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[FreeCourseSite.com] Udemy - [2022] Machine Learning and Deep Learning Bootcamp in Python
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2022-8-3 22:45
2024-10-28 21:35
206
6.57 GB
293
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Udemy
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2022
Machine
Learning
and
Deep
Learning
Bootcamp
in
Python
文件列表
01 - Introduction/001 Introduction.mp4
25.85MB
02 - Environment Setup/003 Installing TensorFlow and Keras.mp4
9.7MB
03 - Artificial Intelligence Basics/001 Why to learn artificial intelligence and machine learning.mp4
14.01MB
03 - Artificial Intelligence Basics/002 Types of artificial intelligence learning.mp4
36.97MB
03 - Artificial Intelligence Basics/003 Fundamentals of statistics.mp4
33.22MB
05 - Linear Regression/001 What is linear regression.mp4
39.99MB
05 - Linear Regression/002 Linear regression theory - optimization.mp4
45.04MB
05 - Linear Regression/003 Linear regression theory - gradient descent.mp4
39.45MB
05 - Linear Regression/004 Linear regression implementation I.mp4
90.81MB
05 - Linear Regression/005 Linear regression implementation II.mp4
12.2MB
06 - Logistic Regression/001 What is logistic regression.mp4
40.2MB
06 - Logistic Regression/002 Logistic regression and maximum likelihood estimation.mp4
22.68MB
06 - Logistic Regression/003 Logistic regression example I - sigmoid function.mp4
33.18MB
06 - Logistic Regression/004 Logistic regression example II- credit scoring.mp4
58.55MB
06 - Logistic Regression/005 Logistic regression example III - credit scoring.mp4
33.52MB
07 - Cross Validation/001 What is cross validation.mp4
24.41MB
07 - Cross Validation/002 Cross validation example.mp4
25.13MB
08 - K-Nearest Neighbor Classifier/001 What is the k-nearest neighbor classifier.mp4
13.69MB
08 - K-Nearest Neighbor Classifier/002 Concept of lazy learning.mp4
15.24MB
08 - K-Nearest Neighbor Classifier/003 Distance metrics - Euclidean-distance.mp4
21.73MB
08 - K-Nearest Neighbor Classifier/004 Bias and variance trade-off.mp4
14.7MB
08 - K-Nearest Neighbor Classifier/005 K-nearest neighbor implementation I.mp4
16.5MB
08 - K-Nearest Neighbor Classifier/006 K-nearest neighbor implementation II.mp4
48.51MB
08 - K-Nearest Neighbor Classifier/007 K-nearest neighbor implementation III.mp4
10.53MB
09 - Naive Bayes Classifier/001 What is the naive Bayes classifier.mp4
42.38MB
09 - Naive Bayes Classifier/002 Naive Bayes classifier illustration.mp4
9.23MB
09 - Naive Bayes Classifier/003 Naive Bayes classifier implementation.mp4
11.08MB
09 - Naive Bayes Classifier/004 What is text clustering.mp4
38.5MB
09 - Naive Bayes Classifier/005 Text clustering - inverse document frequency (TF-IDF).mp4
14.61MB
09 - Naive Bayes Classifier/006 Naive Bayes example - clustering news.mp4
78.91MB
10 - Support Vector Machines (SVMs)/001 What are Support Vector Machines (SVMs).mp4
20.14MB
10 - Support Vector Machines (SVMs)/002 Linearly separable problems.mp4
30.33MB
10 - Support Vector Machines (SVMs)/003 Non-linearly separable problems.mp4
22.96MB
10 - Support Vector Machines (SVMs)/004 Kernel functions.mp4
34.1MB
10 - Support Vector Machines (SVMs)/005 Support vector machine example I - simple.mp4
36.79MB
10 - Support Vector Machines (SVMs)/006 Support vector machine example II - iris dataset.mp4
