Udemy - Machine Learning and Deep Learning Bootcamp in Python (1.2025)

Creation Time	Sept. 9, 2025, 2:29 p.m.
Last Access Time	March 29, 2026, 12:53 a.m.
File Size	5.9 GB
Keywords	and 1 Python - Deep Udemy Machine 2025 Learning in Bootcamp
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Total Files	625
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37. Q Learning Implementation (Tic Tac Toe)/6. Tic tac toe with Q learning implementation VI.mp4 110.8 MB
05. Linear Regression/4. Linear regression implementation I.mp4 92.5 MB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/6. Stochastic gradient descent implementation I.mp4 81.0 MB
09. Naive Bayes Classifier/6. Naive Bayes example - clustering news.mp4 79.8 MB
39. Deep Q Learning Implementation (Tic Tac Toe)/3. Tic Tac Toe with deep Q learning implementation III.mp4 72.9 MB
06. Logistic Regression/4. Logistic regression example II- credit scoring.mp4 72.0 MB
06. Logistic Regression/3. Logistic regression example I - sigmoid function.mp4 65.9 MB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/9. ADAGrad implementation.mp4 59.7 MB
25. Convolutional Neural Networks (CNNs) Implementation/2. Handwritten digit classification II.mp4 58.1 MB
08. K-Nearest Neighbor Classifier/6. K-nearest neighbor implementation II.mp4 56.5 MB
09. Naive Bayes Classifier/1. What is the naive Bayes classifier.mp4 55.0 MB
15. Clustering/3. K-means clustering - text clustering.mp4 54.8 MB
25. Convolutional Neural Networks (CNNs) Implementation/1. Handwritten digit classification I.mp4 54.3 MB
16. Machine Learning Project I - Face Recognition/4. Understanding eigenfaces.mp4 53.6 MB
35. Exploration vs. Exploitation Problem/3. N-armed bandit problem implementation.mp4 53.3 MB
28. Recurrent Neural Networks (RNNs) Implementation/3. Time series analysis example III.mp4 52.6 MB
26. Machine Learning Project III - Identifying Objects with CNNs/5. Tuning the parameters - regularization.mp4 51.9 MB
34. Markov Decision Process (MDP) Theory/3. Markov decision processes - equations.mp4 49.6 MB
37. Q Learning Implementation (Tic Tac Toe)/8. Tic tac toe with Q learning implementation VIII.mp4 48.6 MB
06. Logistic Regression/1. What is logistic regression.mp4 48.4 MB
26. Machine Learning Project III - Identifying Objects with CNNs/3. Fitting the model.mp4 47.9 MB
39. Deep Q Learning Implementation (Tic Tac Toe)/4. Tic Tac Toe with deep Q learning implementation IV.mp4 46.6 MB
31. Generative Adversarial Networks (GANs) Implementation/5. GAN implementation V.mp4 45.4 MB
37. Q Learning Implementation (Tic Tac Toe)/4. Tic tac toe with Q learning implementation IV.mp4 43.4 MB
30. Generative Adversarial Networks (GANs) Theory/4. Training GANs.mp4 43.4 MB
29. Transformers/5. Understanding positional encoding.mp4 42.5 MB
22. Deep Neural Networks Implementation/2. Deep neural network implementation II.mp4 42.1 MB
03. Artificial Intelligence Basics/1. Why to learn artificial intelligence and machine learning.mp4 41.0 MB
03. Artificial Intelligence Basics/2. Types of artificial intelligence learning.mp4 40.8 MB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/7. Stochastic gradient descent implementation II.mp4 39.6 MB
37. Q Learning Implementation (Tic Tac Toe)/5. Tic tac toe with Q learning implementation V.mp4 39.2 MB
39. Deep Q Learning Implementation (Tic Tac Toe)/2. Tic Tac Toe with deep Q learning implementation II.mp4 39.2 MB
13. Boosting/6. Boosting implementation II -wine classification.mp4 38.7 MB
14. Principal Component Analysis (PCA)/3. Principal component analysis example I.mp4 38.3 MB
31. Generative Adversarial Networks (GANs) Implementation/2. GAN implementation II.mp4 38.2 MB
10. Support Vector Machines (SVMs)/5. Support vector machine example I - simple.mp4 37.5 MB
13. Boosting/4. Boosting introduction - final formula.mp4 36.8 MB
31. Generative Adversarial Networks (GANs) Implementation/3. GAN implementation III.mp4 36.6 MB
29. Transformers/6. The self-attention mechanism I.mp4 36.4 MB
40. Proximal Policy Optimization (PPO) Theory/2. TRPO algorithm introduction I.mp4 35.8 MB
03. Artificial Intelligence Basics/3. Fundamentals of statistics.mp4 35.6 MB
22. Deep Neural Networks Implementation/4. Multiclass classification implementation I.mp4 35.4 MB
25. Convolutional Neural Networks (CNNs) Implementation/3. Handwritten digit classification III.mp4 35.2 MB
05. Linear Regression/2. Linear regression theory - optimization.mp4 34.6 MB
10. Support Vector Machines (SVMs)/4. Kernel functions.mp4 34.1 MB
15. Clustering/8. Hierarchical clustering - market segmentation.mp4 33.8 MB
11. Decision Trees/6. Decision trees implementation I.mp4 33.7 MB
05. Linear Regression/1. What is linear regression.mp4 33.6 MB
12. Random Forest Classifier/6. Random forests example II - credit scoring.mp4 33.1 MB
27. Recurrent Neural Networks (RNNs) Theory/6. Long-short term memory (LSTM) model theory II.mp4 32.1 MB
12. Random Forest Classifier/7. Random forests example III - OCR parameter tuning.mp4 31.9 MB
05. Linear Regression/3. Linear regression theory - gradient descent.mp4 31.7 MB
09. Naive Bayes Classifier/4. What is text clustering.mp4 31.5 MB
22. Deep Neural Networks Implementation/5. Multiclass classification implementation II.mp4 31.5 MB
