种子简介

种子名称： [GigaCourse.Com] Udemy - [2022] Machine Learning and Deep Learning Bootcamp in Python
文件类型： 视频
文件数目： 293个文件
文件大小： 6.57 GB
收录时间： 2022-11-14 09:17
已经下载： 3次
资源热度： 140
最近下载： 2024-12-10 10:26

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:ce9f78f66c8fee6ff002f4129bc938d7d1e6cb6d&dn=[GigaCourse.Com] Udemy - [2022] Machine Learning and Deep Learning Bootcamp in Python 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

[GigaCourse.Com] Udemy - [2022] Machine Learning and Deep Learning Bootcamp in Python.torrent

01 - Introduction/001 Introduction.mp425.85MB

02 - Environment Setup/003 Installing TensorFlow and Keras.mp49.7MB

03 - Artificial Intelligence Basics/001 Why to learn artificial intelligence and machine learning.mp414.01MB

03 - Artificial Intelligence Basics/002 Types of artificial intelligence learning.mp436.97MB

03 - Artificial Intelligence Basics/003 Fundamentals of statistics.mp433.22MB

05 - Linear Regression/001 What is linear regression.mp439.99MB

05 - Linear Regression/002 Linear regression theory - optimization.mp445.04MB

05 - Linear Regression/003 Linear regression theory - gradient descent.mp439.45MB

05 - Linear Regression/004 Linear regression implementation I.mp490.81MB

05 - Linear Regression/005 Linear regression implementation II.mp412.2MB

06 - Logistic Regression/001 What is logistic regression.mp440.2MB

06 - Logistic Regression/002 Logistic regression and maximum likelihood estimation.mp422.68MB

06 - Logistic Regression/003 Logistic regression example I - sigmoid function.mp433.18MB

06 - Logistic Regression/004 Logistic regression example II- credit scoring.mp458.55MB

06 - Logistic Regression/005 Logistic regression example III - credit scoring.mp433.52MB

07 - Cross Validation/001 What is cross validation.mp424.41MB

07 - Cross Validation/002 Cross validation example.mp425.13MB

08 - K-Nearest Neighbor Classifier/001 What is the k-nearest neighbor classifier.mp413.69MB

08 - K-Nearest Neighbor Classifier/002 Concept of lazy learning.mp415.24MB

08 - K-Nearest Neighbor Classifier/003 Distance metrics - Euclidean-distance.mp421.73MB

08 - K-Nearest Neighbor Classifier/004 Bias and variance trade-off.mp414.7MB

08 - K-Nearest Neighbor Classifier/005 K-nearest neighbor implementation I.mp416.5MB

08 - K-Nearest Neighbor Classifier/006 K-nearest neighbor implementation II.mp448.51MB

08 - K-Nearest Neighbor Classifier/007 K-nearest neighbor implementation III.mp410.53MB

09 - Naive Bayes Classifier/001 What is the naive Bayes classifier.mp442.38MB

09 - Naive Bayes Classifier/002 Naive Bayes classifier illustration.mp49.23MB

09 - Naive Bayes Classifier/003 Naive Bayes classifier implementation.mp411.08MB

09 - Naive Bayes Classifier/004 What is text clustering.mp438.5MB

09 - Naive Bayes Classifier/005 Text clustering - inverse document frequency (TF-IDF).mp414.61MB

09 - Naive Bayes Classifier/006 Naive Bayes example - clustering news.mp478.91MB

10 - Support Vector Machines (SVMs)/001 What are Support Vector Machines (SVMs).mp420.14MB

10 - Support Vector Machines (SVMs)/002 Linearly separable problems.mp430.33MB

10 - Support Vector Machines (SVMs)/003 Non-linearly separable problems.mp422.96MB

10 - Support Vector Machines (SVMs)/004 Kernel functions.mp434.1MB

10 - Support Vector Machines (SVMs)/005 Support vector machine example I - simple.mp436.79MB

10 - Support Vector Machines (SVMs)/006 Support vector machine example II - iris dataset.mp415.1MB

10 - Support Vector Machines (SVMs)/007 Support vector machines example III - parameter tuning.mp417.83MB

10 - Support Vector Machines (SVMs)/008 Support vector machine example IV - digit recognition.mp422.1MB

