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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.

Predictive Analytics: Basic Modeling Techniques
4 weeks
5–7 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

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Starts Nov 28
Ends Dec 31
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Predictive Analytics: Basic Modeling Techniques

About this course

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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

At a glance

  • Institution: Statistics.comX
  • Subject: Data Analysis & Statistics
  • Level: Intermediate
  • Prerequisites:
    • Python
    • Statistics

    We will present Python code to illustrate how to fit models, so we assume some familiarity with Python. Some exposure to basic statistics is also helpful, more from a comfort perspective than from a need to dive deep into statistical routines.

  • Language: English
  • Video Transcript: English

What you'll learn

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After completing this course, you will be able to:

  • 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

Syllabus

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Week 1 – Data Structures; Linear and Logistic Regression

  • Classification and Regression
  • Rectangular Data
  • Regression
  • Partitioning and Overfitting
  • Illustration - Linear Regression (for verified users)
  • Knowledge Check 1.1
  • Logistic Regression
  • Illustration - Logistic Regression (for verified users)
  • Understand and Prepare Data
  • Visualization
  • CRISP-DM framework
  • P-Values
  • Knowledge Check 1.2
  • Discussion Prompt #1 (for verified students, graded)
  • Quiz #1 (for verified students, graded)
  • Exercise #1 - Linear Regression (for verified students, graded)
  • Exercise #2 - Logistic Regression (for verified students, graded)
  • Summary

Week 2 - Assessing Models; Decision Trees

  • Assessing Model Performance: Metrics
  • ROC Curve and Gains Chart
  • Decision Trees
  • Illustration - Classification Tree (for verified users)
  • Knowledge Check 2
  • Quiz #2 (for verified students, graded)
  • Exercise #3 - Regression Tree (for verified students, graded)
  • Exercise #4 - Classification Tree (for verified students, graded)
  • Summary

Week 3 – Ensembles

  • Cross validation
  • Module 3 Reading
  • Ensembles
  • Illustration - Ensemble Methods (for verified users)
  • Knowledge Check 3
  • Discussion Prompt #2 (for verified students, graded)
  • Quiz #3 (for verified students, graded)
  • Exercise #5 - Ensemble Methods (for verified students, graded)
  • Summary

Week 4 - Neural Networks

  • Neural Nets
  • Illustration - Neural Nets (for verified users)
  • Deep Learning
  • Reading
  • Knowledge Check 4
  • Quiz #4 (for verified students, graded)
  • Exercise #6 - Neural Nets (for verified students, graded)
  • Summary

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

This course is part of Machine Learning Operations with Google Cloud Platform (MLOps with GCP) Professional Certificate Program

Learn more 
Expert instruction
3 skill-building courses
Self-paced
Progress at your own speed
3 months
5 - 7 hours per week

Ways to take this course

Choose your path when you enroll.

Certificate

Free

Price

$189 USD

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Access to course materials

Unlimited

Limited

Expires on Dec 26

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Shareable certificate upon completion

Graded assignments and exams

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