13. Boosting/5. Boosting implementation I - iris dataset.mp4 31.1 MB
31. Generative Adversarial Networks (GANs) Implementation/4. GAN implementation IV.mp4 30.5 MB
10. Support Vector Machines (SVMs)/2. Linearly separable problems.mp4 30.3 MB
39. Deep Q Learning Implementation (Tic Tac Toe)/5. Tic Tac Toe with deep Q learning implementation V.mp4 29.8 MB
21. Deep Neural Networks Theory/5. Loss functions revisited.mp4 29.5 MB
19. Simple Feed-Forward Neural Network Implementation/3. Credit scoring with simple neural networks.mp4 29.0 MB
18. Feed-Forward Neural Network Theory/8. Gradient descent with backpropagation.mp4 28.8 MB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/5. Stochastic gradient descent introduction.mp4 28.5 MB
34. Markov Decision Process (MDP) Theory/4. Markov decision processes - illustration.mp4 28.2 MB
30. Generative Adversarial Networks (GANs) Theory/2. GANs fundamentals.mp4 28.2 MB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/3. Gradient descent implementation.mp4 28.1 MB
29. Transformers/2. Understanding word embeddings I.mp4 27.9 MB
16. Machine Learning Project I - Face Recognition/1. The Olivetti dataset.mp4 27.2 MB
11. Decision Trees/4. The Gini-index approach.mp4 27.1 MB
14. Principal Component Analysis (PCA)/4. Principal component analysis example II.mp4 26.8 MB
11. Decision Trees/2. Entropy and information gain.mp4 26.8 MB
40. Proximal Policy Optimization (PPO) Theory/3. TRPO algorithm introduction II.mp4 26.6 MB
37. Q Learning Implementation (Tic Tac Toe)/7. Tic tac toe with Q learning implementation VII.mp4 26.4 MB
22. Deep Neural Networks Implementation/3. Deep neural network implementation III.mp4 26.1 MB
44. Appendix #3 - Data Structures in Python/15. Sets in Python.mp4 26.0 MB
29. Transformers/7. The self-attention mechanism II.mp4 25.6 MB
12. Random Forest Classifier/3. Bagging introduction.mp4 25.6 MB
10. Support Vector Machines (SVMs)/7. Support vector machines example III - parameter tuning.mp4 25.4 MB
29. Transformers/4. Tokenization and word embeddings.mp4 25.3 MB
29. Transformers/10. The neural network layer.mp4 25.3 MB
30. Generative Adversarial Networks (GANs) Theory/3. GANs learning procedure.mp4 25.3 MB
38. Deep Q Learning Theory/3. Remember and replay.mp4 25.2 MB
28. Recurrent Neural Networks (RNNs) Implementation/5. Time series analysis example V.mp4 25.2 MB
27. Recurrent Neural Networks (RNNs) Theory/5. Long-short term memory (LSTM) model theory I.mp4 24.9 MB
29. Transformers/9. Multi-head architecture.mp4 24.6 MB
07. Cross Validation/1. What is cross validation.mp4 24.4 MB
34. Markov Decision Process (MDP) Theory/7. What is value iteration.mp4 24.2 MB
44. Appendix #3 - Data Structures in Python/16. Sorting.mp4 23.8 MB
19. Simple Feed-Forward Neural Network Implementation/1. Simple neural network implementation - XOR problem.mp4 23.8 MB
34. Markov Decision Process (MDP) Theory/1. What are Markov decision processes.mp4 23.8 MB
29. Transformers/3. Understanding word embeddings II.mp4 23.5 MB
37. Q Learning Implementation (Tic Tac Toe)/3. Tic tac toe with Q learning implementation III.mp4 23.5 MB
24. Convolutional Neural Networks (CNNs) Theory/8. Colored images and tensors.mp4 23.4 MB
44. Appendix #3 - Data Structures in Python/13. Hashing and O(1) running time complexity.mp4 23.1 MB
10. Support Vector Machines (SVMs)/3. Non-linearly separable problems.mp4 23.0 MB
02. Environment Setup/2. Installing PyCharm.mp4 22.8 MB
06. Logistic Regression/2. Logistic regression and maximum likelihood estimation.mp4 22.7 MB
43. Appendix #2 - Functions/3. Positional arguments and keyword arguments.mp4 22.2 MB
10. Support Vector Machines (SVMs)/8. Support vector machine example IV - digit recognition.mp4 22.0 MB
46. Appendix #5 - NumPy/7. Stacking and merging arrays.mp4 21.9 MB
16. Machine Learning Project I - Face Recognition/2. Understanding the dataset.mp4 21.5 MB
16. Machine Learning Project I - Face Recognition/3. Finding optimal number of principal components (eigenvectors).mp4 21.5 MB
36. Q Learning Theory/3. Q learning illustration.mp4 21.4 MB
47. COURSE MATERIALS (DOWNLOADS)/1. PythonMachineLearning.zip 21.2 MB
06. Logistic Regression/5. Logistic regression example III - credit scoring.mp4 21.0 MB
08. K-Nearest Neighbor Classifier/3. Distance metrics - Euclidean-distance.mp4 21.0 MB
23. Machine Learning Project II - Smile Detector/3. Reading the images and constructing the dataset II.mp4 20.8 MB
44. Appendix #3 - Data Structures in Python/11. What are linked list data structures.mp4 20.7 MB
11. Decision Trees/1. What are decision trees.mp4 20.7 MB
15. Clustering/5. DBSCAN example.mp4 20.6 MB
12. Random Forest Classifier/5. Random forests example I - iris dataset.mp4 20.3 MB
27. Recurrent Neural Networks (RNNs) Theory/2. Recurrent neural networks basics.mp4 20.2 MB
15. Clustering/7. Hierarchical clustering example.mp4 20.2 MB
10. Support Vector Machines (SVMs)/1. What are Support Vector Machines (SVMs).mp4 20.1 MB
14. Principal Component Analysis (PCA)/1. Principal component analysis (PCA) introduction.mp4 20.1 MB
26. Machine Learning Project III - Identifying Objects with CNNs/4. What is batch normalization.mp4 19.9 MB
23. Machine Learning Project II - Smile Detector/2. Reading the images and constructing the dataset I.mp4 19.6 MB
