Learn #MachineLearning Coding Basics in a weekend – a new approach to coding for #AI Source – datasciencecentral.com
image source – wikipedia
The idea is: spend one weekend to pick up the basics of coding for machine learning
- Curious and already exploring machine learning online
- Already can code in any language
We are going to use deliberate practise to learn coding
I believe that this concept originated in the old soviet union athletes
It is also associated with a diverse range of people including Golf (Ben Hogan), Shaolin Monks, Benjamin Franklin etc. For the purposes of learning coding for machine learning, we apply the following elements of deliberate practice
- Break down key ideas in simple, small steps. In this case, using a mindmap and a glossary
- Work with micro steps
- Keep the big picture in mind
- Encourage reflection/feedback
- Practice each element
- Reflect and adapt in microdata points
- Go slow
Image source: wikipedia
Artefacts / tools
- Mindmap – Glossary
- Site of book posted for comments
This means we don’t need any installation (it’s completely web-based)
We will guide you through two end-to-end machine learning problems that can be taken over one weekend.
We will introduce you to important machine learning concepts, such as machine learning workflow, defining the problem statement, pre-processing and understanding our data, building baseline and more sophisticated models, and evaluating models.
We will also introduce to keep machine learning libraries in python and demonstrate code that can be used on your own problems. We will cover data exploration in pandas, look at how to evaluate performance in numpy, plot our findings in Matplotlib, and build our models in sci-kit learn.
Day 1 will focus on a regression problem and introduce you to the machine learning workflow and key libraries. We will build and evaluate our first models in sci-kit learn, and replicate our evaluation in numpy as a way of introducing you to the library.
Day 2 will focus on a classification problem, and look to reinforce machine learning workflows, focus on pandas for data exploration and analysis, and build more models in sci-kit learn. We will end by building a machine learning pipeline.
By the end of the weekend, you will have been introduced to core concepts and have some solid code examples that you can translate to new problems. We’ll also provide you with links to enable you to develop your knowledge further.
- comments in the comments section of the book
- we will aim to respond to in a week or the community can respond where possible.
- We are specifically interested in Missing concepts and Coding issues
- In keeping with the ideas of deliberate practise, we will narrow the focus and we will confine to the ideas in the notebooks
first week of Feb 2019
Comment below and we will post link to book and code when live