4. What is batch normalization.mp4
20 MB
26. Machine Learning Project III - Identifying Objects with CNNs/4. What is batch normalization.vtt
5.4 kB
26. Machine Learning Project III - Identifying Objects with CNNs/5. Tuning the parameters - regularization.mp4
52 MB
26. Machine Learning Project III - Identifying Objects with CNNs/5. Tuning the parameters - regularization.vtt
12 kB
27. Recurrent Neural Networks (RNNs) Theory/1. Why do recurrent neural networks are important.mp4
12 MB
27. Recurrent Neural Networks (RNNs) Theory/1. Why do recurrent neural networks are important.vtt
5.1 kB
27. Recurrent Neural Networks (RNNs) Theory/10. Mathematical formulation of recurrent neural networks.html
258 B
27. Recurrent Neural Networks (RNNs) Theory/11.15 Recurrent Neural Networks Quiz.html
18 kB
27. Recurrent Neural Networks (RNNs) Theory/2. Recurrent neural networks basics.mp4
20 MB
27. Recurrent Neural Networks (RNNs) Theory/2. Recurrent neural networks basics.vtt
14 kB
27. Recurrent Neural Networks (RNNs) Theory/3. The backpropagation through time (BPTT) algorithm.mp4
11 MB
27. Recurrent Neural Networks (RNNs) Theory/3. The backpropagation through time (BPTT) algorithm.vtt
6.9 kB
27. Recurrent Neural Networks (RNNs) Theory/4. Vanishing and exploding gradients problem.mp4
9.5 MB
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27. Recurrent Neural Networks (RNNs) Theory/5. Long-short term memory (LSTM) model theory I.mp4
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27. Recurrent Neural Networks (RNNs) Theory/7. Long-short term memory (LSTM) forward pass example.mp4
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27. Recurrent Neural Networks (RNNs) Theory/8. Long-short term memory (LSTM) backpropagation example.mp4
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27. Recurrent Neural Networks (RNNs) Theory/9. Gated recurrent units (GRUs).mp4
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28. Recurrent Neural Networks (RNNs) Implementation/1. Time series analysis example I.mp4
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28. Recurrent Neural Networks (RNNs) Implementation/1. Time series analysis example I.vtt
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28. Recurrent Neural Networks (RNNs) Implementation/6. Time series analysis example VI.mp4
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29. Transformers/1. Transformers chapter overview.mp4
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29. Transformers/10. The neural network layer.mp4
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29. Transformers/11. Understanding the training of transformers.mp4
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29. Transformers/12. What is ChatGPT.mp4
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29. Transformers/13. Mathematical formulation of transformers.html
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29. Transformers/14.16 Transformers Quiz.html
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29. Transformers/2. Understanding word embeddings I.mp4
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29. Transformers/2. Understanding word embeddings I.vtt
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29. Transformers/5. Understanding positional encoding.mp4
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29. Transformers/6. The self-attention mechanism I.mp4
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29. Transformers/8. What is masking.mp4
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29. Transformers/9. Multi-head architecture.mp4
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30. Generative Adversarial Networks (GANs) Theory/1. What is a generative adversarial network (GAN).mp4
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30. Generative Adversarial Networks (GANs) Theory/1. What is a generative adversarial network (GAN).vtt
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30. Generative Adversarial Networks (GANs) Theory/2. GANs fundamentals.mp4
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30. Generative Adversarial Networks (GANs) Theory/3. GANs learning procedure.mp4
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30. Generative Adversarial Networks (GANs) Theory/4. Training GANs.mp4
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30. Generative Adversarial Networks (GANs) Theory/5. Mathematical formulation of GANs.html
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30. Generative Adversarial Networks (GANs) Theory/6.17 GANs Quiz.html
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31. Generative Adversarial Networks (GANs) Implementation/1. GAN implementation I.mp4
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31. Generative Adversarial Networks (GANs) Implementation/6. GAN.py
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/1. Numerical optimization algorithms in machine learning.html
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/10. What is RMSProp.mp4
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/11. ADAM optimizer introduction.mp4
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/12. ADAM optimizer implementation.mp4
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/12. ADAM.py
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/13. Mathematical formulation of optimization algorithms in machine learning.html
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/2. What is gradient descent.mp4
