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Discover the Power of Machine Learning

Your future in machine learning starts here.

Machine learnings is a subfield of artificial intelligence with the capability of a machine to ultimately imitate intelligent human behavior. It is the use and development of computer systems that are able to learn and adapt without following explicit instructions. Machine learning does so by using algorithms and statistical models to analyze and draw inferences from patterns in data.

Courses are A-G certified in Science (D) / Computer Science.

Learning Format:


Online Asynchronous

Enjoy the flexibility of learning at your own pace. Courses are 100 percent online, with no in-person meetings.

9 Months | Fall, Winter, and Spring Quarters
3 Months | Summer Quarter

What you will learn:

  • Google Colaboratory Integrated Development Environment.
  • Applications of Python Data types, Conditional operators, Loops, Functions, Data structures, Recursion, OOP, and Machine Learning libraries.
  • Probability and Statistics for Machine Learning in addition to Linear Algebra for Machine Learning.
  • Calculus for Machine Learning with Python-based projects focused on applied mathematics for Machine Learning.
  • Create Python programming language scripts in the Google Collaboratory development environment to pre-process a dataset using standard Machine Learning libraries.
  • Implement and analyze regression models including simple and multiple regression, polynomial, lasso, and logistic regression.
  • Implement and analyze “supervised” classification algorithms including Naive Bayes and K nearest neighbors (KNN).
  • Implement and analyze “unsupervised” clustering algorithms including K-means, and density-based spatial (DBSCAN) clustering.
  • Implement and analyze dimensionality reduction techniques including linear discriminant analysis (LDA) and Principal Component Analysis (PCA).
  • Write and test working Python programs from a generic problem statement through algorithm development, design and implementation, unit test, integration, and deployment.
  • Implement and analyze single and multi-layer Perceptron models.
  • Implement and analyze optimization techniques and apply them to DNN frameworks.
  • Implement and apply Convolutional Neural Networks to image processing applications.

Average Entry Level Salary

$90,000 - $143,000

Per Year

How to Enroll:

Pay as You Go $350 Per Course:

For general enrollments, expand the "courses" tab at the bottom of this page to review the course list and then click a course to see details and enroll. You may pay per course as you work through the program. Courses must be taken in sequence.


This program is currently scholarship eligible for both Online and Live Online options. Our Scholarships are first-come, first-served and will cover the full cost of any program you choose. To get up-to-date scholarship information on requirements and to apply see:

Futures Scholarships

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