I recently signed up for the Coursera – Machine Learning Class with Andrew Ng. It is an 11 week class and below is a description that they provided about the course and what you will hopefully get out it. The course is free or you can pay $79 and get a certification. I decided to pay the $79. I have lost more money than that on things so I was willing to risk it for the opportunity to get a certificate for a topic I am incredibly interested in.
About this course: 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.
I am taking this class with three of my friends (Brett Roberts, Kyle Prins, and Thomas Henson). We are all a part of the Big Data Beard group and each of us had different goals for the class. We have started doing some videos about our thoughts on the class and our experience with it. I won’t go into details because we have shared them in the videos but I did want to take time to add some separate thoughts on it.
- I can’t stress enough how important it is to try to take the class with other people you know. Having support for the class for me was necessary because at times it was tough, not just as a course but because of holidays or issues that came up in my personal and professional life. It is also nice to bounce some questions off of them. I found the “forums” overwhelming just too difficult to navigate.
- I graduated from college a long time ago and I still took the class. It isn’t too late to get involved and keep learning. Again, this is why it helps to have others take the class with you. It can keep you motivated.
- This class was recorded in 2011 so it can get somewhat frustrating at times because you still need to read about errors/changes in the course. Then again, it is free so you can’t expect too much.
- It can be tough to do online classes if you are not used to it. I had to do supplemental training because I felt all the details were not there at the level that I needed them. I hope to be sharing these details as I continue with the course.
- Don’t beat yourself up over the class. Just try to get everything you can out of it and focus on specific goals that you set up for yourself. What do you want to get out of the class? What are you trying to achieve? Why are you taking the class? Personally, I am taking the class to be knowledgeable on the topic and have a better understanding of what needs to be done in order for companies to implement Machine Learning.
If you decide to take the class, good luck. I know you will do great!!