Institution: University of California, Berkeley
Start Date: July 24, 2018
Learn how to use machine learning with this free online course entitled “Foundations of Data Science: Prediction and Machine Learning”, offered by the University of California, Berkeley. Machine learning is a way of identifying patterns in data and using them to automatically make predictions or decisions in the future.
In this data science course, you will learn how basic concepts and elements of machine learning. The two main methods of machine learning you will learn are regression and classification. The beginning date of the course is July 24, 2018.
- Duration: 5 weeks
- Commitment: 4 to 6 hours per week
- Subject: Data Science
- Institution: University of California, Berkeley
- Languages: English
- Price: Free
- Session: July 24, 2018
- Requirement: Data 8.1x and Data 8.2x
- Certificate Available: Yes
Who Developed the Course
The University of California, Berkeley was chartered in 1868, and its flagship campus — envisioned as a “City of Learning” — was established at Berkeley, on San Francisco Bay.
No specific knowledge or education background is assumed for the free online course. Anyone can apply for this course.
Where Could This Lead You
After completing this course, you can apply for jobs in the given fields:
Get Extra Benefits
Get a verified certificate to highlight the knowledge and skills you acquire (₹ 10205 INR)
- Official and approved-Get a certificate with the logo of the institution and the signature of a professor to show your achievements and increase your professional prospects
- Easy to share-Add the certificate to your résumé or resume, or publish it directly on LinkedIn
- Proven motivational measure-Give yourself an additional stimulus to complete the course
How to Join This Course
You can register yourself here.
By the end of the course, you’ll be able to:
- Correlation and the phenomenon of regression to the mean
- Linear regression
- Quantifying uncertainty and generating 95% confidence intervals using the bootstrap method
- Classification using the k-nearest neighbor’s algorithm
- How to evaluate the accuracy of a classifier
Who Will You Learn With?
- Ani Adhikari: Teaching Professor of Statistics, UC Berkeley
- John DeNero: Giancarlo Teaching Fellow in the EECS Department, UC Berkeley
- David Wagner: Professor of Computer Science, UC Berkeley
- Importance of Course: The course will highlight the assumptions underlying the techniques, and will provide ways to assess whether those assumptions are good.
- Importance of Certificate: By the Certificate of Achievement you will be able to prove your success when applying for jobs or courses. You can display it on your LinkedIn or CV.
For more information about the course, you may visit the Website.Apply Now