35. Exploration vs. Exploitation Problem/2. N-armed bandit problem introduction.mp4 19.6 MB
44. Appendix #3 - Data Structures in Python/14. Dictionaries in Python.mp4 19.4 MB
15. Clustering/2. K-means clustering example.mp4 19.1 MB
18. Feed-Forward Neural Network Theory/4. Neural networks - the big picture.mp4 19.0 MB
16. Machine Learning Project I - Face Recognition/5. Constructing the machine learning models.mp4 19.0 MB
18. Feed-Forward Neural Network Theory/9. Backpropagation explained.mp4 18.9 MB
37. Q Learning Implementation (Tic Tac Toe)/1. Tic tac toe with Q learning implementation I.mp4 18.9 MB
26. Machine Learning Project III - Identifying Objects with CNNs/1. What is the CIFAR-10 dataset.mp4 18.8 MB
44. Appendix #3 - Data Structures in Python/6. Lists in Python - advanced operations.mp4 18.6 MB
46. Appendix #5 - NumPy/3. Dimension of arrays.mp4 18.4 MB
44. Appendix #3 - Data Structures in Python/1. How to measure the running time of algorithms.mp4 18.3 MB
10. Support Vector Machines (SVMs)/6. Support vector machine example II - iris dataset.mp4 18.1 MB
40. Proximal Policy Optimization (PPO) Theory/7. Proximal policy optimization (PPO) algorithm IV.mp4 17.9 MB
45. Appendix #4 - Object Oriented Programming (OOP)/3. Using the constructor.mp4 17.8 MB
11. Decision Trees/3. Example of how to construct decision trees.mp4 17.7 MB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/12. ADAM optimizer implementation.mp4 17.6 MB
22. Deep Neural Networks Implementation/1. Deep neural network implementation I.mp4 17.5 MB
43. Appendix #2 - Functions/9. What is recursion.mp4 17.4 MB
21. Deep Neural Networks Theory/3. Activation functions and weight initializations.mp4 17.2 MB
45. Appendix #4 - Object Oriented Programming (OOP)/13. Comparing objects - overriding functions.mp4 17.1 MB
24. Convolutional Neural Networks (CNNs) Theory/5. Convolutional neural networks - flattening and the neural network layer.mp4 17.1 MB
46. Appendix #5 - NumPy/6. Reshape.mp4 17.0 MB
37. Q Learning Implementation (Tic Tac Toe)/2. Tic tac toe with Q learning implementation II.mp4 16.9 MB
24. Convolutional Neural Networks (CNNs) Theory/2. Feature selection with kernels.mp4 16.8 MB
46. Appendix #5 - NumPy/2. Creating and updating arrays.mp4 16.8 MB
46. Appendix #5 - NumPy/4. Indexes and slicing.mp4 16.7 MB
15. Clustering/1. K-means clustering introduction.mp4 16.6 MB
15. Clustering/6. Hierarchical clustering introduction.mp4 16.6 MB
28. Recurrent Neural Networks (RNNs) Implementation/2. Time series analysis example II.mp4 16.5 MB
08. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.mp4 16.3 MB
11. Decision Trees/8. Decision tree implementation III - identifying cancer.mp4 16.2 MB
45. Appendix #4 - Object Oriented Programming (OOP)/9. What is polymorphism.mp4 16.2 MB
38. Deep Q Learning Theory/1. What is deep Q learning.mp4 16.0 MB
42. Appendix #1 - Python Basics/9. How to use multiple conditions.mp4 16.0 MB
08. K-Nearest Neighbor Classifier/2. Concept of lazy learning.mp4 16.0 MB
13. Boosting/1. What is boosting.mp4 15.7 MB
28. Recurrent Neural Networks (RNNs) Implementation/6. Time series analysis example VI.mp4 15.7 MB
36. Q Learning Theory/2. Q learning introduction - the algorithm.mp4 15.5 MB
34. Markov Decision Process (MDP) Theory/5. Bellman-equation.mp4 15.4 MB
07. Cross Validation/2. Cross validation example.mp4 15.4 MB
45. Appendix #4 - Object Oriented Programming (OOP)/5. Private variables and name mangling.mp4 15.3 MB
12. Random Forest Classifier/1. What is the bias-variance tradeoff.mp4 15.3 MB
18. Feed-Forward Neural Network Theory/7. Optimization with gradient descent.mp4 15.3 MB
10. Support Vector Machines (SVMs)/9. Support vector machine example V - digit recognition.mp4 14.9 MB
21. Deep Neural Networks Theory/7. Normalization, batches and epochs.mp4 14.8 MB
08. K-Nearest Neighbor Classifier/4. Bias and variance trade-off.mp4 14.7 MB
09. Naive Bayes Classifier/5. Text clustering - inverse document frequency (TF-IDF).mp4 14.7 MB
45. Appendix #4 - Object Oriented Programming (OOP)/4. Class variables and instance variables.mp4 14.7 MB
21. Deep Neural Networks Theory/6. Gradient descent and stochastic gradient descent.mp4 14.7 MB
42. Appendix #1 - Python Basics/4. Strings.mp4 14.6 MB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/10. What is RMSProp.mp4 14.5 MB
34. Markov Decision Process (MDP) Theory/2. Markov decision processes basics.mp4 14.2 MB
11. Decision Trees/7. Decision trees implementation II - parameter tuning.mp4 14.1 MB
40. Proximal Policy Optimization (PPO) Theory/5. Proximal policy optimization (PPO) algorithm II.mp4 13.9 MB
23. Machine Learning Project II - Smile Detector/5. Evaluating and testing the model.mp4 13.9 MB
30. Generative Adversarial Networks (GANs) Theory/1. What is a generative adversarial network (GAN).mp4 13.9 MB
21. Deep Neural Networks Theory/4. Softmax activation function.mp4 13.9 MB
45. Appendix #4 - Object Oriented Programming (OOP)/10. Polymorphism and abstraction example.mp4 13.7 MB
08. K-Nearest Neighbor Classifier/1. What is the k-nearest neighbor classifier.mp4 13.7 MB
13. Boosting/3. Boosting introduction - equations.mp4 13.4 MB
39. Deep Q Learning Implementation (Tic Tac Toe)/1. Tic Tac Toe with deep Q learning implementation I.mp4 13.4 MB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/2. What is gradient descent.mp4 13.3 MB
