Math for Machine Learning

Math for Machine Learning

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Course Description

Would you like to learn a mathematics subject that is crucial for many high-demand lucrative career fields such as: Computer Science Data Science Artificial Intelligence If you're looking to gain a solid foundation in Machine Learning to further your career goals, in a way that allows you to study on your own schedule at a fraction of the cost it would take at a traditional university, this online course is for you. If you're a working professional needing a refresher on machine learning or a complete beginner who needs to learn Machine Learning for the first time, this online course is for you. Why you should take this online course: You need to refresh your knowledge of machine learning for your career to earn a higher salary. You need to learn machine learning because it is a required mathematical subject for your chosen career field such as data science or artificial intelligence. You intend to pursue a masters degree or PhD, and machine learning is a required or recommend

ed subject. Why you should choose this instructor: I earned my PhD in Mathematics from the University of California, Riverside. I have created many successful online math courses that students around the world have found invaluable—courses in linear algebra, discrete math, and calculus. In this course, I cover the core concepts such as: Linear Regression Linear Discriminant Analysis Logistic Regression Artificial Neural Networks Support Vector Machines After taking this course, you will feel CARE-FREE AND CONFIDENT. I will break it all down into bite-sized no-brainer chunks. I explain each definition and go through each example STEP BY STEP so that you understand each topic clearly. I will also be AVAILABLE TO ANSWER ANY QUESTIONS you might have on the lecture material or any other questions you are struggling with.

What you will learn

Refresh your machine learning knowledge. Apply fundamental techniques of machine learning. Gain a firm foundation in machine learning for furthering your career. Learn a subject crucial for data science and artificial intelligence.

Curriculum

Section 1: Linear Regression

Section 2: Linear Discriminant Analysis

Section 3: Logistic Regression

Section 4: Artificial Neural Networks

Section 5: Maximal Margin Classifier

Section 6: Support Vector Classifier

Section 7: Support Vector Machine Classifier

Section 8: Penetration Testing by Kali Linux