15.1MB
10 - Support Vector Machines (SVMs)/007 Support vector machines example III - parameter tuning.mp4
17.83MB
10 - Support Vector Machines (SVMs)/008 Support vector machine example IV - digit recognition.mp4
22.1MB
10 - Support Vector Machines (SVMs)/009 Support vector machine example V - digit recognition.mp4
14.49MB
10 - Support Vector Machines (SVMs)/010 Advantages and disadvantages.mp4
6MB
11 - Decision Trees/001 Decision trees introduction - basics.mp4
27.41MB
11 - Decision Trees/002 Decision trees introduction - entropy.mp4
40.84MB
11 - Decision Trees/003 Decision trees introduction - information gain.mp4
38.24MB
11 - Decision Trees/004 The Gini-index approach.mp4
20.11MB
11 - Decision Trees/005 Decision trees introduction - pros and cons.mp4
5.74MB
11 - Decision Trees/006 Decision trees implementation I.mp4
13.18MB
11 - Decision Trees/007 Decision trees implementation II - parameter tuning.mp4
14.09MB
11 - Decision Trees/008 Decision tree implementation III - identifying cancer.mp4
32.45MB
12 - Random Forest Classifier/001 Pruning introduction.mp4
15.47MB
12 - Random Forest Classifier/002 Bagging introduction.mp4
16.08MB
12 - Random Forest Classifier/003 Random forest classifier introduction.mp4
12.29MB
12 - Random Forest Classifier/004 Random forests example I - iris dataset.mp4
13.5MB
12 - Random Forest Classifier/005 Random forests example II - credit scoring.mp4
9.94MB
12 - Random Forest Classifier/006 Random forests example III - OCR parameter tuning.mp4
31.9MB
13 - Boosting/001 Boosting introduction - basics.mp4
15.75MB
13 - Boosting/002 Boosting introduction - illustration.mp4
11.17MB
13 - Boosting/003 Boosting introduction - equations.mp4
13.42MB
13 - Boosting/004 Boosting introduction - final formula.mp4
36.78MB
13 - Boosting/005 Boosting implementation I - iris dataset.mp4
31.13MB
13 - Boosting/006 Boosting implementation II -wine classification.mp4
38.65MB
13 - Boosting/007 Boosting vs. bagging.mp4
6.87MB
14 - Principal Component Analysis (PCA)/001 Principal component analysis (PCA) introduction.mp4
38.24MB
14 - Principal Component Analysis (PCA)/002 Principal component analysis example.mp4
26.79MB
14 - Principal Component Analysis (PCA)/003 Principal component analysis example II.mp4
22.27MB
15 - Clustering/001 K-means clustering introduction.mp4
16.62MB
15 - Clustering/002 K-means clustering example.mp4
19.52MB
15 - Clustering/003 K-means clustering - text clustering.mp4
37.68MB
15 - Clustering/004 DBSCAN introduction.mp4
11.37MB
15 - Clustering/005 DBSCAN example.mp4
21.15MB
15 - Clustering/006 Hierarchical clustering introduction.mp4
16.58MB
15 - Clustering/007 Hierarchical clustering example.mp4
20.36MB
15 - Clustering/008 Hierarchical clustering - market segmentation.mp4
29MB
16 - Machine Learning Project I - Face Recognition/001 The Olivetti dataset.mp4
22.83MB
16 - Machine Learning Project I - Face Recognition/002 Understanding the dataset.mp4
45.89MB
16 - Machine Learning Project I - Face Recognition/003 Finding optimal number of principal components (eigenvectors).mp4
23.63MB
16 - Machine Learning Project I - Face Recognition/004 Understanding eigenfaces.mp4
62.97MB
16 - Machine Learning Project I - Face Recognition/005 Constructing the machine learning models.mp4
13.36MB
16 - Machine Learning Project I - Face Recognition/006 Using cross-validation.mp4