31. Generative Adversarial Networks (GANs) Implementation/1. GAN implementation I.mp4 13.2 MB
08. K-Nearest Neighbor Classifier/7. K-nearest neighbor implementation III.mp4 13.0 MB
38. Deep Q Learning Theory/4. Why use an additional target neural network.mp4 12.7 MB
42. Appendix #1 - Python Basics/5. String slicing.mp4 12.7 MB
14. Principal Component Analysis (PCA)/2. Mathematical formulation of principal components.mp4 12.6 MB
19. Simple Feed-Forward Neural Network Implementation/2. Linearly and non-linearly separable problems.mp4 12.6 MB
05. Linear Regression/5. Linear regression implementation II.mp4 12.6 MB
24. Convolutional Neural Networks (CNNs) Theory/1. What are convolutional neural networks.mp4 12.5 MB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/8. What is ADAGrad.mp4 12.4 MB
44. Appendix #3 - Data Structures in Python/4. What are array data structures II.mp4 12.3 MB
44. Appendix #3 - Data Structures in Python/3. What are array data structures I.mp4 12.3 MB
18. Feed-Forward Neural Network Theory/3. Why to use activation functions.mp4 12.1 MB
35. Exploration vs. Exploitation Problem/4. Applications AB testing in marketing.mp4 12.1 MB
24. Convolutional Neural Networks (CNNs) Theory/9. Evolution of CNN architectures.mp4 11.9 MB
36. Q Learning Theory/1. What is Q learning.mp4 11.8 MB
18. Feed-Forward Neural Network Theory/6. How to measure the error of the network.mp4 11.7 MB
31. Generative Adversarial Networks (GANs) Implementation/6. GAN implementation VI.mp4 11.7 MB
27. Recurrent Neural Networks (RNNs) Theory/1. Why do recurrent neural networks are important.mp4 11.7 MB
44. Appendix #3 - Data Structures in Python/12. Doubly linked list implementation in Python.mp4 11.4 MB
21. Deep Neural Networks Theory/2. Vanishing and exploding gradients problem.mp4 11.4 MB
44. Appendix #3 - Data Structures in Python/7. Lists in Python - list comprehension.mp4 11.4 MB
15. Clustering/4. DBSCAN introduction.mp4 11.4 MB
16. Machine Learning Project I - Face Recognition/6. Using cross-validation.mp4 11.3 MB
18. Feed-Forward Neural Network Theory/1. What are feed-forward neural networks.mp4 11.3 MB
40. Proximal Policy Optimization (PPO) Theory/4. Proximal policy optimization (PPO) algorithm I.mp4 11.3 MB
13. Boosting/2. Boosting introduction - illustration.mp4 11.2 MB
09. Naive Bayes Classifier/3. Naive Bayes classifier implementation.mp4 11.1 MB
18. Feed-Forward Neural Network Theory/2. Artificial neural networks - the model.mp4 11.1 MB
24. Convolutional Neural Networks (CNNs) Theory/7. How do you update the kernel weights exactly.mp4 11.1 MB
45. Appendix #4 - Object Oriented Programming (OOP)/11. Modules.mp4 11.0 MB
27. Recurrent Neural Networks (RNNs) Theory/3. The backpropagation through time (BPTT) algorithm.mp4 10.9 MB
24. Convolutional Neural Networks (CNNs) Theory/3. Convolutional operation example.mp4 10.8 MB
06. Logistic Regression/6. Why is logistic regression linear.mp4 10.8 MB
42. Appendix #1 - Python Basics/7. Operators.mp4 10.7 MB
12. Random Forest Classifier/4. Random forest classifier introduction.mp4 10.6 MB
21. Deep Neural Networks Theory/8. Regularization.mp4 10.6 MB
24. Convolutional Neural Networks (CNNs) Theory/6. Convolutional neural networks - illustration.mp4 10.6 MB
44. Appendix #3 - Data Structures in Python/5. Lists in Python.mp4 10.5 MB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/4. Gradient descent with momentum.mp4 10.3 MB
40. Proximal Policy Optimization (PPO) Theory/1. What are the problems with deep Q learning.mp4 10.0 MB
46. Appendix #5 - NumPy/5. Types.mp4 9.9 MB
42. Appendix #1 - Python Basics/15. Break and continue.mp4 9.9 MB
02. Environment Setup/3. Installing TensorFlow and Keras.mp4 9.7 MB
43. Appendix #2 - Functions/2. Defining functions.mp4 9.6 MB
42. Appendix #1 - Python Basics/11. Loops - for loop.mp4 9.6 MB
27. Recurrent Neural Networks (RNNs) Theory/4. Vanishing and exploding gradients problem.mp4 9.5 MB
09. Naive Bayes Classifier/2. Naive Bayes classifier illustration.mp4 9.2 MB
43. Appendix #2 - Functions/6. Yield operator.mp4 9.2 MB
45. Appendix #4 - Object Oriented Programming (OOP)/7. The super keyword.mp4 9.1 MB
21. Deep Neural Networks Theory/1. What are deep neural networks.mp4 8.7 MB
44. Appendix #3 - Data Structures in Python/10. Mutability and immutability.mp4 8.7 MB
23. Machine Learning Project II - Smile Detector/4. Building the deep neural network model.mp4 8.6 MB
42. Appendix #1 - Python Basics/8. Conditional statements.mp4 8.6 MB
28. Recurrent Neural Networks (RNNs) Implementation/4. Time series analysis example IV.mp4 8.5 MB
24. Convolutional Neural Networks (CNNs) Theory/4. Convolutional neural networks - pooling.mp4 8.3 MB
42. Appendix #1 - Python Basics/6. Type casting.mp4 8.2 MB
46. Appendix #5 - NumPy/1. What is the key advantage of NumPy.mp4 8.2 MB
45. Appendix #4 - Object Oriented Programming (OOP)/6. What is inheritance in OOP.mp4 8.1 MB
43. Appendix #2 - Functions/1. What are functions.mp4 8.1 MB
42. Appendix #1 - Python Basics/10. Logical operators.mp4 8.1 MB
01. Introduction/1. Introduction.mp4 8.0 MB
43. Appendix #2 - Functions/10. Local vs global variables.mp4 7.8 MB
11. Decision Trees/5. Decision trees introduction - pros and cons.mp4 7.8 MB
