Python for Machine Learning & Deep Learning in One Semester

سرفصل های دوره

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

1. Introduction and Course Material

1. Introduction of the course

2.1 Course Material.zip

2. Course Material.html

2. Introduction to Machine Learning and Deep Learning

1. Introduction of the Section

2. What in Intelligence

3. Machine Learning

4. Supervised Machine Learning

5.1 5-Unsupervised Machine Learning

5. Unsupervised Machine Learning

6. Deep Learning

3. Introduction to Google Colab

1. Introduction of the Section

2. Importing Dataset in Google Colab

3. Importing and Displaying Image in Google Colab

4. Importing more datasets

5. Uploading Course Material on your Google Drive

4. Python Crash Course

1. Introduction of the Section

2. Arithmetic With Python

3. Comparison and Logical Operations

4. Conditional Statements

5. Dealing With Numpy Arrays-Part01

6. Dealing With Numpy Arrays-Part02

7. Dealing With Numpy Arrays-Part03

8. Plotting and Visualization-Part01

9. Plotting and Visualization-Part02

10. Plotting and Visualization-Part03

11. Plotting and Visualization-Part04

12. Lists in Python

13. For Loops-Part01

14. For Loops-Part02

15. Strings

16. Print Formatting With Strings

17. Dictionaries-Part01

18. Dictionaries-Part02

19. Functions in Python-Part01

20. Functions in Python-Part02

21. Pandas-Part01

22. Pandas-Part02

23. Pandas-Part03

24. Pandas-Part04

25. Seaborn-Part01

26. Seaborn-Part02

27. Seaborn-Part03

28. Tuples

29. Classes in Python

5. Data Preprocessing

1. Introduction of the Section

2. Need of Data Preprocessing

3. Data Normalization and Min-Max Scaling

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

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

6. Data Standardization

7. Project02-Data Standardization

8. Project03-Dealing With Missing Values

9. Project04-Dealing With Categorical Features

10. Project05-Feature Engineering

11. Project06-Feature Engineering by Window Method

6. Supervised Machine Learning

1. Supervised Machine Learning

7. Regression Analysis

1. Introduction of the Section

2. Origin of the Regression

3. Definition of Regression

4. Requirement from Regression

5. Simple Linear Regression

6. Multiple Linear Regression

7. Target and Predicted Values

8. Loss Function

9. Regression With Least Square Method

10. Least Square Method With Numerical Example

11. Evaluation Metrics for Regression

12. Project01-Simple Regression-Part01

13. Project01-Simple Regression-Part02

14. Project01-Simple Regression-Part03

15. Project02-Multiple Regression-Part01

16. Project02-Multiple Regression-Part02

17. Project02-Multiple Regression-Part03

18. Project03-Another Multiple Regression

19. Regression by Gradient Descent

20. Project04-Simple Regression With Gradient Descent

21. Project05-Multiple Regression With Gradient Descent

22. Polynomial Regression

23. Project06-Polynomial Regression

24. Cross-validation

25. Project07-Cross-validation

26. Underfitting and Overfitting ( Bias-Variance Tradeoff )

27. Concept of Regularization

28. Ridge Regression OR L2 Regularization

29. Lasso Regression OR L1 Regularization

30. Comparing Ridge and Lasso Regression

31. Elastic Net Regularization

32. Project08-Regularizations

33. Grid search Cross-validation

34. Project09-Grid Search Cross-validation

8. Logistic Regression

1. Introduction of the Section

2. Fundamentals of Logistic Regression

3. Limitations of Regression Models

4. Transforming Linear Regression into Logistic Regression

5. Project01-Getting Class Probabilities-Part01

6. Project01-Getting Class Probabilities-Part02

7. Loss Function

8. Model Evaluation-Confusion Matrix

9. Accuracy, Precision, Recall and F1-Score

10. ROC Curves and Area Under ROC

11. Project02-Evaluating Logistic Regression Model

12. Project03-Cross-validation With Logistic Regression Model

13. Project04-Multiclass Classification

14. Project05-Classification With Challenging Dataset-Part01

15. Project05-Classification With Challenging Dataset-Part02

16. Project05-Classification With Challenging Dataset-Part03

17. Grid Search Cross-validation With Logistic Regression