21.97MB
18 - Feed-Forward Neural Network Theory/001 Artificial neural networks - inspiration.mp4
24.16MB
18 - Feed-Forward Neural Network Theory/002 Artificial neural networks - layers.mp4
11.03MB
18 - Feed-Forward Neural Network Theory/003 Artificial neural networks - the model.mp4
21.55MB
18 - Feed-Forward Neural Network Theory/004 Why to use activation functions.mp4
28.39MB
18 - Feed-Forward Neural Network Theory/005 Neural networks - the big picture.mp4
34.99MB
18 - Feed-Forward Neural Network Theory/006 Using bias nodes in the neural network.mp4
4.32MB
18 - Feed-Forward Neural Network Theory/007 How to measure the error of the network.mp4
12.03MB
18 - Feed-Forward Neural Network Theory/008 Optimization with gradient descent.mp4
39.92MB
18 - Feed-Forward Neural Network Theory/009 Gradient descent with backpropagation.mp4
24.2MB
18 - Feed-Forward Neural Network Theory/010 Backpropagation explained.mp4
46.26MB
19 - Single Layer Networks Implementation/001 Simple neural network implementation - XOR problem.mp4
36.7MB
19 - Single Layer Networks Implementation/002 Simple neural network implementation - Iris dataset.mp4
84.96MB
19 - Single Layer Networks Implementation/003 Credit scoring with simple neural networks.mp4
23.17MB
20 - Deep Learning/001 Types of neural networks.mp4
8.01MB
21 - Deep Neural Networks Theory/001 Deep neural networks.mp4
9.28MB
21 - Deep Neural Networks Theory/002 Activation functions revisited.mp4
26.21MB
21 - Deep Neural Networks Theory/003 Loss functions.mp4
15.42MB
21 - Deep Neural Networks Theory/004 Gradient descent and stochastic gradient descent.mp4
40.09MB
21 - Deep Neural Networks Theory/005 Hyperparameters.mp4
26.92MB
22 - Deep Neural Networks Implementation/001 Deep neural network implementation I.mp4
17.33MB
22 - Deep Neural Networks Implementation/002 Deep neural network implementation II.mp4
18.9MB
22 - Deep Neural Networks Implementation/003 Deep neural network implementation III.mp4
26.09MB
22 - Deep Neural Networks Implementation/004 Multiclass classification implementation I.mp4
28.48MB
22 - Deep Neural Networks Implementation/005 Multiclass classification implementation II.mp4
26.72MB
23 - Machine Learning Project II - Smile Detector/001 Understanding the classification problem.mp4
4.78MB
23 - Machine Learning Project II - Smile Detector/002 Reading the images and constructing the dataset I.mp4
25.08MB
23 - Machine Learning Project II - Smile Detector/003 Reading the images and constructing the dataset II.mp4
38.06MB
23 - Machine Learning Project II - Smile Detector/004 Building the deep neural network model.mp4
9.55MB
23 - Machine Learning Project II - Smile Detector/005 Evaluating and testing the model.mp4
12.52MB
24 - Convolutional Neural Networks (CNNs) Theory/001 Convolutional neural networks basics.mp4
25MB
24 - Convolutional Neural Networks (CNNs) Theory/002 Feature selection.mp4
12.15MB
24 - Convolutional Neural Networks (CNNs) Theory/003 Convolutional neural networks - kernel.mp4
8.9MB
24 - Convolutional Neural Networks (CNNs) Theory/004 Convolutional neural networks - kernel II.mp4
8.88MB
24 - Convolutional Neural Networks (CNNs) Theory/005 Convolutional neural networks - pooling.mp4
25.58MB
24 - Convolutional Neural Networks (CNNs) Theory/006 Convolutional neural networks - flattening.mp4
26.77MB