10 - Support Vector Machines (SVMs)/009 Support vector machine example V - digit recognition.mp414.49MB

10 - Support Vector Machines (SVMs)/010 Advantages and disadvantages.mp46MB

11 - Decision Trees/001 Decision trees introduction - basics.mp427.41MB

11 - Decision Trees/002 Decision trees introduction - entropy.mp440.84MB

11 - Decision Trees/003 Decision trees introduction - information gain.mp438.24MB

11 - Decision Trees/004 The Gini-index approach.mp420.11MB

11 - Decision Trees/005 Decision trees introduction - pros and cons.mp45.74MB

11 - Decision Trees/006 Decision trees implementation I.mp413.18MB

11 - Decision Trees/007 Decision trees implementation II - parameter tuning.mp414.09MB

11 - Decision Trees/008 Decision tree implementation III - identifying cancer.mp432.45MB

12 - Random Forest Classifier/001 Pruning introduction.mp415.47MB

12 - Random Forest Classifier/002 Bagging introduction.mp416.08MB

12 - Random Forest Classifier/003 Random forest classifier introduction.mp412.29MB

12 - Random Forest Classifier/004 Random forests example I - iris dataset.mp413.5MB

12 - Random Forest Classifier/005 Random forests example II - credit scoring.mp49.94MB

12 - Random Forest Classifier/006 Random forests example III - OCR parameter tuning.mp431.9MB

13 - Boosting/001 Boosting introduction - basics.mp415.75MB

13 - Boosting/002 Boosting introduction - illustration.mp411.17MB

13 - Boosting/003 Boosting introduction - equations.mp413.42MB

13 - Boosting/004 Boosting introduction - final formula.mp436.78MB

13 - Boosting/005 Boosting implementation I - iris dataset.mp431.13MB

13 - Boosting/006 Boosting implementation II -wine classification.mp438.65MB

13 - Boosting/007 Boosting vs. bagging.mp46.87MB

14 - Principal Component Analysis (PCA)/001 Principal component analysis (PCA) introduction.mp438.24MB

14 - Principal Component Analysis (PCA)/002 Principal component analysis example.mp426.79MB

14 - Principal Component Analysis (PCA)/003 Principal component analysis example II.mp422.27MB

15 - Clustering/001 K-means clustering introduction.mp416.62MB

15 - Clustering/002 K-means clustering example.mp419.52MB

15 - Clustering/003 K-means clustering - text clustering.mp437.68MB

15 - Clustering/004 DBSCAN introduction.mp411.37MB

15 - Clustering/005 DBSCAN example.mp421.15MB

15 - Clustering/006 Hierarchical clustering introduction.mp416.58MB

15 - Clustering/007 Hierarchical clustering example.mp420.36MB

15 - Clustering/008 Hierarchical clustering - market segmentation.mp429MB

16 - Machine Learning Project I - Face Recognition/001 The Olivetti dataset.mp422.83MB

16 - Machine Learning Project I - Face Recognition/002 Understanding the dataset.mp445.89MB

16 - Machine Learning Project I - Face Recognition/003 Finding optimal number of principal components (eigenvectors).mp423.63MB

16 - Machine Learning Project I - Face Recognition/004 Understanding eigenfaces.mp462.97MB

16 - Machine Learning Project I - Face Recognition/005 Constructing the machine learning models.mp413.36MB

16 - Machine Learning Project I - Face Recognition/006 Using cross-validation.mp421.97MB

18 - Feed-Forward Neural Network Theory/001 Artificial neural networks - inspiration.mp424.16MB

18 - Feed-Forward Neural Network Theory/002 Artificial neural networks - layers.mp411.03MB

18 - Feed-Forward Neural Network Theory/003 Artificial neural networks - the model.mp421.55MB

18 - Feed-Forward Neural Network Theory/004 Why to use activation functions.mp428.39MB

18 - Feed-Forward Neural Network Theory/005 Neural networks - the big picture.mp434.99MB

18 - Feed-Forward Neural Network Theory/006 Using bias nodes in the neural network.mp44.32MB

18 - Feed-Forward Neural Network Theory/007 How to measure the error of the network.mp412.03MB

18 - Feed-Forward Neural Network Theory/008 Optimization with gradient descent.mp439.92MB

18 - Feed-Forward Neural Network Theory/009 Gradient descent with backpropagation.mp424.2MB

