Doesn’t Time Fly When You’re Enjoying Yourself? *)

How many things have happened since my last post! To name a few:

• I’ve learned (mostly by accident) about a new programming language of exotic name and many strengths: Ceylon. More about it in later posts.
• I’ve started two new courses: Functional Programming Principles in Scala and Computing for Data Analysis. I’ll dedicate two future posts to these courses.
• I’ve finished Building Machine Learning Systems with Python. Yay!

And, in fact, this post is the continuation of my review on this book. Therefore, without much ado, I’ll continue with …

Chapter 5: classification – detecting poor answers

This chapter is a rehashing Chapter 2, but in a much more complex context: classifying answers on Q/A websites (such as Stack Overflow) in good/bad , useful/not useful, etc. The techniques which this chapter describes rely, as expected, on measuring first the usefulness of an answer (not an easy problem). Then the techniques consist in applying two algorithms (nearest neighbour and logistic regression) to train the classifier and improve its performance. More importantly, the chapter introduces the reader into debugging ML systems – activity which requires fine-tuning and ad-hoc tweaking.

Not a bad chapter, but the problem that I see is that it does not achieve the initial goal: the classifier only partially satisfies all the criteria.

Return of the Machine (Learning)

After more than a year since my last blog post, I decided to resume blogging.

Why? Well, because many things have happened: I have opened up new venues of excitement (online courses!), I delved more into a very interesting and dynamic field of applied Computer Science (Derivatives Finance!) and I embarked on a serious personal programme of reading as many and as varied technical books as possible (more about it below).

But the true reason for resuming blogging is that blogging is so much fun and I was really missing that fun.

So, not surprisingly, my future blog posts will cover three important aspects:

• Courses I’ve found and/or pursued and interesting things I’ve learned from them
• Topics on numeric processing I’ve encountered
• Books, articles or personal projects that captured my interest (how about “Is there any use for a XAML processor for Java?”)

For starters, I will write (in future posts) about two courses which I’ve just completed:

1. MongoDB for Developers (M101P) generously offered by 10gen (now MongoDB.com)
2. Model Thinking coming from University of Michigan through Coursera.

But before that, I want to dedicate this rejuvenating post to a partial review of a book which I started reading recently: Building Machine Learning Systems with Python by Willi Richert and Luis Pedro Coelho from by Packt Publishing.

Disclaimer:

I am in no way associated with Packt Publishing and I do not collect royalties on this work. Packt Publishing has kindly provided me with a reviewer’s copy in response to my interest in it. These reviews are, in part, my way to return the favour. My objectivity is guaranteed by the fact that, free copy or not, Machine Learning is a fascinating subject to me.