Professional Course

Predictive Analytics: Basic Modeling Techniques

edX, Online
4 weeks
149 USD
Next course start
Start anytime See details
Self-paced Online
4 weeks
149 USD
Next course start
Start anytime See details
Self-paced Online
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Course description

Predictive Analytics: Basic Modeling Techniques

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

Upcoming start dates

1 start date available

Start anytime

  • Self-paced Online
  • Online
  • English

Who should attend?


  • 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.

Training content

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

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


  • 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

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

Course delivery details

This course is offered through, a partner institute of EdX.

5-7 hours per week


  • Verified Track -$149
  • Audit Track - Free

Certification / Credits

What you'll learn

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

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