Machine Learning

January 1, 2018

In September of of 2017 I decided enroll in a machine learning course provided by Stanford Online through Coursera. Today, I finished the course and am happy to have went through with it.

I certainly know a lot more about machine learning now than I did before. I also have an opinion (as of now - but could certainly change in time). My opinion is that it is important for a company to know how machine learning could/would have an impact on your company, but it it likely that you will be renting an ML/AI instance from a company alike that of Amazon, than simply building an end-to-end solution yourself.

You can view my course completion certificate (here). The certificate does not show my grade. I am happy to have report my grade of 96.1% though.

Taught by: Andrew Ng (here), CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Keep updated with my new projects and posts (here).