Machine Learning for Interviews & Research and DL basics

سرفصل های دوره

Machine Learning, Linear Regression, PCA, Neural Networks, Hyperparameters, Deep Learning, Keras, Clustering, Case Study

1. Advanced Statistics and Machine Learning

1. Types of Machine Learning

2. Parametric Models

3. Non-parametric Models

4. Central Limit Theorem. Gaussian Distribution. ML framework

5. Dimensionality Reducing Principle Component Analysis - Eigen Decomposition

6. Quiz on Statistics and PCA.html

2. Training Machine Learning Models

1. Supervised Machine Learning

2. Regression

3. Classification

4. Linear Regression

5. Gradient Descent

6. Tips for Gradient Descent

7. Normal Equations

8. Non-parametric method - Locally Weighted Linear Regression

9. Ridge Regression

10. Lasso Regression

11. Classification Models in sklearn

12. Classification Model - Logistic Regression

13. Mapping non-linear functions using linear techniques

14. Overfitting and Regularization

15. Support Vector Machines

16. Decision Trees

17. Quiz - Section 2.html

3. Neural Networks

1. Neural Networks Forward Propagation Backward Propagation GDstochasticMinibatch

2. Tuning Hyperparameters in Neural Network

4. Training Deep Neural Networks

1. Deep Learning - Requirements

2. Common Tricks for building a Deep NN & Improving accuracy performance

3. Overfitting - Regularization - Dropout

4. Batch Normalization

5. Is it possible for deeper networks to be faster than shallow networks ResNet

6. Convolutional Neural Networks

7. Maximum Pooling Layers

8. Recurrent Neural Networks

9. LSTM Units

10. GRU Units

5. Unsupervised Learning

1. Clustering

6. Implementation and Case Studies

1. Getting started with Python and Machine Learning

2. Case Study - Using Keras - Digits Classification

3. Case Study - Load Forecasting

4. Case Study - Multiple Linear Regression

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تولید کننده: Udemy-Training