About this Course

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Intermediate Level
Approx. 7 hours to complete
English
Subtitles: English

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.

Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 7 hours to complete
English
Subtitles: English

Offered by

University of California San Diego logo

University of California San Diego

Syllabus - What you will learn from this course

Week
1

Week 1

2 hours to complete

Week 1: Diagnostics for Data

2 hours to complete
6 videos (Total 49 min), 4 readings, 3 quizzes
6 videos
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 Diagnostics8m
Review: Classification Diagnostics4m
Diagnostics for Data30m
Week
2

Week 2

2 hours to complete

Week 2: Codebases, Regularization, and Evaluating a Model

2 hours to complete
4 videos (Total 35 min)
4 videos
Model Complexity and Regularization10m
Adding a Regularizer to our Model, and Evaluating the Regularized Model8m
Evaluating Classifiers for Ranking4m
4 practice exercises
Review: Setting Up a Codebase2m
Review: Regularization5m
Review: Evaluating a Model5m
Codebases, Regularization, and Evaluating a Model45m
Week
3

Week 3

1 hour to complete

Week 3: Validation and Pipelines

1 hour to complete
4 videos (Total 24 min)
4 videos
“Theorems” About Training, Testing, and Validation8m
Implementing a Regularization Pipeline in Python5m
Guidelines on the Implementation of Predictive Pipelines5m
3 practice exercises
Review: Validation4m
Review: Predictive Pipelines6m
Predictive Pipelines20m
Week
4

Week 4

2 hours to complete

Final Project

2 hours to complete
2 readings
2 readings
Project Description10m
Where to Find Datasets10m

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

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    • 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.
  • 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.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • 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.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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