24 - Convolutional Neural Networks (CNNs) Theory/007 Convolutional neural networks - illustration.mp4
31.87MB
25 - Convolutional Neural Networks (CNNs) Implementation/001 Handwritten digit classification I.mp4
54.32MB
25 - Convolutional Neural Networks (CNNs) Implementation/002 Handwritten digit classification II.mp4
55.59MB
25 - Convolutional Neural Networks (CNNs) Implementation/003 Handwritten digit classification III.mp4
35.17MB
26 - Machine Learning Project III - Identifying Objects with CNNs/001 What is the CIFAR-10 dataset.mp4
36.07MB
26 - Machine Learning Project III - Identifying Objects with CNNs/002 Preprocessing the data.mp4
7.66MB
26 - Machine Learning Project III - Identifying Objects with CNNs/003 Fitting the model.mp4
43.65MB
26 - Machine Learning Project III - Identifying Objects with CNNs/004 Tuning the parameters - regularization.mp4
60.49MB
27 - Recurrent Neural Networks (RNNs) Theory/001 Why do recurrent neural networks are important.mp4
21.3MB
27 - Recurrent Neural Networks (RNNs) Theory/002 Recurrent neural networks basics.mp4
28.63MB
27 - Recurrent Neural Networks (RNNs) Theory/003 Vanishing and exploding gradients problem.mp4
27.18MB
27 - Recurrent Neural Networks (RNNs) Theory/004 Long-short term memory (LSTM) model.mp4
33.39MB
27 - Recurrent Neural Networks (RNNs) Theory/005 Gated recurrent units (GRUs).mp4
6.41MB
28 - Recurrent Neural Networks (RNNs) Implementation/001 Time series analysis example I.mp4
14.07MB
28 - Recurrent Neural Networks (RNNs) Implementation/002 Time series analysis example II.mp4
13.04MB
28 - Recurrent Neural Networks (RNNs) Implementation/003 Time series analysis example III.mp4
20.06MB
28 - Recurrent Neural Networks (RNNs) Implementation/004 Time series analysis example IV.mp4
8.46MB
28 - Recurrent Neural Networks (RNNs) Implementation/005 Time series analysis example V.mp4
14.59MB
28 - Recurrent Neural Networks (RNNs) Implementation/006 Time series analysis example VI.mp4
12.37MB
29 - ### REINFORCEMENT LEARNING ###/002 Applications of reinforcement learning.mp4
6.6MB
30 - Markov Decision Process (MDP) Theory/001 Markov decision processes basics I.mp4
23.2MB
30 - Markov Decision Process (MDP) Theory/002 Markov decision processes basics II.mp4
14.15MB
30 - Markov Decision Process (MDP) Theory/003 Markov decision processes - equations.mp4
49.65MB
30 - Markov Decision Process (MDP) Theory/004 Markov decision processes - illustration.mp4
28.22MB
30 - Markov Decision Process (MDP) Theory/005 Bellman-equation.mp4
15.43MB
30 - Markov Decision Process (MDP) Theory/006 How to solve MDP problems.mp4
5.7MB
30 - Markov Decision Process (MDP) Theory/007 What is value iteration.mp4
24.23MB
30 - Markov Decision Process (MDP) Theory/008 What is policy iteration.mp4
6.98MB
31 - Exploration vs. Exploitation Problem/001 Exploration vs exploitation problem.mp4
7.65MB
31 - Exploration vs. Exploitation Problem/002 N-armed bandit problem introduction.mp4
19.56MB
31 - Exploration vs. Exploitation Problem/003 N-armed bandit problem implementation.mp4
53.31MB
31 - Exploration vs. Exploitation Problem/004 Applications AB testing in marketing.mp4
12.13MB
32 - Q Learning Theory/001 What is Q learning.mp4
11.79MB
32 - Q Learning Theory/002 Q learning introduction - the algorithm.mp4
15.46MB
32 - Q Learning Theory/003 Q learning illustration.mp4
21.44MB
