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Statistics.comX: Predictive Analytics: Basic Modeling Techniques
What is Predictive Analytics? These methods lie behind the most transformative technologies of the last decade, that go under the more general name Artificial Intelligence or AI. In this course, the focus is on the skills that will allow you to fit a model to data, and measure how well it performs. We will be doing enough data science so that you get hands-on familiarity with understanding a dataset, fitting a model to it, and generating predictions.
This Course Includes
edx
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4 weeks at 5-7 hours per week
english
Online - Self Paced
course
Statistics.comX
About Statistics.comX: Predictive Analytics: Basic Modeling Techniques
What is Predictive Analytics? These methods lie behind the most transformative technologies of the last decade, that go under the more general name Artificial Intelligence or AI. In this course, the focus is on the skills that will allow you to fit a model to data, and measure how well it performs.
These skills also go under the names "machine learning" and "data science," the latter being a broader term than machine learning or predictive analytics but narrower than AI. This course is part of the Machine Learning Operations (MLOps) Program. We will be doing enough data science so that you get hands-on familiarity with understanding a dataset, fitting a model to it, and generating predictions. As you get further into the program, you will learn how to fit that model into a machine learning pipeline.
You will get hands-on experience with the top techniques in supervised learning: linear and logistic regression modeling, decision trees, neural networks, ensembles, and much more.
But most importantly, by the end of this course, you will know
What a predictive model can (and cannot) do, and how its data is structured
How to predict a numerical output, or a class (category)
How to measure the out-of-sample (future)performance of a model
What You Will Learn?
- Develop a variety of machine learning algorithms for both classification and regression, including linear and logistic regression, decisions trees and neural networks .
- Evaluate machine learning model performance with appropriate metrics .
- Combine multiple models into ensembles to improve performance .
- Explain the special contribution that deep learning has made to machine learning task .