18 - Feed-Forward Neural Network Theory/010 Backpropagation explained.mp446.26MB

19 - Single Layer Networks Implementation/001 Simple neural network implementation - XOR problem.mp436.7MB

19 - Single Layer Networks Implementation/002 Simple neural network implementation - Iris dataset.mp484.96MB

19 - Single Layer Networks Implementation/003 Credit scoring with simple neural networks.mp423.17MB

20 - Deep Learning/001 Types of neural networks.mp48.01MB

21 - Deep Neural Networks Theory/001 Deep neural networks.mp49.28MB

21 - Deep Neural Networks Theory/002 Activation functions revisited.mp426.21MB

21 - Deep Neural Networks Theory/003 Loss functions.mp415.42MB

21 - Deep Neural Networks Theory/004 Gradient descent and stochastic gradient descent.mp440.09MB

21 - Deep Neural Networks Theory/005 Hyperparameters.mp426.92MB

22 - Deep Neural Networks Implementation/001 Deep neural network implementation I.mp417.33MB

22 - Deep Neural Networks Implementation/002 Deep neural network implementation II.mp418.9MB

22 - Deep Neural Networks Implementation/003 Deep neural network implementation III.mp426.09MB

22 - Deep Neural Networks Implementation/004 Multiclass classification implementation I.mp428.48MB

22 - Deep Neural Networks Implementation/005 Multiclass classification implementation II.mp426.72MB

23 - Machine Learning Project II - Smile Detector/001 Understanding the classification problem.mp44.78MB

23 - Machine Learning Project II - Smile Detector/002 Reading the images and constructing the dataset I.mp425.08MB

23 - Machine Learning Project II - Smile Detector/003 Reading the images and constructing the dataset II.mp438.06MB

23 - Machine Learning Project II - Smile Detector/004 Building the deep neural network model.mp49.55MB

23 - Machine Learning Project II - Smile Detector/005 Evaluating and testing the model.mp412.52MB

24 - Convolutional Neural Networks (CNNs) Theory/001 Convolutional neural networks basics.mp425MB

24 - Convolutional Neural Networks (CNNs) Theory/002 Feature selection.mp412.15MB

24 - Convolutional Neural Networks (CNNs) Theory/003 Convolutional neural networks - kernel.mp48.9MB

24 - Convolutional Neural Networks (CNNs) Theory/004 Convolutional neural networks - kernel II.mp48.88MB

24 - Convolutional Neural Networks (CNNs) Theory/005 Convolutional neural networks - pooling.mp425.58MB

24 - Convolutional Neural Networks (CNNs) Theory/006 Convolutional neural networks - flattening.mp426.77MB

24 - Convolutional Neural Networks (CNNs) Theory/007 Convolutional neural networks - illustration.mp431.87MB

25 - Convolutional Neural Networks (CNNs) Implementation/001 Handwritten digit classification I.mp454.32MB

25 - Convolutional Neural Networks (CNNs) Implementation/002 Handwritten digit classification II.mp455.59MB

25 - Convolutional Neural Networks (CNNs) Implementation/003 Handwritten digit classification III.mp435.17MB

26 - Machine Learning Project III - Identifying Objects with CNNs/001 What is the CIFAR-10 dataset.mp436.07MB

26 - Machine Learning Project III - Identifying Objects with CNNs/002 Preprocessing the data.mp47.66MB

26 - Machine Learning Project III - Identifying Objects with CNNs/003 Fitting the model.mp443.65MB

26 - Machine Learning Project III - Identifying Objects with CNNs/004 Tuning the parameters - regularization.mp460.49MB

27 - Recurrent Neural Networks (RNNs) Theory/001 Why do recurrent neural networks are important.mp421.3MB

27 - Recurrent Neural Networks (RNNs) Theory/002 Recurrent neural networks basics.mp428.63MB

27 - Recurrent Neural Networks (RNNs) Theory/003 Vanishing and exploding gradients problem.mp427.18MB

27 - Recurrent Neural Networks (RNNs) Theory/004 Long-short term memory (LSTM) model.mp433.39MB

27 - Recurrent Neural Networks (RNNs) Theory/005 Gated recurrent units (GRUs).mp46.41MB

28 - Recurrent Neural Networks (RNNs) Implementation/001 Time series analysis example I.mp414.07MB