33 - Q Learning Implementation (Tic Tac Toe)/001 Tic tac toe with Q learning implementation I.mp4
16.78MB
33 - Q Learning Implementation (Tic Tac Toe)/002 Tic tac toe with Q learning implementation II.mp4
19.81MB
33 - Q Learning Implementation (Tic Tac Toe)/003 Tic tac toe with Q learning implementation III.mp4
26.25MB
33 - Q Learning Implementation (Tic Tac Toe)/004 Tic tac toe with Q learning implementation IV.mp4
46.16MB
33 - Q Learning Implementation (Tic Tac Toe)/005 Tic tac toe with Q learning implementation V.mp4
21.72MB
33 - Q Learning Implementation (Tic Tac Toe)/006 Tic tac toe with Q learning implementation VI.mp4
99.45MB
33 - Q Learning Implementation (Tic Tac Toe)/007 Tic tac toe with Q learning implementation VII.mp4
49.8MB
33 - Q Learning Implementation (Tic Tac Toe)/008 Tic tac toe with Q learning implementation VIII.mp4
49.82MB
34 - Deep Q Learning Theory/001 What is deep Q learning.mp4
9.29MB
34 - Deep Q Learning Theory/003 Remember and replay.mp4
7MB
35 - Deep Q Learning Implementation (Tic Tac Toe)/001 Tic Tac Toe with deep Q learning implementation I.mp4
21.12MB
35 - Deep Q Learning Implementation (Tic Tac Toe)/002 Tic Tac Toe with deep Q learning implementation II.mp4
40.62MB
35 - Deep Q Learning Implementation (Tic Tac Toe)/003 Tic Tac Toe with deep Q learning implementation III.mp4
74.29MB
35 - Deep Q Learning Implementation (Tic Tac Toe)/004 Tic Tac Toe with deep Q learning implementation IV.mp4
15.43MB
35 - Deep Q Learning Implementation (Tic Tac Toe)/005 Tic Tac Toe with deep Q learning implementation V.mp4
31.32MB
36 - ### COMPUTER VISION ###/001 Evolution of computer vision related algorithms.mp4
8.68MB
37 - Handling Images and Pixels/001 Images and pixel intensities.mp4
10.74MB
37 - Handling Images and Pixels/002 Handling pixel intensities I.mp4
34.64MB
37 - Handling Images and Pixels/003 Handling pixel intensities II.mp4
13.21MB
37 - Handling Images and Pixels/004 Why convolution is so important in image processing.mp4
38.48MB
37 - Handling Images and Pixels/005 Image processing - blur operation.mp4
12.67MB
37 - Handling Images and Pixels/006 Image processing - edge detection kernel.mp4
14.58MB
37 - Handling Images and Pixels/007 Image processing - sharpen operation.mp4
9.03MB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/001 Lane detection - the problem.mp4
4.41MB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/002 Lane detection - handling videos.mp4
13.69MB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/003 Lane detection - first transformations.mp4
11.94MB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/004 What is Canny edge detection.mp4
16.43MB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/005 Getting the useful region of the image - masking.mp4
64.74MB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/006 Detecting lines - what is Hough transformation.mp4
45MB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/008 Drawing lines on video frames.mp4
32.69MB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/009 Testing lane detection algorithm.mp4
16.08MB
39 - Viola-Jones Face Detection Algorithm Theory/001 Viola-Jones algorithm.mp4
40.92MB
39 - Viola-Jones Face Detection Algorithm Theory/002 Haar-features.mp4
22.13MB
39 - Viola-Jones Face Detection Algorithm Theory/003 Integral images.mp4
24.53MB