9. K-Nearest Neighbors ( KNN )

1. Introduction of the Section

2. Intuition Behind KNN

3. Steps of KNN Algorithm

4. Numerical Example on KNN Algorithm

5. Project01-KNN Algorithm-Part01

6. Project01-KNN Algorithm-Part02

7. Finding Optimal Value of K

8. Project02-Implementing KNN

9. Project03-Implementing KNN

10. Project04-Implementing KNN

11. Advantages and disadvantages of KNN

10. Bayes Theorem and Naive Bayes Classifier

1. Introduction of the section

2. Fundamentals of Probability

3. Conditional Probability and Bayes Theorem

4. Numerical Example on Bayes Theorem

5. Naive Bayes Classification

6. Comparing Naive Bayes Classification With Logistic Regression

7. Project01 Naive Bayes as probabilistic classifier

8. Project02 Comparing Naive Bayes and Logistic Regression

9. Project03 Multiclass Classification With Naive Bayes Classifier

11. Support Vector Machines ( SVM )

1. Introduction of the Section

2. Basic Concept of SVM

3. Maths of SVM

4. Hard and Soft Margin Classifier

5. Decision rules of SVM

6. Kernel trick in SVM

7. Project01-Understanding SVM-Part01

8. Project01-Understanding SVM-Part02

9. Project02-Multiclass Classification With SVM

10. Project03-Grid Search CV-Part01

11. Project03-Grid Search CV-Part02

12. Project04-Breast Cancer Classification with SVM

12. Decision Tree

1. Introduction of the Section

2. Concept of Decision Tree

3. Important terms related to decision tree

4. Entropy-An information gain criterion

5. Numerical Example on Entropy-Part01

6. Numerical Example on Entropy-Part02

7. Gini Impurity - An information criterion

8. Numerical Example on Gini Impurity

9. Project01-Decision Tree Implementation

10. Project02-Breast Cancer Classification With Decision Tree

11. Project03-Grid Search CV with Decision Tree

13. Random Forest

1. Introduction of the Section

2. Why Random Forest

3. Working of Random Forest

4. Hyperparameters of Random Forest

5. Bootstrap sampling and OOB Error

6. Project01-Random Forest-Part01

7. Project01-Random Forest-Part02

8. Project02-Random Forest-Part01

9. Project02-Random Forest-Part02

14. Boosting Methods in Machine Learning

1. Introduction of the Section

2. AdaBoost (Adaptive Boosting )

3. Numerical Example on Adaboost

4. Project01-AdaBoost Classifier

5. Project02-AdaBoost Classifier

6. Gradient Boosting

7. Numerical Example on Gradient Boosting

8. Project03-Gradient Boosting

9. Project04-Gradient Boosting

10. Extreme Gradient Boosting ( XGBoost )

11. Project05-XGBoost-Part01

12. Project05-XGBoost-Part02

15. Deep Learning

1. Deep Learning

16. Introduction to Neural Networks and Deep Learning

1. Introduction of the Section

2. The perceptron

3. Features, Weights and Activation Function

4. Learning of Neural Network

5. Rise of Deep Learning

17. Activation Functions

1. Introduction of the Section

2. Classification by Perceptron-Part01

3. Classification by Perceptron-Part02

4. Need of Activation Functions

5. Adding Activation Function to Neural Network

6. Sigmoid as Activation Function

7. Hyperbolic Tangent Function

8. ReLU and Leaky ReLU Function

18. Loss Functions

1. Introduction of the Section

2. MSE Loss Function

3. Cross Entropy Loss Function

4. Softmax Function

19. Back Propagation

1. Introduction of the Section

2. Forward Propagation

3. Backward Propagation-Part01

4. Backward Propagation-Part02

20. Neural Networks for Regression Analysis

1. Introduction of the Section

2. Project01-Neural Network for Simple Regression-Part01

3. Project01-Neural Network for Simple Regression-Part02

4. Project02 Neural Network for Multiple Regression

5. Creating Neural Network Using Python Class

21. Neural Networks for Classification

1. Introduction of the Section

2. Epoch, Batch size and Iteration

3. Project00 Tensor Dataset and Data Loader

4. Code Preparation for Iris Dataset

5. Project01 Neural Network for Iris Data Classification

6. Code Preparation for MNIST dataset

7. Project02 Neural Network for MNIST data classification-Part01

8. Project02 Neural Network for MNIST data classification-Part02

9. Save and Load Trained model

10. Code Preparation for Custom Images