28 - Recurrent Neural Networks (RNNs) Implementation/002 Time series analysis example II.mp413.04MB

28 - Recurrent Neural Networks (RNNs) Implementation/003 Time series analysis example III.mp420.06MB

28 - Recurrent Neural Networks (RNNs) Implementation/004 Time series analysis example IV.mp48.46MB

28 - Recurrent Neural Networks (RNNs) Implementation/005 Time series analysis example V.mp414.59MB

28 - Recurrent Neural Networks (RNNs) Implementation/006 Time series analysis example VI.mp412.37MB

29 - ### REINFORCEMENT LEARNING ###/002 Applications of reinforcement learning.mp46.6MB

30 - Markov Decision Process (MDP) Theory/001 Markov decision processes basics I.mp423.2MB

30 - Markov Decision Process (MDP) Theory/002 Markov decision processes basics II.mp414.15MB

30 - Markov Decision Process (MDP) Theory/003 Markov decision processes - equations.mp449.65MB

30 - Markov Decision Process (MDP) Theory/004 Markov decision processes - illustration.mp428.22MB

30 - Markov Decision Process (MDP) Theory/005 Bellman-equation.mp415.43MB

30 - Markov Decision Process (MDP) Theory/006 How to solve MDP problems.mp45.7MB

30 - Markov Decision Process (MDP) Theory/007 What is value iteration.mp424.23MB

30 - Markov Decision Process (MDP) Theory/008 What is policy iteration.mp46.98MB

31 - Exploration vs. Exploitation Problem/001 Exploration vs exploitation problem.mp47.65MB

31 - Exploration vs. Exploitation Problem/002 N-armed bandit problem introduction.mp419.56MB

31 - Exploration vs. Exploitation Problem/003 N-armed bandit problem implementation.mp453.31MB

31 - Exploration vs. Exploitation Problem/004 Applications AB testing in marketing.mp412.13MB

32 - Q Learning Theory/001 What is Q learning.mp411.79MB

32 - Q Learning Theory/002 Q learning introduction - the algorithm.mp415.46MB

32 - Q Learning Theory/003 Q learning illustration.mp421.44MB

33 - Q Learning Implementation (Tic Tac Toe)/001 Tic tac toe with Q learning implementation I.mp416.78MB

33 - Q Learning Implementation (Tic Tac Toe)/002 Tic tac toe with Q learning implementation II.mp419.81MB

33 - Q Learning Implementation (Tic Tac Toe)/003 Tic tac toe with Q learning implementation III.mp426.25MB

33 - Q Learning Implementation (Tic Tac Toe)/004 Tic tac toe with Q learning implementation IV.mp446.16MB

33 - Q Learning Implementation (Tic Tac Toe)/005 Tic tac toe with Q learning implementation V.mp421.72MB

33 - Q Learning Implementation (Tic Tac Toe)/006 Tic tac toe with Q learning implementation VI.mp499.45MB

33 - Q Learning Implementation (Tic Tac Toe)/007 Tic tac toe with Q learning implementation VII.mp449.8MB

33 - Q Learning Implementation (Tic Tac Toe)/008 Tic tac toe with Q learning implementation VIII.mp449.82MB

34 - Deep Q Learning Theory/001 What is deep Q learning.mp49.29MB

34 - Deep Q Learning Theory/003 Remember and replay.mp47MB

35 - Deep Q Learning Implementation (Tic Tac Toe)/001 Tic Tac Toe with deep Q learning implementation I.mp421.12MB

35 - Deep Q Learning Implementation (Tic Tac Toe)/002 Tic Tac Toe with deep Q learning implementation II.mp440.62MB

35 - Deep Q Learning Implementation (Tic Tac Toe)/003 Tic Tac Toe with deep Q learning implementation III.mp474.29MB

35 - Deep Q Learning Implementation (Tic Tac Toe)/004 Tic Tac Toe with deep Q learning implementation IV.mp415.43MB

35 - Deep Q Learning Implementation (Tic Tac Toe)/005 Tic Tac Toe with deep Q learning implementation V.mp431.32MB

36 - ### COMPUTER VISION ###/001 Evolution of computer vision related algorithms.mp48.68MB