39 - Viola-Jones Face Detection Algorithm Theory/004 Boosting in computer vision.mp4
23.41MB
39 - Viola-Jones Face Detection Algorithm Theory/005 Cascading.mp4
9.88MB
40 - Face Detection with Viola-Jones Method Implementation/001 Face detection implementation I - installing OpenCV.mp4
7.64MB
40 - Face Detection with Viola-Jones Method Implementation/002 Face detection implementation II - CascadeClassifier.mp4
70.68MB
40 - Face Detection with Viola-Jones Method Implementation/003 Face detection implementation III - CascadeClassifier parameters.mp4
18.36MB
40 - Face Detection with Viola-Jones Method Implementation/004 Face detection implementation IV - tuning the parameters.mp4
18MB
40 - Face Detection with Viola-Jones Method Implementation/005 Face detection implementation V - detecting faces real-time.mp4
18.85MB
41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/001 Histogram of oriented gradients basics.mp4
19.24MB
41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/002 Histogram of oriented gradients - gradient kernel.mp4
30.56MB
41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/003 Histogram of oriented gradients - magnitude and angle.mp4
33.92MB
41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/004 Histogram of oriented gradients - normalization.mp4
22.59MB
41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/005 Histogram of oriented gradients - big picture.mp4
7.84MB
42 - Histogram of Oriented Gradients (HOG) Implementation/001 Showing the HOG features programatically.mp4
53.4MB
42 - Histogram of Oriented Gradients (HOG) Implementation/002 Face detection with HOG implementation I.mp4
15.45MB
42 - Histogram of Oriented Gradients (HOG) Implementation/003 Face detection with HOG implementation II.mp4
52.33MB
42 - Histogram of Oriented Gradients (HOG) Implementation/004 Face detection with HOG implementation III.mp4
36.08MB
42 - Histogram of Oriented Gradients (HOG) Implementation/005 Face detection with HOG implementation IV.mp4
32.31MB
43 - Convolutional Neural Networks (CNNs) Based Approaches/001 The standard convolutional neural network (CNN) way.mp4
18.36MB
43 - Convolutional Neural Networks (CNNs) Based Approaches/002 Region proposals and convolutional neural networks (CNNs).mp4
60.62MB
43 - Convolutional Neural Networks (CNNs) Based Approaches/003 Detecting bounding boxes with regression.mp4
22.1MB
43 - Convolutional Neural Networks (CNNs) Based Approaches/004 What is the Fast R-CNN model.mp4
6.42MB
43 - Convolutional Neural Networks (CNNs) Based Approaches/005 What is the Faster R-CNN model.mp4
3.97MB
44 - You Only Look Once (YOLO) Algorithm Theory/001 What is the YOLO approach.mp4
12.37MB
44 - You Only Look Once (YOLO) Algorithm Theory/002 YOLO algorithm - grid cells.mp4
38.38MB
44 - You Only Look Once (YOLO) Algorithm Theory/003 YOLO algorithm - intersection over union.mp4
51.4MB
44 - You Only Look Once (YOLO) Algorithm Theory/004 How to train the YOLO algorithm.mp4
25.08MB
44 - You Only Look Once (YOLO) Algorithm Theory/005 YOLO algorithm - loss function.mp4
16.29MB
44 - You Only Look Once (YOLO) Algorithm Theory/006 YOLO algorithm - non-max suppression.mp4
9.12MB
44 - You Only Look Once (YOLO) Algorithm Theory/007 Why to use the so-called anchor boxes.mp4
19.94MB
45 - You Only Look Once (YOLO) Algorithm Implementation/001 YOLO algorithm implementation I.mp4
22.81MB