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/3. Gradient descent implementation.mp4
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/3. GradientDescent.py
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/4. Gradient descent with momentum.mp4
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/5. Stochastic gradient descent introduction.mp4
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/6. Stochastic gradient descent implementation I.mp4
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/6. Stochastic gradient descent implementation I.vtt
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/6. StochasticGradientDescent.py
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/7. Stochastic gradient descent implementation II.mp4
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/7. StochasticGradientDescentRegression.py
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/8. What is ADAGrad.mp4
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/9. ADAGrad implementation.mp4
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/9. GradientDescentAdaGrad.py
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33. ### REINFORCEMENT LEARNING ###/1. What is reinforcement learning.html
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33. ### REINFORCEMENT LEARNING ###/2. Applications of reinforcement learning.mp4
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34. Markov Decision Process (MDP) Theory/1. What are Markov decision processes.mp4
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34. Markov Decision Process (MDP) Theory/1. What are Markov decision processes.vtt
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34. Markov Decision Process (MDP) Theory/10.18 Reinforcement Learning Basics Quiz.html
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34. Markov Decision Process (MDP) Theory/2. Markov decision processes basics.mp4
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34. Markov Decision Process (MDP) Theory/3. Markov decision processes - equations.mp4
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34. Markov Decision Process (MDP) Theory/3. Markov decision processes - equations.vtt
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34. Markov Decision Process (MDP) Theory/4. Markov decision processes - illustration.mp4
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34. Markov Decision Process (MDP) Theory/4. Markov decision processes - illustration.vtt
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34. Markov Decision Process (MDP) Theory/5. Bellman-equation.mp4
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34. Markov Decision Process (MDP) Theory/5. Bellman-equation.vtt
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34. Markov Decision Process (MDP) Theory/6. How to solve MDP problems.mp4
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34. Markov Decision Process (MDP) Theory/7. What is value iteration.mp4
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34. Markov Decision Process (MDP) Theory/8. What is policy iteration.mp4
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34. Markov Decision Process (MDP) Theory/9. Mathematical formulation of reinforcement learning.html
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35. Exploration vs. Exploitation Problem/1. Exploration vs exploitation problem.mp4
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35. Exploration vs. Exploitation Problem/1. Exploration vs exploitation problem.vtt
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35. Exploration vs. Exploitation Problem/2. N-armed bandit problem introduction.mp4
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35. Exploration vs. Exploitation Problem/2. N-armed bandit problem introduction.vtt
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35. Exploration vs. Exploitation Problem/3. N-armed bandit problem implementation.mp4
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35. Exploration vs. Exploitation Problem/4. Applications AB testing in marketing.mp4
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35. Exploration vs. Exploitation Problem/5.19 Exploration vs. Exploitation Quiz.html
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36. Q Learning Theory/1. What is Q learning.mp4
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36. Q Learning Theory/2. Q learning introduction - the algorithm.mp4
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36. Q Learning Theory/3. Q learning illustration.mp4
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36. Q Learning Theory/4. Mathematical formulation of Q learning.html
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36. Q Learning Theory/5.20 Q Learning Quiz.html
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37. Q Learning Implementation (Tic Tac Toe)/1. Tic tac toe with Q learning implementation I.mp4
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37. Q Learning Implementation (Tic Tac Toe)/2. Tic tac toe with Q learning implementation II.mp4
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37. Q Learning Implementation (Tic Tac Toe)/3. Tic tac toe with Q learning implementation III.mp4
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37. Q Learning Implementation (Tic Tac Toe)/7. Tic tac toe with Q learning implementation VII.mp4
