The Data Science community group in Computas work on monthly newsletters that we will publish continously on this blog. The first newsletter out is the one for September, where we have divided the content into three different sections: «Getting started», «Beginner Tutorials», and «Advanced». We hope you enjoy it!

Getting started

This section includes links to articles where one gets an overview of machine learning. No code, no math, just plain english.

What’s the best way to get started in Statistics for Programmers/Data Science?

I am often asked this question: What’s the best way for getting started in Statistics for Programmers?

I have used the following approach in my teaching.

A visual introduction to machine learning


In machine learning, computers apply statistical learning techniques to automatically identify patterns in data.

These techniques can be used to make highly accurate predictions.



Beginner Tutorials

This section includes links to tutorials you can follow up. Some have code you can follow along.

Machine Learning Crash Course: Part 1

This is a series of tutorials that introduce both beginning and advanced readers to the fundamentals of machine learning and provide insight into how machine learning algorithms actually learn.

  • No math
  • Nice  charts
  • Well written
  • Nice explanations
  • Birds overview

Machine Learning is Fun!

This guide is for anyone who is curious about machine learning but has no idea where to start. I imagine there are a lot of people who tried reading the wikipedia article, got frustrated and gave up wishing someone would just give them a high-level explanation. That’s what this is.

The goal is be accessible to anyone — which means that there’s a lot of generalizations. But who cares? If this gets anyone more interested in ML, then mission accomplished.

  • A bit of math
  • Well written
  • Nice comparisons
  • Sponsored by Lydia?




This section includes links to resources where you have to make a bigger effort. But it pays off.

Neural Networks and Deep Learning

Neural Networks and Deep Learning is a free online book. The book will teach you about:

  • Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
  • Deep learning, a powerful set of techniques for learning in neural networks

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing.

This book will teach you many of the core concepts behind neural networks and deep learning.

  • Take your time
  • Good explanations

Contextual Chatbots with Tensorflow

We’re going to create a chat-bot framework and build a conversational model for an island moped rental shop. The chatbot for this small business needs to handle simple questions about hours of operation, reservation options and so on. We also want it to handle contextual responses such as inquiries about same-day rentals. Getting this right could save a vacation!

We’ll be working through 3 steps:

  • We’ll transform conversational intent definitions to a Tensorflow model
  • Next, we will build a chat-bot framework to process responses
  • Lastly, we’ll show how basic context can be incorporated into our response processor

We’ll be using tflearn, a layer above tensorflow, and of course Python. As always we’ll use iPython notebook as a tool to facilitate our work.

This link is cited from this one: where the author follows the code and explain his experience. Seems like a cool weekend project? If you don’t want to try alone, we are thinking on having a hackatlon.

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