11. Project03-Neural Networks for Custom Images

12. Code Preparation for Human Action Recognition

13. Project04-Neural Network for Human Action Recognition

14. Project05-Neural Network for Feature Engineered Dataset

22. Dropout Regularization and Batch Normalization

1. Introduction of the Section

2. Dropout Regularization

3. Introducing Dataset for dropout Regularization

4. Project01-Dropout Regularization

5. Project02-Dropout Regularization

6. Batch Normalization

7. Project03-Batch Normalization

8. Project04-Batch Normalization

23. Convolutional Neural Network ( CNN )

1. Introduction of the Section

2. CNN Architecture and main operations

3. 2D Convolution

4. Shape of Feature Map after Convolution

5. Average and Maximum Pooling

6. Pooling to Classification

7. Project01-CNN on MNIST-Part01

8. Project01-CNN on MNIST-Part02

9. An Efficient Lazy Linear Layer

10. Project02 CNN on Custom Images

11. Transfer Learning

12. Project03-Transfer Learning With ResNet-18

13. Project04-Transfer Learning With VGG-16

24. Recurrent Neural Networks ( RNN )

1. Introduction of the Section

2. Why we need RNN

3. Sequential data

4. ANN to RNN

5. Back Propagation Through Time

6. Long-Short Term Memory ( LSTM )

7. LSTM Gates

8. Project01-LSTM Shapes

9. Project02-LSTM Basics

10. Batch size, Sequence length and Feature dimension

11. Project03-Interpolation and Extrapolation With LSTM

12. Project04- Data classification with LSTM

25. Autoencoders

1. Introduction of the Section

2. Architecture of Autoencoder

3. Applications of Autoencoders

4. Project01-Image Denoising using Autoencoder

5. Project02-Occlusion Removing Using Autoencoder

6. Project03-Autoencoder as an Image Classifier

26. Generative Adversarial Networks (GANs)

1. Introduction of the Section

2. Discriminative and Generative Models

3. Training of GAN

4. Project01 GAN Implementation

27. Unsupervised Machine Learning

1. Unsupervised Machine Learning

28. K-Means Clustering

1. Introduction of the Section

2. Steps of K-Means Clustering

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

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

5. Objective Function of K-Means Clustering

6. Selecting Optimal Number of Clusters ( Elbow Method )

7. Evaluating Metric for K-Means Clustering

8. Project01-K-Means Clustering-Part01

9. Project01-K-Means Clustering-Part02

10. Project01-K-Means Clustering-Part03

11. Project02-K-Means Clustering

12. Project03-K-Means Clustering

29. Hierarchical Clustering

1. Introduction of the Section

2. Hierarchical Clustering Algorithm

3. Hierarchical Clustering in One-D

4. Dendrograms-Selecting Optimal Clusters-Part01

5. Dendrograms-Selecting Optimal Clusters-Part02

6. Hierarchical Clustering Using d-max criterion

7. Hierarchical Clustering in 2D

8. Evaluating Metrics for Hierarchical Clustering

9. Project01-Hierarchical Clustering-Part01

10. Project01-Hierarchical Clustering-Part02

11. Project02-Hierarchical Clustering

12. Project03-Hierarchical Clustering

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

1. Introduction of the Section

2. Definition of DBSCAN

3. Step by step DBSCAN

4. Comparing DBSCAN with K-Means Clustering

5. Project01-Part01

6. Project01-Part02

7. Parameters of DBSCAN

8. Project02-DBSCAN

31. Gaussian Mixture Model (GMM) Clustering

1. Introduction of the Section

2. Definition of GMM Clustering

3. Limitations of K-Means Clustering

4. Project01-GMM Clustering

5. Project02-GMM Clustering

6. Project03-GMM Clustering

7. Binomial Distribution

8. Expectation Maximization (EM) Algorithm

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

32. Principal Component Analysis (PCA)

1. Introduction of the Section

2. Key Concepts of PCA

3. Need of PCA

4. PCA Algorithm With Numerical Example

5. Project01-PCA

6. Project02-PCA

7. Project03-PCA

8. Project04-PCA

9. Project05-PCA

10. Project06-PCA

179,000 تومان

افزودن به سبد خرید

خرید دانلودی فوری

در این روش نیاز به افزودن محصول به سبد خرید و تکمیل اطلاعات نیست و شما پس از وارد کردن ایمیل خود و طی کردن مراحل پرداخت لینک های دریافت محصولات را در ایمیل خود دریافت خواهید کرد.

تولید کننده: Udemy