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37. Q Learning Implementation (Tic Tac Toe)/8. QLearningTicTacToe.py
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37. Q Learning Implementation (Tic Tac Toe)/8. Tic tac toe with Q learning implementation VIII.mp4
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38. Deep Q Learning Theory/1. What is deep Q learning.mp4
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38. Deep Q Learning Theory/2. Deep Q learning and ε-greedy strategy.mp4
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38. Deep Q Learning Theory/3. Remember and replay.mp4
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38. Deep Q Learning Theory/4. Why use an additional target neural network.mp4
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38. Deep Q Learning Theory/5. Mathematical formulation of deep Q learning.html
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38. Deep Q Learning Theory/6.21 Deep Q Learning Quiz.html
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39. Deep Q Learning Implementation (Tic Tac Toe)/1. Tic Tac Toe with deep Q learning implementation I.mp4
13 MB
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39. Deep Q Learning Implementation (Tic Tac Toe)/2. Tic Tac Toe with deep Q learning implementation II.mp4
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39. Deep Q Learning Implementation (Tic Tac Toe)/2. Tic Tac Toe with deep Q learning implementation II.vtt
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39. Deep Q Learning Implementation (Tic Tac Toe)/3. Tic Tac Toe with deep Q learning implementation III.mp4
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39. Deep Q Learning Implementation (Tic Tac Toe)/4. Tic Tac Toe with deep Q learning implementation IV.mp4
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39. Deep Q Learning Implementation (Tic Tac Toe)/5. DeepQLearningTicTacToe.py
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39. Deep Q Learning Implementation (Tic Tac Toe)/5. Tic Tac Toe with deep Q learning implementation V.mp4
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39. Deep Q Learning Implementation (Tic Tac Toe)/5. Tic Tac Toe with deep Q learning implementation V.vtt
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40. Proximal Policy Optimization (PPO) Theory/1. What are the problems with deep Q learning.mp4
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40. Proximal Policy Optimization (PPO) Theory/1. What are the problems with deep Q learning.vtt
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40. Proximal Policy Optimization (PPO) Theory/2. TRPO algorithm introduction I.mp4
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40. Proximal Policy Optimization (PPO) Theory/2. TRPO algorithm introduction I.vtt
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40. Proximal Policy Optimization (PPO) Theory/3. TRPO algorithm introduction II.mp4
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40. Proximal Policy Optimization (PPO) Theory/4. Proximal policy optimization (PPO) algorithm I.mp4
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41. ### PYTHON PROGRAMMING CRASH COURSE ###/1. Python crash course introduction.mp4
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41. ### PYTHON PROGRAMMING CRASH COURSE ###/1. Python crash course introduction.vtt
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42. Appendix #1 - Python Basics/1. First steps in Python.mp4
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42. Appendix #1 - Python Basics/1. First steps in Python.vtt
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42. Appendix #1 - Python Basics/10. Logical operators.mp4
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42. Appendix #1 - Python Basics/11. Loops - for loop.mp4
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42. Appendix #1 - Python Basics/11. Loops - for loop.vtt
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42. Appendix #1 - Python Basics/12. Loops - while loop.mp4
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42. Appendix #1 - Python Basics/12. Loops - while loop.vtt
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42. Appendix #1 - Python Basics/13. What are nested loops.mp4
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42. Appendix #1 - Python Basics/13. What are nested loops.vtt
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42. Appendix #1 - Python Basics/14. Enumerate.mp4
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42. Appendix #1 - Python Basics/14. Enumerate.vtt
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42. Appendix #1 - Python Basics/15. Break and continue.mp4
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42. Appendix #1 - Python Basics/15. Break and continue.vtt
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42. Appendix #1 - Python Basics/16. Calculating Fibonacci-numbers.mp4
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42. Appendix #1 - Python Basics/16. Calculating Fibonacci-numbers.vtt
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42. Appendix #1 - Python Basics/2. What are the basic data types.mp4
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42. Appendix #1 - Python Basics/2. What are the basic data types.vtt
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42. Appendix #1 - Python Basics/3. Booleans.mp4
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42. Appendix #1 - Python Basics/3. Booleans.vtt