42. Appendix #1 - Python Basics/2. What are the basic data types.mp4 7.7 MB
42. Appendix #1 - Python Basics/14. Enumerate.mp4 7.7 MB
18. Feed-Forward Neural Network Theory/5. Using bias nodes in the neural network.mp4 7.7 MB
45. Appendix #4 - Object Oriented Programming (OOP)/12. The __str__ function.mp4 7.7 MB
46. Appendix #5 - NumPy/8. Filter.mp4 7.7 MB
35. Exploration vs. Exploitation Problem/1. Exploration vs exploitation problem.mp4 7.7 MB
43. Appendix #2 - Functions/8. What are the most relevant built-in functions.mp4 7.6 MB
42. Appendix #1 - Python Basics/12. Loops - while loop.mp4 7.6 MB
44. Appendix #3 - Data Structures in Python/9. What are tuples.mp4 7.5 MB
27. Recurrent Neural Networks (RNNs) Theory/9. Gated recurrent units (GRUs).mp4 7.5 MB
29. Transformers/8. What is masking.mp4 7.5 MB
29. Transformers/11. Understanding the training of transformers.mp4 7.4 MB
42. Appendix #1 - Python Basics/1. First steps in Python.mp4 7.4 MB
27. Recurrent Neural Networks (RNNs) Theory/7. Long-short term memory (LSTM) forward pass example.mp4 7.3 MB
43. Appendix #2 - Functions/11. The __main__ function.mp4 7.3 MB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/11. ADAM optimizer introduction.mp4 7.2 MB
34. Markov Decision Process (MDP) Theory/8. What is policy iteration.mp4 7.0 MB
28. Recurrent Neural Networks (RNNs) Implementation/1. Time series analysis example I.mp4 6.9 MB
13. Boosting/7. Boosting vs. bagging.mp4 6.9 MB
44. Appendix #3 - Data Structures in Python/2. Data structures introduction.mp4 6.7 MB
33. ### REINFORCEMENT LEARNING ###/2. Applications of reinforcement learning.mp4 6.6 MB
45. Appendix #4 - Object Oriented Programming (OOP)/8. Function (method) override.mp4 6.5 MB
26. Machine Learning Project III - Identifying Objects with CNNs/2. Preprocessing the data.mp4 6.2 MB
38. Deep Q Learning Theory/2. Deep Q learning and ε-greedy strategy.mp4 6.2 MB
43. Appendix #2 - Functions/5. Returning multiple values.mp4 6.0 MB
10. Support Vector Machines (SVMs)/10. Advantages and disadvantages.mp4 6.0 MB
42. Appendix #1 - Python Basics/13. What are nested loops.mp4 5.9 MB
29. Transformers/1. Transformers chapter overview.mp4 5.8 MB
40. Proximal Policy Optimization (PPO) Theory/6. Proximal policy optimization (PPO) algorithm III.mp4 5.8 MB
20. Deep Learning/1. Types of neural networks.mp4 5.8 MB
29. Transformers/12. What is ChatGPT.mp4 5.7 MB
34. Markov Decision Process (MDP) Theory/6. How to solve MDP problems.mp4 5.7 MB
27. Recurrent Neural Networks (RNNs) Theory/8. Long-short term memory (LSTM) backpropagation example.mp4 5.7 MB
45. Appendix #4 - Object Oriented Programming (OOP)/2. Class and objects basics.mp4 5.4 MB
45. Appendix #4 - Object Oriented Programming (OOP)/1. What is object oriented programming (OOP).mp4 5.2 MB
02. Environment Setup/1. Installing Python.mp4 4.8 MB
43. Appendix #2 - Functions/7. Local and global variables.mp4 4.3 MB
43. Appendix #2 - Functions/4. Returning values.mp4 4.1 MB
42. Appendix #1 - Python Basics/16. Calculating Fibonacci-numbers.mp4 4.0 MB
41. ### PYTHON PROGRAMMING CRASH COURSE ###/1. Python crash course introduction.mp4 4.0 MB
12. Random Forest Classifier/2. Pruning introduction.mp4 3.7 MB
42. Appendix #1 - Python Basics/3. Booleans.mp4 3.5 MB
23. Machine Learning Project II - Smile Detector/1. Understanding the classification problem.mp4 3.4 MB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/6. Stochastic gradient descent implementation I.vtt 30.4 KB
29. Transformers/5. Understanding positional encoding.vtt 20.0 KB
21. Deep Neural Networks Theory/10.13 Deep Neural Networks Quiz.html 17.9 KB
18. Feed-Forward Neural Network Theory/11.12 Feed-Forward Neural Networks Quiz.html 17.8 KB
34. Markov Decision Process (MDP) Theory/10.18 Reinforcement Learning Basics Quiz.html 17.8 KB
27. Recurrent Neural Networks (RNNs) Theory/11.15 Recurrent Neural Networks Quiz.html 17.8 KB
08. K-Nearest Neighbor Classifier/9.4 K-Nearest Neighbor Quiz.html 17.5 KB
29. Transformers/14.16 Transformers Quiz.html 17.5 KB
40. Proximal Policy Optimization (PPO) Theory/8.22 PPO Quiz.html 17.3 KB
10. Support Vector Machines (SVMs)/12.6 Support Vector Machines Quiz.html 17.2 KB
30. Generative Adversarial Networks (GANs) Theory/6.17 GANs Quiz.html 17.2 KB
05. Linear Regression/7.1 Linear Regression Quiz.html 17.2 KB
24. Convolutional Neural Networks (CNNs) Theory/12.14 Convolutional Neural Networks (CNNs) Quiz.html 17.1 KB
12. Random Forest Classifier/9.8 Random Forests Quiz.html 17.0 KB
11. Decision Trees/10.7 Decision Trees Quiz.html 17.0 KB
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37. Q Learning Implementation (Tic Tac Toe)/6. Tic tac toe with Q learning implementation VI.vtt 13.8 KB
10. Support Vector Machines (SVMs)/4. Kernel functions.vtt 13.5 KB
12. Random Forest Classifier/7. Random forests example III - OCR parameter tuning.vtt 13.1 KB
18. Feed-Forward Neural Network Theory/8. Gradient descent with backpropagation.vtt 13.1 KB
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/3. Gradient descent implementation.vtt 12.7 KB
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30. Generative Adversarial Networks (GANs) Theory/4. Training GANs.vtt 12.5 KB
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14. Principal Component Analysis (PCA)/1. Principal component analysis (PCA) introduction.vtt 12.5 KB
