COVID-19 STATEMENT: While this virus is impacting everyone differently, this online program is continuing as planned.
Please consider joining our global online classroom for an enriching and interactive experience to further your career.

STARTS ON

June 28, 2019

DURATION

3 Months, Online
6-8 hours per week

COURSE FEES

$1,400

info Flexible payment available

What is the Practical Machine Learning course about?

This courses teaches machine learning from a practitioner’s perspective. If you want to get started with machine learning and learn the easy and practical way, this course is appropriate for you.

This course will provide an easy-to-follow roadmap to frame a given business problem and identify steps toward training, testing, scoring, and deploying an appropriate model for the situation.

Learning Outcome

At the end of the course, you will be able to

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Your Learning Journey

116 Video Lectures

18 R-Studio Demos

11 Quizzes

6 Exercises / Assessments

5 Assignments

  • Module 1: Machine Learning Fundamentals
  • Module 2: Exploratory Data Analysis
  • Module 3: Linear Regression
  • Module 4: Classification Modeling and Logistic Regression
  • Module 5: Classification: Model Evaluation and Reporting
  • Module 6: Decision Trees
  • Module 7: Resampling
  • Module 8: Improving Model Performance
  • Module 9: Neural Networks
  • Module 10: Introduction to Unsupervised Learning
  • Module 11: Model Deployment
  • Module 12: Problem Framing: Recommendation Engines & CLV

PREREQUISITES: The course requires an undergraduate knowledge of statistics (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation etc.) and probability. Familiarity with R (importing a data set, assigning variables, working with a variety of data structures like. numeric, character, factor etc., creating and adding columns to data frames) is required.

Assignments /application projects which require programming will be done using the R programming language.

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Application Assignments

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Faculty

Christopher Brown
Christopher Brown
Adjunct Professor in UC Berkeley's Department of Computer Science and a Founding Partner of Decision Patterns

For the past 15 years, Christopher Brown has worked as a data science consultant in a variety of industries, from financial services and healthcare to...
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Kristen Kehrer
Kristen Kehrer
Course Instructor, EMERITUS

Kristen is #8 LinkedIn Global Top Voice 2018 – Data Science & Analytics. Since 2010, Kristen has been a data scientist across multiple industries, including...
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Certificate of Completion

Upon successful completion of the course, participants will receive a certificate from UC Berkeley Extension.

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Early applications encouraged. Seats fill up soon!

Flexible payment options available. Click here to know more.