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42. Appendix #1 - Python Basics/4. Strings.mp4
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42. Appendix #1 - Python Basics/4. Strings.vtt
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42. Appendix #1 - Python Basics/5. String slicing.mp4
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42. Appendix #1 - Python Basics/5. String slicing.vtt
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42. Appendix #1 - Python Basics/6. Type casting.mp4
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42. Appendix #1 - Python Basics/6. Type casting.vtt
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42. Appendix #1 - Python Basics/7. Operators.mp4
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42. Appendix #1 - Python Basics/7. Operators.vtt
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42. Appendix #1 - Python Basics/8. Conditional statements.mp4
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42. Appendix #1 - Python Basics/8. Conditional statements.vtt
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42. Appendix #1 - Python Basics/9. How to use multiple conditions.mp4
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42. Appendix #1 - Python Basics/9. How to use multiple conditions.vtt
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43. Appendix #2 - Functions/1. What are functions.mp4
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43. Appendix #2 - Functions/1. What are functions.vtt
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43. Appendix #2 - Functions/10. Local vs global variables.mp4
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43. Appendix #2 - Functions/10. Local vs global variables.vtt
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43. Appendix #2 - Functions/11. The __main__ function.mp4
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43. Appendix #2 - Functions/2. Defining functions.mp4
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43. Appendix #2 - Functions/3. Positional arguments and keyword arguments.mp4
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43. Appendix #2 - Functions/3. Positional arguments and keyword arguments.vtt
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43. Appendix #2 - Functions/4. Returning values.mp4
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43. Appendix #2 - Functions/4. Returning values.vtt
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43. Appendix #2 - Functions/5. Returning multiple values.mp4
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43. Appendix #2 - Functions/5. Returning multiple values.vtt
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43. Appendix #2 - Functions/6. Yield operator.mp4
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43. Appendix #2 - Functions/6. Yield operator.vtt
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43. Appendix #2 - Functions/7. Local and global variables.mp4
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43. Appendix #2 - Functions/7. Local and global variables.vtt
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43. Appendix #2 - Functions/8. What are the most relevant built-in functions.mp4
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43. Appendix #2 - Functions/8. What are the most relevant built-in functions.vtt
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43. Appendix #2 - Functions/9. What is recursion.mp4
17 MB
43. Appendix #2 - Functions/9. What is recursion.vtt
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44. Appendix #3 - Data Structures in Python/1. How to measure the running time of algorithms.mp4
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44. Appendix #3 - Data Structures in Python/1. How to measure the running time of algorithms.vtt
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44. Appendix #3 - Data Structures in Python/10. Mutability and immutability.mp4
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44. Appendix #3 - Data Structures in Python/10. Mutability and immutability.vtt
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44. Appendix #3 - Data Structures in Python/11. What are linked list data structures.mp4
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44. Appendix #3 - Data Structures in Python/11. What are linked list data structures.vtt
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44. Appendix #3 - Data Structures in Python/12. Doubly linked list implementation in Python.mp4
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44. Appendix #3 - Data Structures in Python/13. Hashing and O(1) running time complexity.mp4
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44. Appendix #3 - Data Structures in Python/13. Hashing and O(1) running time complexity.vtt
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44. Appendix #3 - Data Structures in Python/14. Dictionaries in Python.mp4
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44. Appendix #3 - Data Structures in Python/14. Dictionaries in Python.vtt
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44. Appendix #3 - Data Structures in Python/15. Sets in Python.mp4
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44. Appendix #3 - Data Structures in Python/15. Sets in Python.vtt
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44. Appendix #3 - Data Structures in Python/16. Sorting.mp4
