Interpreting Data with Advanced Statistical Models

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Axel Sirota

3:09:34

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1. Course Overview

1. Course Overview.mp4

01:50

2. Getting Started with Machine Learning

1. Getting Started with Machine Learning.mp4

02:57

2. Unsupervised Learning - I Dont Wanna Miss a Thing!.mp4

04:54

3. Supervised Learning - When We Learn from History.mp4

06:26

4. Demo.mp4

03:27

5. Summary.mp4

00:45

3. Finding Those Models

1. How to Learn in Machine Learning Cost Functions!.mp4

04:05

2. Finding the Minima - GD and SGD.mp4

05:18

3. Making Things Faster - Feature Scaling and Learning Rates.mp4

05:10

4. How It All Fits - Going Back to the Model!.mp4

02:56

5. Demo - Implementing GD and SGD.mp4

06:00

6. Summary.mp4

00:33

4. Predicting Linear Relationships with Regression

1. Back to the Basics - Linear Regression Again.mp4

04:29

2. Hyperparameter Optimization - TrainDevTest Sets.mp4

06:15

3. Demo - Perform Simple Linear Regression.mp4

03:11

4. What if I Want More Variables Multiple Regression to the Rescue!.mp4

02:45

5. Demo - Perform Multiple Linear Regression.mp4

02:36

6. What No One Talks About - Assumptions.mp4

04:04

7. Demo - Evaluate a Regression Model.mp4

05:47

8. Summary.mp4

00:49

5. Understanding Regression Models in Depth

1. Non-linear Regression - Polynomial Features.mp4

03:24

2. Overfitting - A Great Responsibility Conveys a Great Regularization.mp4

05:29

3. Demo - Linear Regression with Regularization.mp4

05:17

4. Demo - Perform Polynomial Regression.mp4

04:53

5. Outliers Strike Again - Spline Regression as Local Regressor.mp4

02:24

6. Demo - Perform a Spline Regression.mp4

03:39

7. Model Selection - Let the Simplest Model Win.mp4

08:10

8. Demo - Comparing Models.mp4

04:19

9. Summary.mp4

00:53

6. The Problem of Correct Classification

1. What Does the Boundary Look Like.mp4

04:25

2. If You Need to Classify, Try the Star - Logistic Regression in Depth!.mp4

05:30

3. Demo - Classify with Logistic Regression.mp4

10:42

4. Classify Multiple Categories - One vs. All Classification.mp4

03:17

5. Demo - Multiclass Classification with Multinomial Logistic Regression.mp4

02:58

6. Summary.mp4

00:32

7. Large Margin and Bayesian Classification

1. Maximizing the Actual Information - Bayesian Attack!.mp4

03:25

2. Demo - Multilabel Intent Classification with Naive Bayes.mp4

04:20

3. Large Margin Classification - Outliers!.mp4

03:53

4. Passing from Linear Boundaries to Nonlinear Ones - Kernel Trick.mp4

04:18

5. Demo - Classify Iris with SVM.mp4

02:38

6. Summary.mp4

01:05

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

1. Distance and Covariance Matrices.mp4

05:46

2. Clustering - Hierarchical and Non-hierarchical.mp4

07:15

3. Compression - PCA and CA.mp4

05:30

4. Demo - Perform PCA.mp4

02:24

5. Demo - Breast Cancer at a Glance.mp4

07:07

6. Summary.mp4

01:44

Description

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

What You'll Learn?

When you look at the core of machine learning, there are advanced statistical models. In this course, Interpreting Data with Advanced Statistical Models, you will gain the ability to effectively understand how to create an ML application that will be able to revolutionize the problems that appear at your work. First, you will learn the basic of Machine learning. Next, you will discover linear regression in a more general pattern, expanding to multiple and polynomial features. Continuing, you will explore how to classify with Logistic Regression, SVMs, and Bayesian methods. Finally, you will learn the intrinsic patterns of data with unsupervised techniques such as K Means and PCA. When you’re finished with this course, you will have the skills and knowledge of Machine Learning needed to apply it in a real-world application.

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Statistics

Data Modeling

Axel Sirota

Instructor's Courses

Axel Sirota is a Microsoft Certified Trainer with a deep interest in Deep Learning and Machine Learning Operations. He has a Masters degree in Mathematics and after researching in Probability, Statistics and Machine Learning optimisation, he works as an AI and Cloud Consultant as well as being an Author and Instructor at Pluralsight, Develop Intelligence, and O'Reilly Media.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.