Python for Machine Learning & Deep Learning in One Semester

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Zeeshan Ahmad

46:49:01

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1. Introduction and Course Material

1. Introduction of the course.mp4

07:39

2.1 Course Material.zip

2. Course Material.html

2. Introduction to Machine Learning and Deep Learning

1. Introduction of the Section.mp4

01:36

2. What in Intelligence.mp4

05:43

3. Machine Learning.mp4

03:46

4. Supervised Machine Learning.mp4

13:01

5.1 5-Unsupervised Machine Learning.mp4

03:26

5. Unsupervised Machine Learning.mp4

03:26

6. Deep Learning.mp4

07:14

3. Introduction to Google Colab

1. Introduction of the Section.mp4

02:23

2. Importing Dataset in Google Colab.mp4

13:51

3. Importing and Displaying Image in Google Colab.mp4

08:01

4. Importing more datasets.mp4

04:43

5. Uploading Course Material on your Google Drive.mp4

02:31

4. Python Crash Course

1. Introduction of the Section.mp4

02:50

2. Arithmetic With Python.mp4

10:54

3. Comparison and Logical Operations.mp4

05:52

4. Conditional Statements.mp4

08:14

5. Dealing With Numpy Arrays-Part01.mp4

12:26

6. Dealing With Numpy Arrays-Part02.mp4

15:02

7. Dealing With Numpy Arrays-Part03.mp4

10:52

8. Plotting and Visualization-Part01.mp4

17:51

9. Plotting and Visualization-Part02.mp4

15:03

10. Plotting and Visualization-Part03.mp4

13:34

11. Plotting and Visualization-Part04.mp4

07:35

12. Lists in Python.mp4

20:27

13. For Loops-Part01.mp4

21:03

14. For Loops-Part02.mp4

20:36

15. Strings.mp4

13:16

16. Print Formatting With Strings.mp4

03:36

17. Dictionaries-Part01.mp4

07:47

18. Dictionaries-Part02.mp4

08:04

19. Functions in Python-Part01.mp4

08:15

20. Functions in Python-Part02.mp4

07:59

21. Pandas-Part01.mp4

06:57

22. Pandas-Part02.mp4

05:20

23. Pandas-Part03.mp4

08:54

24. Pandas-Part04.mp4

11:23

25. Seaborn-Part01.mp4

08:56

26. Seaborn-Part02.mp4

06:50

27. Seaborn-Part03.mp4

07:16

28. Tuples.mp4

07:05

29. Classes in Python.mp4

16:27

5. Data Preprocessing

1. Introduction of the Section.mp4

03:09

2. Need of Data Preprocessing.mp4

04:02

3. Data Normalization and Min-Max Scaling.mp4

06:27

4. Project01-Data Normalization and Min-Max Scaling-Part01.mp4

09:16

5. Project01-Data Normalization and Min-Max Scaling-Part02.mp4

11:25

6. Data Standardization.mp4

04:23

7. Project02-Data Standardization.mp4

08:21

8. Project03-Dealing With Missing Values.mp4

16:37

9. Project04-Dealing With Categorical Features.mp4

14:53

10. Project05-Feature Engineering.mp4

09:05

11. Project06-Feature Engineering by Window Method.mp4

12:48

6. Supervised Machine Learning

1. Supervised Machine Learning.mp4

01:16

7. Regression Analysis

1. Introduction of the Section.mp4

05:03

2. Origin of the Regression.mp4

13:11

3. Definition of Regression.mp4

03:49

4. Requirement from Regression.mp4

03:23

5. Simple Linear Regression.mp4

06:38

6. Multiple Linear Regression.mp4

04:32

7. Target and Predicted Values.mp4

03:50

8. Loss Function.mp4

04:08

9. Regression With Least Square Method.mp4

14:32

10. Least Square Method With Numerical Example.mp4

05:07

11. Evaluation Metrics for Regression.mp4

08:07

12. Project01-Simple Regression-Part01.mp4

09:41

13. Project01-Simple Regression-Part02.mp4

08:46

14. Project01-Simple Regression-Part03.mp4

18:26

15. Project02-Multiple Regression-Part01.mp4

10:49

16. Project02-Multiple Regression-Part02.mp4

10:37

17. Project02-Multiple Regression-Part03.mp4

15:46

18. Project03-Another Multiple Regression.mp4

15:04

19. Regression by Gradient Descent.mp4

09:58

20. Project04-Simple Regression With Gradient Descent.mp4

19:31

21. Project05-Multiple Regression With Gradient Descent.mp4

15:31

22. Polynomial Regression.mp4

06:30

23. Project06-Polynomial Regression.mp4

12:10

24. Cross-validation.mp4

03:32

25. Project07-Cross-validation.mp4

