About this Course
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Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 11 hours to complete

Suggested: 5 hours/week...

English

Subtitles: English

What you will learn

  • Check

    Understand the definitions of simple error measures (e.g. MSE, accuracy, precision/recall).

  • Check

    Evaluate the performance of regressors / classifiers using the above measures.

  • Check

    Understand the difference between training/testing performance, and generalizability.

  • Check

    Understand techniques to avoid overfitting and achieve good generalization performance.

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 11 hours to complete

Suggested: 5 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Week 1: Diagnostics for Data

6 videos (Total 49 min), 4 readings, 3 quizzes
6 videos
Over- and Under-Fitting6m
Classification Diagnostics: Accuracy and Error11m
Classification Diagnostics: Precision and Recall12m
4 readings
Syllabus10m
Setting Up Your System10m
(Optional) Additional Resources and Recommended Readings10m
Course Materials10m
3 practice exercises
Review: Regression Diagnostics8m
Review: Classification Diagnostics4m
Diagnostics for Data30m
Week
2
2 hours to complete

Week 2: Codebases, Regularization, and Evaluating a Model

4 videos (Total 35 min), 4 quizzes
4 practice exercises
Review: Setting Up a Codebase2m
Review: Regularization5m
Review: Evaluating a Model5m
Codebases, Regularization, and Evaluating a Model45m
Week
3
1 hour to complete

Week 3: Validation and Pipelines

4 videos (Total 24 min), 3 quizzes
4 videos
Guidelines on the Implementation of Predictive Pipelines5m
3 practice exercises
Review: Validation4m
Review: Predictive Pipelines6m
Predictive Pipelines20m
Week
4
2 hours to complete

Final Project

2 readings, 1 quiz
2 readings
Project Description10m
Where to Find Datasets10m

Instructors

Avatar

Julian McAuley

Assistant Professor
Computer Science
Avatar

Ilkay Altintas

Chief Data Science Officer
San Diego Supercomputer Center

About University of California San Diego

UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory....

About the Python Data Products for Predictive Analytics Specialization

Python data products are powering the AI revolution. Top companies like Google, Facebook, and Netflix use predictive analytics to improve the products and services we use every day. Take your Python skills to the next level and learn to make accurate predictions with data-driven systems and deploy machine learning models with this four-course Specialization from UC San Diego. This Specialization is for learners who are proficient with the basics of Python. You’ll start by creating your first data strategy. You’ll also develop statistical models, devise data-driven workflows, and learn to make meaningful predictions for a wide-range of business and research purposes. Finally, you’ll use design thinking methodology and data science techniques to extract insights from a wide range of data sources. This is your chance to master one of the technology industry’s most in-demand skills. Python Data Products for Predictive Analytics is taught by Professor Ilkay Altintas, Ph.D. and Julian McAuley. Dr. Alintas is a prominent figure in the data science community and the designer of the highly-popular Big Data Specialization on Coursera. She has helped educate hundreds of thousands of learners on how to unlock value from massive datasets....
Python Data Products for Predictive Analytics

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.