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

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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. 9 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

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. 9 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Instructors

Instructor rating

4.5/5 (7 Ratings)

Julian McAuley

Ilkay Altintas

Chief Data Science Officer

San Diego Supercomputer Center

416,899 Learners

12 Courses

Offered by

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.

Syllabus - What you will learn from this course

Week

Week 1

3 hours to complete

Week 1: Diagnostics for Data

For this first week, we will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of diagnostics for the results of supervised learning.

3 hours to complete

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

6 videos

Introduction to Course 3: Meaningful Predictive Modeling3m

Motivation Behind the MSE8m

Regression Diagnostics: MSE and R²6m

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 Diagnostics30m

Review: Classification Diagnostics30m

Diagnostics for Data30m

Week

Week 2

2 hours to complete

Week 2: Codebases, Regularization, and Evaluating a Model

This week, we will learn how to create a simple bag of words for analysis. We will also cover regularization and why it matters when building a model. Lastly, we will evaluate a model with regularization, focusing on classifiers.

2 hours to complete

4 videos (Total 35 min)

Week

Week 3

2 hours to complete

Week 3: Validation and Pipelines

This week, we will learn about validation and how to implement it in tandem with training and testing. We will also cover how to implement a regularization pipeline in Python and introduce a few guidelines for best practices.

2 hours to complete

4 videos (Total 24 min)

Week

Week 4

2 hours to complete

Final Project

In the final week of this course, you will continue building on the project from the first and second courses of Python Data Products for Predictive Analytics with simple predictive machine learning algorithms. Find a dataset, clean it, and perform basic analyses on the data. Evaluate your model, validate your analyses, and make sure you aren't overfitting the data.

2 hours to complete

2 readings

Reviews

4.4

9 reviews

5 stars
57.77%
4 stars
24.44%
3 stars
13.33%
2 stars
4.44%

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, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

More questions? Visit the Learner Help Center.

Meaningful Predictive Modeling