13:57

26. Underfitting and Overfitting ( Bias-Variance Tradeoff ).mp4

12:04

27. Concept of Regularization.mp4

04:31

28. Ridge Regression OR L2 Regularization.mp4

09:55

29. Lasso Regression OR L1 Regularization.mp4

08:02

30. Comparing Ridge and Lasso Regression.mp4

03:06

31. Elastic Net Regularization.mp4

03:16

32. Project08-Regularizations.mp4

21:18

33. Grid search Cross-validation.mp4

04:02

34. Project09-Grid Search Cross-validation.mp4

20:31

8. Logistic Regression

1. Introduction of the Section.mp4

02:21

2. Fundamentals of Logistic Regression.mp4

07:34

3. Limitations of Regression Models.mp4

09:26

4. Transforming Linear Regression into Logistic Regression.mp4

08:05

5. Project01-Getting Class Probabilities-Part01.mp4

08:23

6. Project01-Getting Class Probabilities-Part02.mp4

11:31

7. Loss Function.mp4

05:33

8. Model Evaluation-Confusion Matrix.mp4

10:14

9. Accuracy, Precision, Recall and F1-Score.mp4

14:02

10. ROC Curves and Area Under ROC.mp4

03:42

11. Project02-Evaluating Logistic Regression Model.mp4

21:27

12. Project03-Cross-validation With Logistic Regression Model.mp4

17:01

13. Project04-Multiclass Classification.mp4

21:46

14. Project05-Classification With Challenging Dataset-Part01.mp4

07:14

15. Project05-Classification With Challenging Dataset-Part02.mp4

05:56

16. Project05-Classification With Challenging Dataset-Part03.mp4

07:45

17. Grid Search Cross-validation With Logistic Regression.mp4

13:14

9. K-Nearest Neighbors ( KNN )

1. Introduction of the Section.mp4

01:34

2. Intuition Behind KNN.mp4

04:39

3. Steps of KNN Algorithm.mp4

04:07

4. Numerical Example on KNN Algorithm.mp4

07:13

5. Project01-KNN Algorithm-Part01.mp4

09:01

6. Project01-KNN Algorithm-Part02.mp4

11:54

7. Finding Optimal Value of K.mp4

02:56

8. Project02-Implementing KNN.mp4

15:44

9. Project03-Implementing KNN.mp4

09:40

10. Project04-Implementing KNN.mp4

17:50

11. Advantages and disadvantages of KNN.mp4

03:37

10. Bayes Theorem and Naive Bayes Classifier

1. Introduction of the section.mp4

03:00

2. Fundamentals of Probability.mp4

12:44

3. Conditional Probability and Bayes Theorem.mp4

08:08

4. Numerical Example on Bayes Theorem.mp4

05:13

5. Naive Bayes Classification.mp4

09:40

6. Comparing Naive Bayes Classification With Logistic Regression.mp4

03:58

7. Project01 Naive Bayes as probabilistic classifier.mp4

08:30

8. Project02 Comparing Naive Bayes and Logistic Regression.mp4

15:40

9. Project03 Multiclass Classification With Naive Bayes Classifier.mp4

23:58

11. Support Vector Machines ( SVM )

1. Introduction of the Section.mp4

01:42

2. Basic Concept of SVM.mp4

07:01

3. Maths of SVM.mp4

13:41

4. Hard and Soft Margin Classifier.mp4

05:32

5. Decision rules of SVM.mp4

03:57

6. Kernel trick in SVM.mp4

07:23

7. Project01-Understanding SVM-Part01.mp4

20:05

8. Project01-Understanding SVM-Part02.mp4

10:11

9. Project02-Multiclass Classification With SVM.mp4

12:19

10. Project03-Grid Search CV-Part01.mp4

16:32

11. Project03-Grid Search CV-Part02.mp4

01:56

12. Project04-Breast Cancer Classification with SVM.mp4

07:53

12. Decision Tree

1. Introduction of the Section.mp4

02:30

2. Concept of Decision Tree.mp4

06:03

3. Important terms related to decision tree.mp4

05:30

4. Entropy-An information gain criterion.mp4

05:42

5. Numerical Example on Entropy-Part01.mp4

21:56

6. Numerical Example on Entropy-Part02.mp4

13:34

7. Gini Impurity - An information criterion.mp4

02:50

8. Numerical Example on Gini Impurity.mp4

19:21

9. Project01-Decision Tree Implementation.mp4

14:42

10. Project02-Breast Cancer Classification With Decision Tree.mp4

07:58

11. Project03-Grid Search CV with Decision Tree.mp4

22:02

13. Random Forest

1. Introduction of the Section.mp4

01:48

2. Why Random Forest.mp4

04:01

3. Working of Random Forest.mp4

04:18

4. Hyperparameters of Random Forest.mp4

08:18

