CaltechX: Learning From Data (Introductory Machine Learning)
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Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just “knowing.”
About this course
This introductory computer science course in machine learning will cover basic theory, algorithms, and applications. Machine learning is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to automatically learn how to perform a desired task based on information extracted from the data. Machine learning has become one of the hottest fields of study today and the demand for jobs is only expected to increase. Gaining skills in this field will get you one step closer to becoming a data scientist or quantitative analyst.
This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion:
What is learning?
Can a machine learn?
How to do it?
How to do it well?
Take-home lessons.
At a Glance:
Institution: CaltechX
Subject: Computer Science
Level: Introductory
Prerequisites:
Basic probability, matrices, and calculus. Familiarity with some programming language or platform will help with the homework.
Language: English
Video Transcripts: English, Português
Associated skills:Data Science, Computer Science, Algorithms, Machine Learning, Big Data
What You’ll Learn:
About this course
This introductory computer science course in machine learning will cover basic theory, algorithms, and applications. Machine learning is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to automatically learn how to perform a desired task based on information extracted from the data. Machine learning has become one of the hottest fields of study today and the demand for jobs is only expected to increase. Gaining skills in this field will get you one step closer to becoming a data scientist or quantitative analyst.
This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion:
What is learning?
Can a machine learn?
How to do it?
How to do it well?
Take-home lessons.
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