Introduction to Machine Learning
COURSE DESCRIPTION
Machine learning is the application of artificial intelligence (AI) that allows machines to automatically learn and improve without being explicitly programmed for the task. The main focus of Machine Learning is to provide algorithms to build and train such systems and use them to solve a determined problem. This course will discuss different learning methods used in machine learning and some of the most common algorithms available for your projects.
LEARNING OUTCOMES
- Understand the basics of Machine Learning concepts
- Understand the basic concepts of Linear Algebra, descriptive statistics and probability
- Comprehend and practice basic Python programming, data structures in Python; work with Pandas and Numpy, Classes and Inheritance
- Comprehend and practice with tool for Machine Learning
- Know the basics of Supervised and Unsupervised Learning in Machine Learning with case studies
Course Content
Time: 55 hours
Module 1 – Machine Learning Overview 0/0
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Module 2 – Python for Machine Learning 0/0
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Assignment 1: Write a program to grade the exams for multiple classes with the class size of thousands of students 0/0
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Module 3 – Mathematics for Machine Learning 0/0
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Progress Test 0/0
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Module 3 – Machine Learning Foundations: A Case Study 0/0
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Assignment 2: Project – (1) Sentiment analysis - Determine if a movie review is positive or negative and (2) Image classification - Determine if an image is a cat or a dog 0/0
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Instructor
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