37 - Handling Images and Pixels/001 Images and pixel intensities.mp410.74MB

37 - Handling Images and Pixels/002 Handling pixel intensities I.mp434.64MB

37 - Handling Images and Pixels/003 Handling pixel intensities II.mp413.21MB

37 - Handling Images and Pixels/004 Why convolution is so important in image processing.mp438.48MB

37 - Handling Images and Pixels/005 Image processing - blur operation.mp412.67MB

37 - Handling Images and Pixels/006 Image processing - edge detection kernel.mp414.58MB

37 - Handling Images and Pixels/007 Image processing - sharpen operation.mp49.03MB

38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/001 Lane detection - the problem.mp44.41MB

38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/002 Lane detection - handling videos.mp413.69MB

38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/003 Lane detection - first transformations.mp411.94MB

38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/004 What is Canny edge detection.mp416.43MB

38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/005 Getting the useful region of the image - masking.mp464.74MB

38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/006 Detecting lines - what is Hough transformation.mp445MB

38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/008 Drawing lines on video frames.mp432.69MB

38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/009 Testing lane detection algorithm.mp416.08MB

39 - Viola-Jones Face Detection Algorithm Theory/001 Viola-Jones algorithm.mp440.92MB

39 - Viola-Jones Face Detection Algorithm Theory/002 Haar-features.mp422.13MB

39 - Viola-Jones Face Detection Algorithm Theory/003 Integral images.mp424.53MB

39 - Viola-Jones Face Detection Algorithm Theory/004 Boosting in computer vision.mp423.41MB

39 - Viola-Jones Face Detection Algorithm Theory/005 Cascading.mp49.88MB

40 - Face Detection with Viola-Jones Method Implementation/001 Face detection implementation I - installing OpenCV.mp47.64MB

40 - Face Detection with Viola-Jones Method Implementation/002 Face detection implementation II - CascadeClassifier.mp470.68MB

40 - Face Detection with Viola-Jones Method Implementation/003 Face detection implementation III - CascadeClassifier parameters.mp418.36MB

40 - Face Detection with Viola-Jones Method Implementation/004 Face detection implementation IV - tuning the parameters.mp418MB

40 - Face Detection with Viola-Jones Method Implementation/005 Face detection implementation V - detecting faces real-time.mp418.85MB

41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/001 Histogram of oriented gradients basics.mp419.24MB

41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/002 Histogram of oriented gradients - gradient kernel.mp430.56MB

41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/003 Histogram of oriented gradients - magnitude and angle.mp433.92MB

41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/004 Histogram of oriented gradients - normalization.mp422.59MB

41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/005 Histogram of oriented gradients - big picture.mp47.84MB

42 - Histogram of Oriented Gradients (HOG) Implementation/001 Showing the HOG features programatically.mp453.4MB

42 - Histogram of Oriented Gradients (HOG) Implementation/002 Face detection with HOG implementation I.mp415.45MB

42 - Histogram of Oriented Gradients (HOG) Implementation/003 Face detection with HOG implementation II.mp452.33MB

42 - Histogram of Oriented Gradients (HOG) Implementation/004 Face detection with HOG implementation III.mp436.08MB

42 - Histogram of Oriented Gradients (HOG) Implementation/005 Face detection with HOG implementation IV.mp432.31MB

43 - Convolutional Neural Networks (CNNs) Based Approaches/001 The standard convolutional neural network (CNN) way.mp418.36MB

43 - Convolutional Neural Networks (CNNs) Based Approaches/002 Region proposals and convolutional neural networks (CNNs).mp460.62MB

43 - Convolutional Neural Networks (CNNs) Based Approaches/003 Detecting bounding boxes with regression.mp422.1MB

43 - Convolutional Neural Networks (CNNs) Based Approaches/004 What is the Fast R-CNN model.mp46.42MB

43 - Convolutional Neural Networks (CNNs) Based Approaches/005 What is the Faster R-CNN model.mp43.97MB

44 - You Only Look Once (YOLO) Algorithm Theory/001 What is the YOLO approach.mp412.37MB

44 - You Only Look Once (YOLO) Algorithm Theory/002 YOLO algorithm - grid cells.mp438.38MB

44 - You Only Look Once (YOLO) Algorithm Theory/003 YOLO algorithm - intersection over union.mp451.4MB

