The Applied Machine Learning course teaches you a wide-ranging set of techniques of supervised and unsupervised machine learning approaches using Python as the programming language.
Since this course requires an intermediate knowledge of Python, you will spend the first part of this course learning Python for Data Analytics taught by Emeritus. This will provide you with the programming knowledge required to do the assignments and application projects that are part of the Applied Machine Learning course.
If you are looking to implement or lead a machine learning project or looking to incorporate machine learning capability in your software application, this course is appropriate for you. This is a programming course: you will be required to write code, but no prior programming knowledge is required.
The course requires an undergraduate knowledge of statistics (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation etc.), calculus (derivatives), linear algebra (vectors & matrix transformation) and probability (conditional probability/Bayes theorem).
*Assessment: Students will be given an assessment to test their math skills prior to commencement of the course. You can view sample questions by clicking here. To familiarize yourself with the topics of the assessment, refer to learning resources by clicking here.