PH125.8x: Data Science: Machine Learning - Course Syllabus
Course Instructor
Rafael Irizarry
Course Description
In this 8th course of nine in the HarvardX Data Science Professional Certificate, we learn how to use R to build a movie recommendation system using the basics of machine learning, the science behind the most popular and successful data science techniques.
Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.
In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
You will learn about training data, a set of data used to discover potentially predictive relationships and how the data can come in the form of the outcome we want to predict and features that we will use to predict this outcome. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.
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Course Structure
This is a self-paced course; you can progress through the material at your own pace.
Grading
Unlike the first four courses in the series, this course contains comprehension checks which are worth 100% of your grade. (This is the same grading structure as in the fifth, sixth, and seventh courses in the series.)
Certification
In order to receive a Verified Certificate, you must sign up and pay for a Verified Certificate by the deadline on the course page and earn a passing grade of at least 70%.
COURSE OUTLINE
Section 1: Introduction to Machine Learning (available now)
In this section, you'll be introduced to some of the terminology and concepts you'll need going forward.
Section 2: Machine Learning Basics (available now)
In this section, you'll learn how to start building a machine learning algorithm using training and test data sets and the importance of conditional probabilities for machine learning.
Section 3: Linear Regression for Prediction, Smoothing, and Working with Matrices (available now)
In this section, you'll learn why linear regression is a useful baseline approach but is often insufficiently flexible for more complex analyses, how to smooth noisy data, and how to use matrices for machine learning.
Section 4: Distance, Knn, Cross Validation, and Generative Models (available now)
In this section, you'll learn different types of discriminative and generative approaches for machine learning algorithms.
Section 5: Classification with More than Two Classes and the Caret Package (available now)
In this section, you'll learn how to overcome the curse of dimensionality using methods that adapt to higher dimensions and how to use the caret package to implement many different machine learning algorithms.
Section 6: Model Fitting and Recommendation Systems (available now)
In this section, you'll learn how to apply the machine learning algorithms you have learned.
GENERAL FAQs
What is the deadline to sign up for a Verified Certificate?
The deadline is listed on the right side of the course landing page.
How do I earn a certificate?
To earn a certificate, you must sign up for a Verified Certificate by the deadline and earn a grade of at least 70%. When you achieve this score, a view your certificate button will appear on your dashboard. For more information, click on this link.
How long does the course take?
That is up to you! It is 4 weeks of content. Just be aware of the course close date on the right side of the course landing page!
I am doing well on the assessments, but when I look under "Progress" I have a very low grade...why?
The grade is calculated based on all of the assessments you have completed and the assessments that you have not completed (edX says you have a "zero" on those assessments until you have attempted them). You will see your overall grade move up as you progress through the course.
ABOUT THE PROFESSIONAL CERTIFICATE
Thank you for taking PH125.8x Data Science: Machine Learning! We hope you now feel equipped to tackle the rest of the HarvardX Data Science Professional Certificate (you can enroll in the entire certificate series or take more of the courses in the series).
PROFESSIONAL CERTIFICATE FAQs
How often will the courses be offered?
Courses in the program are offered frequently, with overlap - so if now isn’t a good time for you to start one of the courses you need as a prerequisite or if you missed a deadline, there will be another offering of the course you need coming soon!
Does the order of courses in the Professional Certificate Program matter?
Yes, order does matter, particularly for the first four courses in the sequence. For the later courses, depending on your previous experience, you may be able to swap the sequence of some of the courses. The courses are designed to be taken in the following order:
Do I need to register for all of the courses at once in order to be eligible for the Professional Certificate?
No! You can take courses individually - once you have obtained an ID Verified Certificate in each course, you will be eligible for the Professional Certificate. If you choose to pre-pay for the entire program, you receive a discount on the total registration cost.