CHOOSE YOUR COURSE
GET THE WORLD CLASS & VERIFIED COURSE
What You Will Learn
- Define common ML terms
- Describe examples of products that use ML and general methods of ML problem-solving used in each
- Identify whether to solve a problem with ML
- Compare and contrast ML to other programming methods
- Apply hypothesis testing and the scientific method to ML problems
- Have conversations about ML problem-solving methods
ln basic terms, ML is the process of training a piece of software, called a model, to make useful predictions using a data set. This predictive model can then serve up predictions about previously unseen data. We use these predictions to take action in a product; for example, the system predicts that a user will like a certain video, so the system recommends that video to the user.
Often, people talk about ML as having two paradigms, supervised and unsupervised learning. However, it is more accurate to describe ML problems as falling along a spectrum of supervision between supervised and unsupervised learning. For the sake of simplicity, this course will focus on the two extremes of this spectrum.
This course is an introduction to computer science and programming in Python. Upon successful completion of this course, you will be able to:
1. Take a new computational problem and develop a plan to solve it through problem understanding and decomposition.
2. Follow a design creation process that includes specifications, algorithms, and testing.
3. Code, test, and debug a program in Python, based on your design.
What You Will Learn
Data Science is a dynamic and fast growing field at the interface of Statistics and Computer Science. The emergence of massive datasets containing millions or even billions of observations provides the primary impetus for the field. Such datasets arise, for instance, in large-scale retailing, telecommunications, astronomy, and internet social media. This course will emphasize practical techniques for working with large-scale data. Specific topics covered will include statistical modeling and machine learning, data pipelines, programming languages, "big data" tools, and real world topics and case studies. The use of statistical and data manipulation software will be required. Course intended for non-quantitative graduate-level disciplines. This course will not count towards degree requirements for graduate programs such as Statistics, Computer Science, or Data Science. Students should inquire with their respective programs to determine eligibility of course to count towards minimum degree requirements. This course does not fulfill any major requirements for undergraduate degree programs offered by Computer Science.