This course is part of the Python Data Products for Predictive Analytics Specialization

4.4
stars
46 ratings

Julian McAuley +1 more instructor

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Python Data Products for Predictive Analytics SpecializationUniversity of California San Diego

About this Course

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This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better?
By the end of this course you will be familiar with diagnostic techniques that allow you to evaluate and compare classifiers, as well as performance measures that can be used in different regression and classification scenarios. We will also study the training/validation/test pipeline, which can be used to ensure that the models you develop will generalize well to new (or "unseen") data.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Course 3 of 4 in the

Python Data Products for Predictive Analytics Specialization

Intermediate Level

Approx. 8 hours to complete

English

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What you will learn

Understand the definitions of simple error measures (e.g. MSE, accuracy, precision/recall).
Evaluate the performance of regressors / classifiers using the above measures.
Understand the difference between training/testing performance, and generalizability.
Understand techniques to avoid overfitting and achieve good generalization performance.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 4 in the

Python Data Products for Predictive Analytics Specialization

Intermediate Level

Approx. 8 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.5/5 (7 Ratings)

Julian McAuley

Ilkay Altintas

Chief Data Science Officer

San Diego Supercomputer Center

459,913 Learners

13 Courses

Offered by

University of California San Diego

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Syllabus - What you will learn from this course

Week1

Week 1

3 hours to complete

Week 1: Diagnostics for Data

3 hours to complete

6 videos (Total 49 min), 4 readings, 3 quizzes

6 videos

Introduction to Course 3: Meaningful Predictive Modeling3mMotivation Behind the MSE8mRegression Diagnostics: MSE and R²6mOver- and Under-Fitting6mClassification Diagnostics: Accuracy and Error11mClassification Diagnostics: Precision and Recall12m

4 readings

Syllabus10mSetting Up Your System10m(Optional) Additional Resources and Recommended Readings10mCourse Materials10m

3 practice exercises

Review: Regression Diagnostics30mReview: Classification Diagnostics30mDiagnostics for Data30m

Week2

Week 2

2 hours to complete

Week 2: Codebases, Regularization, and Evaluating a Model

2 hours to complete

4 videos (Total 35 min)

Week3

Week 3

2 hours to complete

Week 3: Validation and Pipelines

2 hours to complete

4 videos (Total 24 min)

Week4

Week 4

2 hours to complete

Final Project

2 hours to complete

2 readings

Reviews

4.4

9 reviews

5 stars
58.69%
4 stars
23.91%
3 stars
13.04%
2 stars
4.34%

TOP REVIEWS FROM MEANINGFUL PREDICTIVE MODELING

by ISNov 16, 2019

Excellent content, but presentation is a bit challenging at times.

by PTMar 31, 2021

The course provided a lot of insights into predictive modeling.

View all reviews

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

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Meaningful Predictive Modeling

About this Course

What you will learn