45 - You Only Look Once (YOLO) Algorithm Implementation/002 YOLO algorithm implementation II.mp4
23.78MB
45 - You Only Look Once (YOLO) Algorithm Implementation/003 YOLO algorithm implementation III.mp4
24.71MB
45 - You Only Look Once (YOLO) Algorithm Implementation/004 YOLO algorithm implementation IV.mp4
69.67MB
45 - You Only Look Once (YOLO) Algorithm Implementation/005 YOLO algorithm implementation V.mp4
95.8MB
45 - You Only Look Once (YOLO) Algorithm Implementation/006 YOLO algorithm implementation VI.mp4
7.3MB
45 - You Only Look Once (YOLO) Algorithm Implementation/007 YOLO algorithm implementation VII.mp4
27.94MB
46 - Single-Shot MultiBox Detector (SSD) Theory/001 What is the SSD algorithm.mp4
18.05MB
46 - Single-Shot MultiBox Detector (SSD) Theory/002 Basic concept behind SSD algorithm (architecture).mp4
43.48MB
46 - Single-Shot MultiBox Detector (SSD) Theory/003 Bounding boxes and anchor boxes.mp4
70.53MB
46 - Single-Shot MultiBox Detector (SSD) Theory/004 Feature maps and convolution layers.mp4
13.86MB
46 - Single-Shot MultiBox Detector (SSD) Theory/005 Hard negative mining during training.mp4
6.12MB
46 - Single-Shot MultiBox Detector (SSD) Theory/006 Regularization (data augmentation) and non-max suppression during training.mp4
6.87MB
47 - SSD Algorithm Implementation/001 SSD implementation I.mp4
30.89MB
47 - SSD Algorithm Implementation/002 SSD implementation II.mp4
6.37MB
47 - SSD Algorithm Implementation/003 SSD implementation III.mp4
18.84MB
47 - SSD Algorithm Implementation/004 SSD implementation IV.mp4
50.57MB
47 - SSD Algorithm Implementation/005 SSD implementation V.mp4
14.99MB
48 - ### PYTHON PROGRAMMING CRASH COURSE ###/001 Python crash course introduction.mp4
3.97MB
49 - Appendix #1 - Python Basics/001 First steps in Python.mp4
7.38MB
49 - Appendix #1 - Python Basics/002 What are the basic data types.mp4
7.7MB
49 - Appendix #1 - Python Basics/003 Booleans.mp4
3.52MB
49 - Appendix #1 - Python Basics/004 Strings.mp4
14.57MB
49 - Appendix #1 - Python Basics/005 String slicing.mp4
12.66MB
49 - Appendix #1 - Python Basics/006 Type casting.mp4
8.18MB
49 - Appendix #1 - Python Basics/007 Operators.mp4
10.69MB
49 - Appendix #1 - Python Basics/008 Conditional statements.mp4
8.57MB
49 - Appendix #1 - Python Basics/009 How to use multiple conditions.mp4
15.96MB
49 - Appendix #1 - Python Basics/010 Logical operators.mp4
8.05MB
49 - Appendix #1 - Python Basics/011 Loops - for loop.mp4
9.56MB
49 - Appendix #1 - Python Basics/012 Loops - while loop.mp4
7.55MB
49 - Appendix #1 - Python Basics/013 What are nested loops.mp4
5.95MB
49 - Appendix #1 - Python Basics/014 Enumerate.mp4
7.69MB
49 - Appendix #1 - Python Basics/015 Break and continue.mp4
9.92MB
49 - Appendix #1 - Python Basics/016 Calculating Fibonacci-numbers.mp4
4.02MB
50 - Appendix #2 - Functions/001 What are functions.mp4
8.09MB
50 - Appendix #2 - Functions/002 Defining functions.mp4
9.6MB
50 - Appendix #2 - Functions/003 Positional arguments and keyword arguments.mp4
22.2MB
50 - Appendix #2 - Functions/004 Returning values.mp4
4.11MB
50 - Appendix #2 - Functions/005 Returning multiple values.mp4
6MB
50 - Appendix #2 - Functions/006 Yield operator.mp4
9.15MB
50 - Appendix #2 - Functions/007 Local and global variables.mp4
4.25MB
50 - Appendix #2 - Functions/008 What are the most relevant built-in functions.mp4
7.63MB
