Essential Math for Data Science (v.1)
The course objective is to explore important concepts of mathematics that form the foundation for Machine Learning algorithms, Data Science, and Artificial Intelligence, including probability, statistics, calculus, and linear algebra.
Additional DetailsSkills
- Decision Trees
- Dimensionality Reduction
- Distance-based Models
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Math and Decision Trees
- Mathematics For Machine Learning
- Neural Network Mathematics
- Probability And Statistics
- Problem Solving With Python
- Support Vector Machine (SVM) Math
Earning Criteria
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The methods of assessment include examinations, quizzes, with a minimum passing score of 70%.
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Instructional strategies include: laboratory, computer-based training, practical exercises.
Endorsements
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American Council on Education
This credential has been successfully evaluated by the American Council on Education for college credit. It is recommended for a total of 4 college credits. For more information about ACE Learning Evaluations, visit www.acenet.edu. -
American Council on Education
3 semester hours in Mathematics for Machine Learning in the upper-division undergraduate category -
American Council on Education
1 semester hour in Problem Solving with Python in the upper-division undergraduate category