5. Bootstrap sampling and OOB Error.mp4

07:51

6. Project01-Random Forest-Part01.mp4

10:12

7. Project01-Random Forest-Part02.mp4

08:05

8. Project02-Random Forest-Part01.mp4

10:27

9. Project02-Random Forest-Part02.mp4

07:14

14. Boosting Methods in Machine Learning

1. Introduction of the Section.mp4

01:10

2. AdaBoost (Adaptive Boosting ).mp4

11:08

3. Numerical Example on Adaboost.mp4

21:18

4. Project01-AdaBoost Classifier.mp4

13:25

5. Project02-AdaBoost Classifier.mp4

12:52

6. Gradient Boosting.mp4

02:50

7. Numerical Example on Gradient Boosting.mp4

11:36

8. Project03-Gradient Boosting.mp4

11:54

9. Project04-Gradient Boosting.mp4

13:17

10. Extreme Gradient Boosting ( XGBoost ).mp4

04:12

11. Project05-XGBoost-Part01.mp4

15:14

12. Project05-XGBoost-Part02.mp4

03:02

15. Deep Learning

1. Deep Learning.mp4

01:44

16. Introduction to Neural Networks and Deep Learning

1. Introduction of the Section.mp4

01:57

2. The perceptron.mp4

16:03

3. Features, Weights and Activation Function.mp4

07:01

4. Learning of Neural Network.mp4

09:34

5. Rise of Deep Learning.mp4

07:40

17. Activation Functions

1. Introduction of the Section.mp4

01:49

2. Classification by Perceptron-Part01.mp4

07:27

3. Classification by Perceptron-Part02.mp4

07:07

4. Need of Activation Functions.mp4

06:54

5. Adding Activation Function to Neural Network.mp4

04:58

6. Sigmoid as Activation Function.mp4

08:01

7. Hyperbolic Tangent Function.mp4

05:08

8. ReLU and Leaky ReLU Function.mp4

06:19

18. Loss Functions

1. Introduction of the Section.mp4

02:11

2. MSE Loss Function.mp4

04:14

3. Cross Entropy Loss Function.mp4

09:41

4. Softmax Function.mp4

08:34

19. Back Propagation

1. Introduction of the Section.mp4

02:12

2. Forward Propagation.mp4

08:41

3. Backward Propagation-Part01.mp4

18:26

4. Backward Propagation-Part02.mp4

09:47

20. Neural Networks for Regression Analysis

1. Introduction of the Section.mp4

01:43

2. Project01-Neural Network for Simple Regression-Part01.mp4

20:55

3. Project01-Neural Network for Simple Regression-Part02.mp4

18:54

4. Project02 Neural Network for Multiple Regression.mp4

14:04

5. Creating Neural Network Using Python Class.mp4

07:35

21. Neural Networks for Classification

1. Introduction of the Section.mp4

02:59

2. Epoch, Batch size and Iteration.mp4

04:15

3. Project00 Tensor Dataset and Data Loader.mp4

15:40

4. Code Preparation for Iris Dataset.mp4

06:12

5. Project01 Neural Network for Iris Data Classification.mp4

16:42

6. Code Preparation for MNIST dataset.mp4

05:22

7. Project02 Neural Network for MNIST data classification-Part01.mp4

17:58

8. Project02 Neural Network for MNIST data classification-Part02.mp4

23:46

9. Save and Load Trained model.mp4

05:20

10. Code Preparation for Custom Images.mp4

05:29

11. Project03-Neural Networks for Custom Images.mp4

22:02

12. Code Preparation for Human Action Recognition.mp4

01:54

13. Project04-Neural Network for Human Action Recognition.mp4

16:51

14. Project05-Neural Network for Feature Engineered Dataset.mp4

10:14

22. Dropout Regularization and Batch Normalization

1. Introduction of the Section.mp4

01:46

2. Dropout Regularization.mp4

11:35

3. Introducing Dataset for dropout Regularization.mp4

03:27

4. Project01-Dropout Regularization.mp4

27:12

5. Project02-Dropout Regularization.mp4

14:10

6. Batch Normalization.mp4

05:54

7. Project03-Batch Normalization.mp4

23:54

8. Project04-Batch Normalization.mp4

12:30

23. Convolutional Neural Network ( CNN )

1. Introduction of the Section.mp4

02:20

2. CNN Architecture and main operations.mp4

03:22

3. 2D Convolution.mp4

09:51

4. Shape of Feature Map after Convolution.mp4

12:04

5. Average and Maximum Pooling.mp4

06:24

6. Pooling to Classification.mp4

03:54

7. Project01-CNN on MNIST-Part01.mp4

24:12

8. Project01-CNN on MNIST-Part02.mp4

11:04

9. An Efficient Lazy Linear Layer.mp4

04:56

10. Project02 CNN on Custom Images.mp4