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44. Appendix #3 - Data Structures in Python/16. Sorting.vtt
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44. Appendix #3 - Data Structures in Python/2. Data structures introduction.mp4
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44. Appendix #3 - Data Structures in Python/2. Data structures introduction.vtt
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44. Appendix #3 - Data Structures in Python/3. What are array data structures I.mp4
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44. Appendix #3 - Data Structures in Python/3. What are array data structures I.vtt
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44. Appendix #3 - Data Structures in Python/4. What are array data structures II.mp4
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44. Appendix #3 - Data Structures in Python/4. What are array data structures II.vtt
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44. Appendix #3 - Data Structures in Python/5. Lists in Python.mp4
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44. Appendix #3 - Data Structures in Python/5. Lists in Python.vtt
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44. Appendix #3 - Data Structures in Python/6. Lists in Python - advanced operations.mp4
19 MB
44. Appendix #3 - Data Structures in Python/6. Lists in Python - advanced operations.vtt
9.5 kB
44. Appendix #3 - Data Structures in Python/7. Lists in Python - list comprehension.mp4
11 MB
44. Appendix #3 - Data Structures in Python/7. Lists in Python - list comprehension.vtt
7.1 kB
44. Appendix #3 - Data Structures in Python/8. (!!!) Python lists and arrays.html
631 B
44. Appendix #3 - Data Structures in Python/9. What are tuples.mp4
7.5 MB
44. Appendix #3 - Data Structures in Python/9. What are tuples.vtt
5.3 kB
45. Appendix #4 - Object Oriented Programming (OOP)/1. What is object oriented programming (OOP).mp4
5.2 MB
45. Appendix #4 - Object Oriented Programming (OOP)/1. What is object oriented programming (OOP).vtt
3.3 kB
45. Appendix #4 - Object Oriented Programming (OOP)/10. Polymorphism and abstraction example.mp4
14 MB
45. Appendix #4 - Object Oriented Programming (OOP)/10. Polymorphism and abstraction example.vtt
7.0 kB
45. Appendix #4 - Object Oriented Programming (OOP)/11. Modules.mp4
11 MB
45. Appendix #4 - Object Oriented Programming (OOP)/11. Modules.vtt
7.6 kB
45. Appendix #4 - Object Oriented Programming (OOP)/12. The __str__ function.mp4
7.7 MB
45. Appendix #4 - Object Oriented Programming (OOP)/12. The __str__ function.vtt
4.1 kB
45. Appendix #4 - Object Oriented Programming (OOP)/13. Comparing objects - overriding functions.mp4
17 MB
45. Appendix #4 - Object Oriented Programming (OOP)/13. Comparing objects - overriding functions.vtt
10 kB
45. Appendix #4 - Object Oriented Programming (OOP)/2. Class and objects basics.mp4
5.4 MB
45. Appendix #4 - Object Oriented Programming (OOP)/2. Class and objects basics.vtt
3.7 kB
45. Appendix #4 - Object Oriented Programming (OOP)/3. Using the constructor.mp4
18 MB
45. Appendix #4 - Object Oriented Programming (OOP)/3. Using the constructor.vtt
7.9 kB
45. Appendix #4 - Object Oriented Programming (OOP)/4. Class variables and instance variables.mp4
15 MB
45. Appendix #4 - Object Oriented Programming (OOP)/4. Class variables and instance variables.vtt
5.6 kB
45. Appendix #4 - Object Oriented Programming (OOP)/5. Private variables and name mangling.mp4
15 MB
45. Appendix #4 - Object Oriented Programming (OOP)/5. Private variables and name mangling.vtt
5.9 kB
45. Appendix #4 - Object Oriented Programming (OOP)/6. What is inheritance in OOP.mp4
8.1 MB
45. Appendix #4 - Object Oriented Programming (OOP)/6. What is inheritance in OOP.vtt
4.8 kB
45. Appendix #4 - Object Oriented Programming (OOP)/7. The super keyword.mp4
9.1 MB
45. Appendix #4 - Object Oriented Programming (OOP)/7. The super keyword.vtt
5.7 kB
45. Appendix #4 - Object Oriented Programming (OOP)/8. Function (method) override.mp4
6.5 MB
45. Appendix #4 - Object Oriented Programming (OOP)/8. Function (method) override.vtt
3.2 kB
45. Appendix #4 - Object Oriented Programming (OOP)/9. What is polymorphism.mp4
16 MB
45. Appendix #4 - Object Oriented Programming (OOP)/9. What is polymorphism.vtt
6.3 kB
46. Appendix #5 - NumPy/1. What is the key advantage of NumPy.mp4
8.2 MB
46. Appendix #5 - NumPy/1. What is the key advantage of NumPy.vtt
5.8 kB
46. Appendix #5 - NumPy/2. Creating and updating arrays.mp4
17 MB
46. Appendix #5 - NumPy/2. Creating and updating arrays.vtt
8.1 kB
46. Appendix #5 - NumPy/3. Dimension of arrays.mp4
18 MB
46. Appendix #5 - NumPy/3. Dimension of arrays.vtt
12 kB
46. Appendix #5 - NumPy/4. Indexes and slicing.mp4
17 MB
46. Appendix #5 - NumPy/4. Indexes and slicing.vtt
11 kB
46. Appendix #5 - NumPy/5. Types.mp4
9.9 MB
46. Appendix #5 - NumPy/5. Types.vtt
5.8 kB
46. Appendix #5 - NumPy/6. Reshape.mp4
17 MB
46. Appendix #5 - NumPy/6. Reshape.vtt
10 kB
46. Appendix #5 - NumPy/7. Stacking and merging arrays.mp4
22 MB
46. Appendix #5 - NumPy/7. Stacking and merging arrays.vtt
8.1 kB
46. Appendix #5 - NumPy/8. Filter.mp4
7.7 MB
46. Appendix #5 - NumPy/8. Filter.vtt
4.8 kB
46. Appendix #5 - NumPy/9. Running time comparison arrays and lists.html
1.3 kB
47. COURSE MATERIALS (DOWNLOADS)/1. Course materials (source code and slides).html
66 B
47. COURSE MATERIALS (DOWNLOADS)/1. PythonMachineLearning.zip
21 MB