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.
Author Name: Axel Sirota
Author Description:
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.

Table of Contents

• Course Overview
  1min
• Getting Started with Machine Learning
  18mins
• Finding Those Models
  24mins
• Predicting Linear Relationships with Regression
  29mins
• Understanding Regression Models in Depth
  38mins
• The Problem of Correct Classification
  27mins
• Large Margin and Bayesian Classification
  19mins
• The Subtle Art of Not Needing Labels: Unsupervised Learning
  29mins