13:33

11. Transfer Learning.mp4

05:57

12. Project03-Transfer Learning With ResNet-18.mp4

14:30

13. Project04-Transfer Learning With VGG-16.mp4

06:59

24. Recurrent Neural Networks ( RNN )

1. Introduction of the Section.mp4

02:12

2. Why we need RNN .mp4

03:00

3. Sequential data.mp4

05:15

4. ANN to RNN.mp4

07:40

5. Back Propagation Through Time.mp4

13:29

6. Long-Short Term Memory ( LSTM ).mp4

04:25

7. LSTM Gates.mp4

10:09

8. Project01-LSTM Shapes.mp4

24:58

9. Project02-LSTM Basics.mp4

13:40

10. Batch size, Sequence length and Feature dimension.mp4

11:30

11. Project03-Interpolation and Extrapolation With LSTM.mp4

22:14

12. Project04- Data classification with LSTM.mp4

16:38

25. Autoencoders

1. Introduction of the Section.mp4

01:46

2. Architecture of Autoencoder.mp4

08:11

3. Applications of Autoencoders.mp4

07:03

4. Project01-Image Denoising using Autoencoder.mp4

16:39

5. Project02-Occlusion Removing Using Autoencoder.mp4

05:24

6. Project03-Autoencoder as an Image Classifier.mp4

13:29

26. Generative Adversarial Networks (GANs)

1. Introduction of the Section.mp4

01:06

2. Discriminative and Generative Models.mp4

02:41

3. Training of GAN.mp4

06:14

4. Project01 GAN Implementation.mp4

23:26

27. Unsupervised Machine Learning

1. Unsupervised Machine Learning.mp4

01:16

28. K-Means Clustering

1. Introduction of the Section.mp4

01:41

2. Steps of K-Means Clustering.mp4

07:56

3. Numerical Example- K-Means Clustering in One-D.mp4

09:24

4. Numerical Example-K-Means Clustering in 2D.mp4

11:50

5. Objective Function of K-Means Clustering.mp4

01:59

6. Selecting Optimal Number of Clusters ( Elbow Method ).mp4

08:20

7. Evaluating Metric for K-Means Clustering.mp4

10:45

8. Project01-K-Means Clustering-Part01.mp4

14:14

9. Project01-K-Means Clustering-Part02.mp4

12:08

10. Project01-K-Means Clustering-Part03.mp4

03:09

11. Project02-K-Means Clustering.mp4

09:34

12. Project03-K-Means Clustering.mp4

08:00

29. Hierarchical Clustering

1. Introduction of the Section.mp4

02:05

2. Hierarchical Clustering Algorithm.mp4

05:52

3. Hierarchical Clustering in One-D.mp4

04:50

4. Dendrograms-Selecting Optimal Clusters-Part01.mp4

07:00

5. Dendrograms-Selecting Optimal Clusters-Part02.mp4

06:53

6. Hierarchical Clustering Using d-max criterion.mp4

11:18

7. Hierarchical Clustering in 2D.mp4

06:43

8. Evaluating Metrics for Hierarchical Clustering.mp4

04:01

9. Project01-Hierarchical Clustering-Part01.mp4

12:29

10. Project01-Hierarchical Clustering-Part02.mp4

07:01

11. Project02-Hierarchical Clustering.mp4

06:34

12. Project03-Hierarchical Clustering.mp4

06:03

30. Density Based Spatial Clustering of Applications With Noise (DBSCAN)

1. Introduction of the Section.mp4

01:27

2. Definition of DBSCAN.mp4

05:16

3. Step by step DBSCAN.mp4

07:55

4. Comparing DBSCAN with K-Means Clustering.mp4

08:50

5. Project01-Part01.mp4

07:05

6. Project01-Part02.mp4

08:49

7. Parameters of DBSCAN.mp4

07:02

8. Project02-DBSCAN.mp4

11:13

31. Gaussian Mixture Model (GMM) Clustering

1. Introduction of the Section.mp4

01:24

2. Definition of GMM Clustering.mp4

02:29

3. Limitations of K-Means Clustering.mp4

07:27

4. Project01-GMM Clustering.mp4

17:16

5. Project02-GMM Clustering.mp4

12:54

6. Project03-GMM Clustering.mp4

09:51

7. Binomial Distribution.mp4

06:44

8. Expectation Maximization (EM) Algorithm.mp4

06:44

9. Expectation Maximization (EM) Algorithm ( Numerical Example ).mp4

16:12

32. Principal Component Analysis (PCA)

1. Introduction of the Section.mp4

01:49

2. Key Concepts of PCA.mp4

05:19

3. Need of PCA.mp4

07:30

4. PCA Algorithm With Numerical Example.mp4

23:03

5. Project01-PCA.mp4

12:47

6. Project02-PCA.mp4

10:04

7. Project03-PCA.mp4

04:54

8. Project04-PCA.mp4

07:26

9. Project05-PCA.mp4

06:11

10. Project06-PCA.mp4

08:28

Description