44 - You Only Look Once (YOLO) Algorithm Theory/004 How to train the YOLO algorithm.mp425.08MB

44 - You Only Look Once (YOLO) Algorithm Theory/005 YOLO algorithm - loss function.mp416.29MB

44 - You Only Look Once (YOLO) Algorithm Theory/006 YOLO algorithm - non-max suppression.mp49.12MB

44 - You Only Look Once (YOLO) Algorithm Theory/007 Why to use the so-called anchor boxes.mp419.94MB

45 - You Only Look Once (YOLO) Algorithm Implementation/001 YOLO algorithm implementation I.mp422.81MB

45 - You Only Look Once (YOLO) Algorithm Implementation/002 YOLO algorithm implementation II.mp423.78MB

45 - You Only Look Once (YOLO) Algorithm Implementation/003 YOLO algorithm implementation III.mp424.71MB

45 - You Only Look Once (YOLO) Algorithm Implementation/004 YOLO algorithm implementation IV.mp469.67MB

45 - You Only Look Once (YOLO) Algorithm Implementation/005 YOLO algorithm implementation V.mp495.8MB

45 - You Only Look Once (YOLO) Algorithm Implementation/006 YOLO algorithm implementation VI.mp47.3MB

45 - You Only Look Once (YOLO) Algorithm Implementation/007 YOLO algorithm implementation VII.mp427.94MB

46 - Single-Shot MultiBox Detector (SSD) Theory/001 What is the SSD algorithm.mp418.05MB

46 - Single-Shot MultiBox Detector (SSD) Theory/002 Basic concept behind SSD algorithm (architecture).mp443.48MB

46 - Single-Shot MultiBox Detector (SSD) Theory/003 Bounding boxes and anchor boxes.mp470.53MB

46 - Single-Shot MultiBox Detector (SSD) Theory/004 Feature maps and convolution layers.mp413.86MB

46 - Single-Shot MultiBox Detector (SSD) Theory/005 Hard negative mining during training.mp46.12MB

46 - Single-Shot MultiBox Detector (SSD) Theory/006 Regularization (data augmentation) and non-max suppression during training.mp46.87MB

47 - SSD Algorithm Implementation/001 SSD implementation I.mp430.89MB

47 - SSD Algorithm Implementation/002 SSD implementation II.mp46.37MB

47 - SSD Algorithm Implementation/003 SSD implementation III.mp418.84MB

47 - SSD Algorithm Implementation/004 SSD implementation IV.mp450.57MB

47 - SSD Algorithm Implementation/005 SSD implementation V.mp414.99MB

48 - ### PYTHON PROGRAMMING CRASH COURSE ###/001 Python crash course introduction.mp43.97MB

49 - Appendix #1 - Python Basics/001 First steps in Python.mp47.38MB

49 - Appendix #1 - Python Basics/002 What are the basic data types.mp47.7MB

49 - Appendix #1 - Python Basics/003 Booleans.mp43.52MB

49 - Appendix #1 - Python Basics/004 Strings.mp414.57MB

49 - Appendix #1 - Python Basics/005 String slicing.mp412.66MB

49 - Appendix #1 - Python Basics/006 Type casting.mp48.18MB

49 - Appendix #1 - Python Basics/007 Operators.mp410.69MB

49 - Appendix #1 - Python Basics/008 Conditional statements.mp48.57MB

49 - Appendix #1 - Python Basics/009 How to use multiple conditions.mp415.96MB

49 - Appendix #1 - Python Basics/010 Logical operators.mp48.05MB

49 - Appendix #1 - Python Basics/011 Loops - for loop.mp49.56MB

49 - Appendix #1 - Python Basics/012 Loops - while loop.mp47.55MB

49 - Appendix #1 - Python Basics/013 What are nested loops.mp45.95MB

49 - Appendix #1 - Python Basics/014 Enumerate.mp47.69MB

49 - Appendix #1 - Python Basics/015 Break and continue.mp49.92MB

49 - Appendix #1 - Python Basics/016 Calculating Fibonacci-numbers.mp44.02MB

50 - Appendix #2 - Functions/001 What are functions.mp48.09MB

50 - Appendix #2 - Functions/002 Defining functions.mp49.6MB

50 - Appendix #2 - Functions/003 Positional arguments and keyword arguments.mp422.2MB