10. Support Vector Machines (SVMs)/5. Support vector machine example I - simple.vtt 12.5 KB
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29. Transformers/2. Understanding word embeddings I.vtt 12.4 KB
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39. Deep Q Learning Implementation (Tic Tac Toe)/3. Tic Tac Toe with deep Q learning implementation III.vtt 11.8 KB
18. Feed-Forward Neural Network Theory/4. Neural networks - the big picture.vtt 11.7 KB
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/12. ADAM optimizer implementation.vtt 11.7 KB
05. Linear Regression/2. Linear regression theory - optimization.vtt 11.6 KB
26. Machine Learning Project III - Identifying Objects with CNNs/3. Fitting the model.vtt 11.6 KB
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15. Clustering/3. K-means clustering - text clustering.vtt 11.3 KB
40. Proximal Policy Optimization (PPO) Theory/2. TRPO algorithm introduction I.vtt 11.3 KB
44. Appendix #3 - Data Structures in Python/15. Sets in Python.vtt 11.2 KB
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35. Exploration vs. Exploitation Problem/2. N-armed bandit problem introduction.vtt 11.1 KB
19. Simple Feed-Forward Neural Network Implementation/3. Credit scoring with simple neural networks.vtt 11.1 KB
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10. Support Vector Machines (SVMs)/8. Support vector machine example IV - digit recognition.vtt 11.0 KB
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15. Clustering/8. Hierarchical clustering - market segmentation.vtt 10.7 KB
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19. Simple Feed-Forward Neural Network Implementation/1. Simple neural network implementation - XOR problem.vtt 10.5 KB
11. Decision Trees/2. Entropy and information gain.vtt 10.5 KB
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29. Transformers/3. Understanding word embeddings II.vtt 10.4 KB
29. Transformers/7. The self-attention mechanism II.vtt 10.4 KB
03. Artificial Intelligence Basics/2. Types of artificial intelligence learning.vtt 10.4 KB
29. Transformers/4. Tokenization and word embeddings.vtt 10.3 KB
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36. Q Learning Theory/2. Q learning introduction - the algorithm.vtt 9.6 KB
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38. Deep Q Learning Theory/1. What is deep Q learning.vtt 9.4 KB
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22. Deep Neural Networks Implementation/4. Multiclass classification implementation I.vtt 9.3 KB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/2. What is gradient descent.vtt 9.2 KB
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37. Q Learning Implementation (Tic Tac Toe)/2. Tic tac toe with Q learning implementation II.vtt 8.8 KB
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03. Artificial Intelligence Basics/3. Fundamentals of statistics.vtt 8.8 KB
30. Generative Adversarial Networks (GANs) Theory/2. GANs fundamentals.vtt 8.8 KB
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11. Decision Trees/6. Decision trees implementation I.vtt 8.6 KB
13. Boosting/5. Boosting implementation I - iris dataset.vtt 8.5 KB
42. Appendix #1 - Python Basics/5. String slicing.vtt 8.3 KB
37. Q Learning Implementation (Tic Tac Toe)/3. Tic tac toe with Q learning implementation III.vtt 8.3 KB
24. Convolutional Neural Networks (CNNs) Theory/5. Convolutional neural networks - flattening and the neural network layer.vtt 8.3 KB
31. Generative Adversarial Networks (GANs) Implementation/4. GAN implementation IV.vtt 8.3 KB
18. Feed-Forward Neural Network Theory/3. Why to use activation functions.vtt 8.3 KB
34. Markov Decision Process (MDP) Theory/7. What is value iteration.vtt 8.2 KB
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08. K-Nearest Neighbor Classifier/1. What is the k-nearest neighbor classifier.vtt 8.2 KB
37. Q Learning Implementation (Tic Tac Toe)/4. Tic tac toe with Q learning implementation IV.vtt 8.1 KB
11. Decision Trees/3. Example of how to construct decision trees.vtt 8.1 KB
24. Convolutional Neural Networks (CNNs) Theory/3. Convolutional operation example.vtt 8.1 KB
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30. Generative Adversarial Networks (GANs) Theory/3. GANs learning procedure.vtt 8.1 KB
29. Transformers/9. Multi-head architecture.vtt 8.1 KB
46. Appendix #5 - NumPy/2. Creating and updating arrays.vtt 8.1 KB
10. Support Vector Machines (SVMs)/7. Support vector machines example III - parameter tuning.vtt 8.1 KB
03. Artificial Intelligence Basics/1. Why to learn artificial intelligence and machine learning.vtt 8.1 KB
22. Deep Neural Networks Implementation/2. Deep neural network implementation II.vtt 8.0 KB
31. Generative Adversarial Networks (GANs) Implementation/2. GAN implementation II.vtt 8.0 KB
24. Convolutional Neural Networks (CNNs) Theory/8. Colored images and tensors.vtt 7.9 KB
38. Deep Q Learning Theory/4. Why use an additional target neural network.vtt 7.9 KB
45. Appendix #4 - Object Oriented Programming (OOP)/3. Using the constructor.vtt 7.9 KB
42. Appendix #1 - Python Basics/11. Loops - for loop.vtt 7.8 KB
26. Machine Learning Project III - Identifying Objects with CNNs/1. What is the CIFAR-10 dataset.vtt 7.8 KB
08. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.vtt 7.8 KB
25. Convolutional Neural Networks (CNNs) Implementation/3. Handwritten digit classification III.vtt 7.8 KB
10. Support Vector Machines (SVMs)/1. What are Support Vector Machines (SVMs).vtt 7.8 KB
22. Deep Neural Networks Implementation/1. Deep neural network implementation I.vtt 7.7 KB
37. Q Learning Implementation (Tic Tac Toe)/8. Tic tac toe with Q learning implementation VIII.vtt 7.7 KB
39. Deep Q Learning Implementation (Tic Tac Toe)/2. Tic Tac Toe with deep Q learning implementation II.vtt 7.7 KB
38. Deep Q Learning Theory/3. Remember and replay.vtt 7.7 KB
28. Recurrent Neural Networks (RNNs) Implementation/3. Time series analysis example III.vtt 7.6 KB
29. Transformers/10. The neural network layer.vtt 7.6 KB
45. Appendix #4 - Object Oriented Programming (OOP)/11. Modules.vtt 7.6 KB
12. Random Forest Classifier/5. Random forests example I - iris dataset.vtt 7.6 KB
11. Decision Trees/4. The Gini-index approach.vtt 7.5 KB
37. Q Learning Implementation (Tic Tac Toe)/8. QLearningTicTacToe.py 7.5 KB
24. Convolutional Neural Networks (CNNs) Theory/1. What are convolutional neural networks.vtt 7.4 KB
36. Q Learning Theory/1. What is Q learning.vtt 7.4 KB
42. Appendix #1 - Python Basics/1. First steps in Python.vtt 7.4 KB
12. Random Forest Classifier/6. Random forests example II - credit scoring.vtt 7.4 KB
18. Feed-Forward Neural Network Theory/6. How to measure the error of the network.vtt 7.3 KB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/7. Stochastic gradient descent implementation II.vtt 7.3 KB
44. Appendix #3 - Data Structures in Python/5. Lists in Python.vtt 7.3 KB
42. Appendix #1 - Python Basics/15. Break and continue.vtt 7.3 KB
13. Boosting/2. Boosting introduction - illustration.vtt 7.3 KB
24. Convolutional Neural Networks (CNNs) Theory/2. Feature selection with kernels.vtt 7.2 KB
10. Support Vector Machines (SVMs)/6. Support vector machine example II - iris dataset.vtt 7.2 KB
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44. Appendix #3 - Data Structures in Python/7. Lists in Python - list comprehension.vtt 7.1 KB
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18. Feed-Forward Neural Network Theory/2. Artificial neural networks - the model.vtt 7.1 KB
22. Deep Neural Networks Implementation/3. Deep neural network implementation III.vtt 7.1 KB
34. Markov Decision Process (MDP) Theory/5. Bellman-equation.vtt 7.1 KB
37. Q Learning Implementation (Tic Tac Toe)/7. Tic tac toe with Q learning implementation VII.vtt 7.1 KB
07. Cross Validation/1. What is cross validation.vtt 7.0 KB
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12. Random Forest Classifier/3. Bagging introduction.vtt 6.9 KB
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11. Decision Trees/1. What are decision trees.vtt 6.8 KB
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23. Machine Learning Project II - Smile Detector/2. Reading the images and constructing the dataset I.vtt 6.7 KB
16. Machine Learning Project I - Face Recognition/2. Understanding the dataset.vtt 6.7 KB
27. Recurrent Neural Networks (RNNs) Theory/7. Long-short term memory (LSTM) forward pass example.vtt 6.7 KB
31. Generative Adversarial Networks (GANs) Implementation/3. GAN implementation III.vtt 6.6 KB
16. Machine Learning Project I - Face Recognition/3. Finding optimal number of principal components (eigenvectors).vtt 6.6 KB
42. Appendix #1 - Python Basics/7. Operators.vtt 6.6 KB
24. Convolutional Neural Networks (CNNs) Theory/9. Evolution of CNN architectures.vtt 6.4 KB
39. Deep Q Learning Implementation (Tic Tac Toe)/4. Tic Tac Toe with deep Q learning implementation IV.vtt 6.4 KB
42. Appendix #1 - Python Basics/2. What are the basic data types.vtt 6.3 KB
22. Deep Neural Networks Implementation/5. Multiclass classification implementation II.vtt 6.3 KB
12. Random Forest Classifier/4. Random forest classifier introduction.vtt 6.3 KB
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11. Decision Trees/8. Decision tree implementation III - identifying cancer.vtt 6.3 KB
28. Recurrent Neural Networks (RNNs) Implementation/2. Time series analysis example II.vtt 6.3 KB
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21. Deep Neural Networks Theory/3. Activation functions and weight initializations.vtt 6.1 KB
28. Recurrent Neural Networks (RNNs) Implementation/5. Time series analysis example V.vtt 6.1 KB
06. Logistic Regression/5. Logistic regression example III - credit scoring.vtt 6.1 KB
11. Decision Trees/7. Decision trees implementation II - parameter tuning.vtt 6.1 KB
44. Appendix #3 - Data Structures in Python/10. Mutability and immutability.vtt 6.0 KB
43. Appendix #2 - Functions/1. What are functions.vtt 6.0 KB
07. Cross Validation/2. Cross validation example.vtt 6.0 KB
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13. Boosting/1. What is boosting.vtt 5.9 KB
31. Generative Adversarial Networks (GANs) Implementation/1. GAN implementation I.vtt 5.9 KB
06. Logistic Regression/6. Why is logistic regression linear.vtt 5.9 KB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/11. ADAM optimizer introduction.vtt 5.9 KB
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35. Exploration vs. Exploitation Problem/4. Applications AB testing in marketing.vtt 5.8 KB