Practical Oriented Explanations by solving more than 80 projects with Numpy, Scikit-learn, Pandas, Matplotlib, Pytorch.

What You'll Learn?

Theory, Maths and Implementation of machine learning and deep learning algorithms.
Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, and Random Forest
Build Artificial Neural Networks and use them for Regression and Classification Problems
Using GPU with Neural Networks and Deep Learning Models.
Convolutional Neural Networks
Transfer Learning
Recurrent Neural Networks and LSTM
Time series forecasting and classification.
Autoencoders
Generative Adversarial Networks (GANs)
Python from scratch
Numpy, Matplotlib, Seaborn, Pandas, Pytorch, Scikit-learn and other python libraries.
More than 80 projects solved with Machine Learning and Deep Learning models

Who is this for?

Students in Machine Learning and Deep Learning course

Beginners Who want to Learn Machine Learning and Deep Learning from Scratch

Researchers in Artificial Intelligence

Students and Researchers who want to develop Python Programming skills to solve Machine Learning and Deep Learning Tasks

Those who know Matlab and Other Programming Languages and want to switch to Python for Machine Learning and Deep Learning

What You Need to Know?

Some Programming Knowledge is preferable but not necessary

Gmail account ( For Google Colab )

More details

Description
Introduction
Introduction of the Course
Introduction to Machine Learning and Deep Learning
Introduction to Google Colab
Python Crash Course
Data Preprocessing

Supervised Machine Learning
Regression Analysis
Logistic Regression
K-Nearest Neighbor (KNN)
Bayes Theorem and Naive Bayes Classifier
Support Vector Machine (SVM)
Decision Trees
Random Forest
Boosting Methods in Machine Learning
Introduction to Neural Networks and Deep Learning
Activation Functions
Loss Functions
Back Propagation
Neural Networks for Regression Analysis
Neural Networks for Classification
Dropout Regularization and Batch Normalization
Convolutional Neural Network (CNN)
Recurrent Neural Network (RNN)
Autoencoders
Generative Adversarial Network (GAN)

Unsupervised Machine Learning
K-Means Clustering
Hierarchical Clustering
Density Based Spatial Clustering Of Applications With Noise (DBSCAN)
Gaussian Mixture Model (GMM) Clustering
Principal Component Analysis (PCA)

What youâ€™ll learn

Theory, Maths and Implementation of machine learning and deep learning algorithms.
Regression Analysis.
Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, Random Forest, and Boosting Methods in Machine Learning.
Build Artificial Neural Networks and use them for Regression and Classification Problems.
Using GPU with Deep Learning Models.
Convolutional Neural Networks
Transfer Learning
Recurrent Neural Networks
Time series forecasting and classification.
Autoencoders
Generative Adversarial Networks
Python from scratch
Numpy, Matplotlib, seaborn, Pandas, Pytorch, scikit-learn and other python libraries.
More than 80 projects solved with Machine Learning and Deep Learning models.

Who this course is for:
Students in Machine Learning and Deep Learning course
Beginners Who want to Learn Machine Learning and Deep Learning from Scratch
Researchers in Artificial Intelligence
Students and Researchers who want to develop Python Programming skills to solve Machine Learning and Deep Learning Tasks
Those who know Matlab and Other Programming Languages and want to switch to Python for Machine Learning and Deep Learning

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Dr. Zeeshan is PhD in Electrical and Computer Engineering from Ryerson University Toronto. He has more than 18 years of teaching and research experience. He has taught many courses related to Computer and Electrical Engineering. His research interests include Machine learning, Deep learning, Computer vision, Signal and Image processing and multimodal fusion. He has publications in reputed journals and conferences.

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