50 - Appendix #2 - Functions/004 Returning values.mp44.11MB

50 - Appendix #2 - Functions/005 Returning multiple values.mp46MB

50 - Appendix #2 - Functions/006 Yield operator.mp49.15MB

50 - Appendix #2 - Functions/007 Local and global variables.mp44.25MB

50 - Appendix #2 - Functions/008 What are the most relevant built-in functions.mp47.63MB

50 - Appendix #2 - Functions/009 What is recursion.mp417.38MB

50 - Appendix #2 - Functions/010 Local vs global variables.mp47.83MB

50 - Appendix #2 - Functions/011 The __main__ function.mp47.33MB

51 - Appendix #3 - Data Structures in Python/001 How to measure the running time of algorithms.mp418.29MB

51 - Appendix #3 - Data Structures in Python/002 Data structures introduction.mp46.72MB

51 - Appendix #3 - Data Structures in Python/003 What are array data structures I.mp412.26MB

51 - Appendix #3 - Data Structures in Python/004 What are array data structures II.mp412.3MB

51 - Appendix #3 - Data Structures in Python/005 Lists in Python.mp410.51MB

51 - Appendix #3 - Data Structures in Python/006 Lists in Python - advanced operations.mp418.63MB

51 - Appendix #3 - Data Structures in Python/007 Lists in Python - list comprehension.mp411.39MB

51 - Appendix #3 - Data Structures in Python/009 What are tuples.mp47.52MB

51 - Appendix #3 - Data Structures in Python/010 Mutability and immutability.mp48.7MB

51 - Appendix #3 - Data Structures in Python/011 What are linked list data structures.mp420.75MB

51 - Appendix #3 - Data Structures in Python/012 Doubly linked list implementation in Python.mp411.44MB

51 - Appendix #3 - Data Structures in Python/013 Hashing and O(1) running time complexity.mp423.11MB

51 - Appendix #3 - Data Structures in Python/014 Dictionaries in Python.mp419.44MB

51 - Appendix #3 - Data Structures in Python/015 Sets in Python.mp426.05MB

51 - Appendix #3 - Data Structures in Python/016 Sorting.mp423.77MB

52 - Appendix #4 - Object Oriented Programming (OOP)/001 What is object oriented programming (OOP).mp45.23MB

52 - Appendix #4 - Object Oriented Programming (OOP)/002 Class and objects basics.mp45.39MB

52 - Appendix #4 - Object Oriented Programming (OOP)/003 Using the constructor.mp417.82MB

52 - Appendix #4 - Object Oriented Programming (OOP)/004 Class variables and instance variables.mp414.67MB

52 - Appendix #4 - Object Oriented Programming (OOP)/005 Private variables and name mangling.mp415.3MB

52 - Appendix #4 - Object Oriented Programming (OOP)/006 What is inheritance in OOP.mp48.13MB

52 - Appendix #4 - Object Oriented Programming (OOP)/007 The super keyword.mp49.13MB

52 - Appendix #4 - Object Oriented Programming (OOP)/008 Function (method) override.mp46.46MB

52 - Appendix #4 - Object Oriented Programming (OOP)/009 What is polymorphism.mp416.18MB

52 - Appendix #4 - Object Oriented Programming (OOP)/010 Polymorphism and abstraction example.mp413.72MB

52 - Appendix #4 - Object Oriented Programming (OOP)/011 Modules.mp411.04MB

52 - Appendix #4 - Object Oriented Programming (OOP)/012 The __str__ function.mp47.67MB

52 - Appendix #4 - Object Oriented Programming (OOP)/013 Comparing objects - overriding functions.mp417.11MB

53 - Appendix #5 - NumPy/001 What is the key advantage of NumPy.mp48.16MB

53 - Appendix #5 - NumPy/002 Creating and updating arrays.mp416.76MB

53 - Appendix #5 - NumPy/003 Dimension of arrays.mp418.44MB

53 - Appendix #5 - NumPy/004 Indexes and slicing.mp416.72MB

53 - Appendix #5 - NumPy/005 Types.mp49.92MB

53 - Appendix #5 - NumPy/006 Reshape.mp416.97MB

53 - Appendix #5 - NumPy/007 Stacking and merging arrays.mp421.95MB

53 - Appendix #5 - NumPy/008 Filter.mp47.65MB