29. Transformers/11. Understanding the training of transformers.vtt 5.8 KB
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32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/4. Gradient descent with momentum.vtt 5.5 KB
39. Deep Q Learning Implementation (Tic Tac Toe)/5. Tic Tac Toe with deep Q learning implementation V.vtt 5.4 KB
09. Naive Bayes Classifier/3. Naive Bayes classifier implementation.vtt 5.4 KB
26. Machine Learning Project III - Identifying Objects with CNNs/4. What is batch normalization.vtt 5.4 KB
37. Q Learning Implementation (Tic Tac Toe)/5. Tic tac toe with Q learning implementation V.vtt 5.3 KB
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44. Appendix #3 - Data Structures in Python/9. What are tuples.vtt 5.3 KB
02. Environment Setup/2. Installing PyCharm.vtt 5.3 KB
29. Transformers/8. What is masking.vtt 5.3 KB
08. K-Nearest Neighbor Classifier/4. Bias and variance trade-off.vtt 5.2 KB
21. Deep Neural Networks Theory/6. Gradient descent and stochastic gradient descent.vtt 5.2 KB
34. Markov Decision Process (MDP) Theory/8. What is policy iteration.vtt 5.2 KB
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21. Deep Neural Networks Theory/8. Regularization.vtt 5.1 KB
27. Recurrent Neural Networks (RNNs) Theory/1. Why do recurrent neural networks are important.vtt 5.1 KB
23. Machine Learning Project II - Smile Detector/3. Reading the images and constructing the dataset II.vtt 5.1 KB
31. Generative Adversarial Networks (GANs) Implementation/6. GAN.py 5.1 KB
28. Recurrent Neural Networks (RNNs) Implementation/6. Time series analysis example VI.vtt 5.0 KB
05. Linear Regression/5. Linear regression implementation II.vtt 5.0 KB
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/10. What is RMSProp.vtt 5.0 KB
16. Machine Learning Project I - Face Recognition/5. Constructing the machine learning models.vtt 5.0 KB
24. Convolutional Neural Networks (CNNs) Theory/4. Convolutional neural networks - pooling.vtt 5.0 KB
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35. Exploration vs. Exploitation Problem/1. Exploration vs exploitation problem.vtt 4.8 KB
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11. Decision Trees/5. Decision trees introduction - pros and cons.vtt 4.5 KB
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28. Recurrent Neural Networks (RNNs) Implementation/4. Time series analysis example IV.vtt 3.5 KB
02. Environment Setup/1. Installing Python.vtt 3.3 KB
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33. ### REINFORCEMENT LEARNING ###/1. What is reinforcement learning.html 899 bytes
44. Appendix #3 - Data Structures in Python/8. (!!!) Python lists and arrays.html 631 bytes
15. Clustering/9. Mathematical formulation of clustering.html 629 bytes
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/1. Numerical optimization algorithms in machine learning.html 599 bytes
04. ### MACHINE LEARNING ###/1. Machine learning section.html 471 bytes
10. Support Vector Machines (SVMs)/11. Mathematical formulation of Support Vector Machines (SVMs).html 419 bytes
17. ### NEURAL NETWORKS AND DEEP LEARNING ###/1. Neural networks and deep learning section.html 374 bytes
11. Decision Trees/9. Mathematical formulation of decision trees.html 356 bytes
29. Transformers/13. Mathematical formulation of transformers.html 323 bytes
13. Boosting/8. Mathematical formulation of boosting.html 290 bytes
21. Deep Neural Networks Theory/9. Mathematical formulation of deep neural networks.html 290 bytes
24. Convolutional Neural Networks (CNNs) Theory/11. Mathematical formulation of convolution neural networks.html 290 bytes
34. Markov Decision Process (MDP) Theory/9. Mathematical formulation of reinforcement learning.html 284 bytes
14. Principal Component Analysis (PCA)/5. Mathematical formulation of principle component analysis (PCA).html 282 bytes
08. K-Nearest Neighbor Classifier/8. Mathematical formulation of k-nearest neighbor classifier.html 276 bytes
05. Linear Regression/6. Mathematical formulation of linear regression.html 275 bytes
32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/13. Mathematical formulation of optimization algorithms in machine learning.html 275 bytes
38. Deep Q Learning Theory/5. Mathematical formulation of deep Q learning.html 272 bytes
06. Logistic Regression/7. Mathematical formulation of logistic regression.html 263 bytes
12. Random Forest Classifier/8. Mathematical formulation of random forest classifiers.html 263 bytes
36. Q Learning Theory/4. Mathematical formulation of Q learning.html 262 bytes
18. Feed-Forward Neural Network Theory/10. Mathematical formulation of feed-forward neural networks.html 261 bytes
27. Recurrent Neural Networks (RNNs) Theory/10. Mathematical formulation of recurrent neural networks.html 258 bytes
09. Naive Bayes Classifier/7. Mathematical formulation of naive Bayes classifier.html 246 bytes
30. Generative Adversarial Networks (GANs) Theory/5. Mathematical formulation of GANs.html 234 bytes
24. Convolutional Neural Networks (CNNs) Theory/10. CNN visualization tool.html 126 bytes
47. COURSE MATERIALS (DOWNLOADS)/1. Course materials (source code and slides).html 66 bytes

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