50 - Appendix #2 - Functions/009 What is recursion.mp4
17.38MB
50 - Appendix #2 - Functions/010 Local vs global variables.mp4
7.83MB
50 - Appendix #2 - Functions/011 The __main__ function.mp4
7.33MB
51 - Appendix #3 - Data Structures in Python/001 How to measure the running time of algorithms.mp4
18.29MB
51 - Appendix #3 - Data Structures in Python/002 Data structures introduction.mp4
6.72MB
51 - Appendix #3 - Data Structures in Python/003 What are array data structures I.mp4
12.26MB
51 - Appendix #3 - Data Structures in Python/004 What are array data structures II.mp4
12.3MB
51 - Appendix #3 - Data Structures in Python/005 Lists in Python.mp4
10.51MB
51 - Appendix #3 - Data Structures in Python/006 Lists in Python - advanced operations.mp4
18.63MB
51 - Appendix #3 - Data Structures in Python/007 Lists in Python - list comprehension.mp4
11.39MB
51 - Appendix #3 - Data Structures in Python/009 What are tuples.mp4
7.52MB
51 - Appendix #3 - Data Structures in Python/010 Mutability and immutability.mp4
8.7MB
51 - Appendix #3 - Data Structures in Python/011 What are linked list data structures.mp4
20.75MB
51 - Appendix #3 - Data Structures in Python/012 Doubly linked list implementation in Python.mp4
11.44MB
51 - Appendix #3 - Data Structures in Python/013 Hashing and O(1) running time complexity.mp4
23.11MB
51 - Appendix #3 - Data Structures in Python/014 Dictionaries in Python.mp4
19.44MB
51 - Appendix #3 - Data Structures in Python/015 Sets in Python.mp4
26.05MB
51 - Appendix #3 - Data Structures in Python/016 Sorting.mp4
23.77MB
52 - Appendix #4 - Object Oriented Programming (OOP)/001 What is object oriented programming (OOP).mp4
5.23MB
52 - Appendix #4 - Object Oriented Programming (OOP)/002 Class and objects basics.mp4
5.39MB
52 - Appendix #4 - Object Oriented Programming (OOP)/003 Using the constructor.mp4
17.82MB
52 - Appendix #4 - Object Oriented Programming (OOP)/004 Class variables and instance variables.mp4
14.67MB
52 - Appendix #4 - Object Oriented Programming (OOP)/005 Private variables and name mangling.mp4
15.3MB
52 - Appendix #4 - Object Oriented Programming (OOP)/006 What is inheritance in OOP.mp4
8.13MB
52 - Appendix #4 - Object Oriented Programming (OOP)/007 The super keyword.mp4
9.13MB
52 - Appendix #4 - Object Oriented Programming (OOP)/008 Function (method) override.mp4
6.46MB
52 - Appendix #4 - Object Oriented Programming (OOP)/009 What is polymorphism.mp4
16.18MB
52 - Appendix #4 - Object Oriented Programming (OOP)/010 Polymorphism and abstraction example.mp4
13.72MB
52 - Appendix #4 - Object Oriented Programming (OOP)/011 Modules.mp4
11.04MB
52 - Appendix #4 - Object Oriented Programming (OOP)/012 The __str__ function.mp4
7.67MB
52 - Appendix #4 - Object Oriented Programming (OOP)/013 Comparing objects - overriding functions.mp4
17.11MB
53 - Appendix #5 - NumPy/001 What is the key advantage of NumPy.mp4
8.16MB
53 - Appendix #5 - NumPy/002 Creating and updating arrays.mp4
16.76MB
53 - Appendix #5 - NumPy/003 Dimension of arrays.mp4
18.44MB
53 - Appendix #5 - NumPy/004 Indexes and slicing.mp4
16.72MB
53 - Appendix #5 - NumPy/005 Types.mp4
9.92MB
53 - Appendix #5 - NumPy/006 Reshape.mp4
16.97MB
53 - Appendix #5 - NumPy/007 Stacking and merging arrays.mp4
21.95MB
53 - Appendix #5 - NumPy/008 Filter.mp4
7.65MB
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