Interpreting Data with Advanced Statistical Models

سرفصل های دوره

Machine Learning is changing the world and at the very core of machine learning are advanced statistical models. With this course, you will know how to create an ML application for problems that appear at your work and understand the basis behind it

1. Course Overview

1. Course Overview

2. Getting Started with Machine Learning

1. Getting Started with Machine Learning

2. Unsupervised Learning - I Dont Wanna Miss a Thing!

3. Supervised Learning - When We Learn from History

4. Demo

5. Summary

3. Finding Those Models

1. How to Learn in Machine Learning Cost Functions!

2. Finding the Minima - GD and SGD

3. Making Things Faster - Feature Scaling and Learning Rates

4. How It All Fits - Going Back to the Model!

5. Demo - Implementing GD and SGD

6. Summary

4. Predicting Linear Relationships with Regression

1. Back to the Basics - Linear Regression Again

2. Hyperparameter Optimization - TrainDevTest Sets

3. Demo - Perform Simple Linear Regression

4. What if I Want More Variables Multiple Regression to the Rescue!

5. Demo - Perform Multiple Linear Regression

6. What No One Talks About - Assumptions

7. Demo - Evaluate a Regression Model

8. Summary

5. Understanding Regression Models in Depth

1. Non-linear Regression - Polynomial Features

2. Overfitting - A Great Responsibility Conveys a Great Regularization

3. Demo - Linear Regression with Regularization

4. Demo - Perform Polynomial Regression

5. Outliers Strike Again - Spline Regression as Local Regressor

6. Demo - Perform a Spline Regression

7. Model Selection - Let the Simplest Model Win

8. Demo - Comparing Models

9. Summary

6. The Problem of Correct Classification

1. What Does the Boundary Look Like

2. If You Need to Classify, Try the Star - Logistic Regression in Depth!

3. Demo - Classify with Logistic Regression

4. Classify Multiple Categories - One vs. All Classification

5. Demo - Multiclass Classification with Multinomial Logistic Regression

6. Summary

7. Large Margin and Bayesian Classification

1. Maximizing the Actual Information - Bayesian Attack!

2. Demo - Multilabel Intent Classification with Naive Bayes

3. Large Margin Classification - Outliers!

4. Passing from Linear Boundaries to Nonlinear Ones - Kernel Trick

5. Demo - Classify Iris with SVM

6. Summary

8. The Subtle Art of Not Needing Labels - Unsupervised Learning

1. Distance and Covariance Matrices

2. Clustering - Hierarchical and Non-hierarchical

3. Compression - PCA and CA

4. Demo - Perform PCA

5. Demo - Breast Cancer at a Glance

6. Summary

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تولید کننده: PluralSight