Self-study machine learning

Hi, there are lots of things you can do to learn about machine learning.

There are resources like books and courses you can follow, competitions you can enter and tools you can use. It’s overwhelming.

I want to put some structure around these self-study activities for you and suggest a loose ordering of what to tackle and when in your journey from developer to machine learning master. Believe. You can practice and apply machine learning. Pick a Process. Use a systemic process to work through predictive modeling problems. Pick a Tool. Select an appropriate tool for your level and map it onto your process. Pick a Dataset. Select datasets to work on and practice the process. Build a Portfolio. Write up your results and learnings and use them to demonstrate your growing skills. You can learn more about this approach to self-studying machine learning in the post:

How do I get started in machine learning?

We will cover each of these steps in upcoming emails and go into others in a lot more detail later.

I’ll speak to you soon.


How Do I Get Started In Machine Learning? (the short version)

by Jason Brownlee on September 10, 2015 in Start Machine Learning

I get daily emails asking the question:

How do I get started in machine learning?

This post provides my quick answer. Here is my long answer.

So here is how to get started in machine learning, the quick version.

Practice Creating Predictive Models

You’re interested in machine learning but you’re not sure of the specific outcome you’re looking for.

Maybe you’re interested in learning more about machine learning algorithms. Maybe you’re interested in creating predictions. Maybe you’re interested in solving complex problems. Maybe you’re interested in creating smarter software. Maybe you’re even interested in becoming a data scientist. I have a suggestion…

Given a dataset, learn how to create accurate models, reliably.

You will learn about the types and behaviours of machine learning algorithms. You can use the resulting predictions directly. You can build the skills to be able to solve your complex problems. You can use the models in your software. You can use the models in competitions, like those on Kaggle. You can use the results to demonstrate your skills at applied machine learning. Here’s What To Do, Step-by-Step

You are going to be told to learn the math, read the textbooks and study theory.

Maybe that path is good for academics. I call this approach the bottom-up approach to getting started in machine learning.

This is not the only path. There are other ways.

The Top-Down Approach To Getting Started in Machine Learning

Here are the steps to get started:

Believe. Know that you can learn machine learning by practicing working through problems (top-down) rather than studying theory (bottom-up). Pick a Process. Select a systematic process for working through a machine learning problem from beginning to end that you can use to reliably get a good result on any problem you work on. Pick a Tool. Select a tool or platform that you can use to actually work through problems and map it onto your chosen systematic process. Pick a Dataset. Select datasets to work on and practice the process. Ideally select properties of problems that you want to practice and find well understood datasets that have those traits on which to practice. Build a Portfolio. Write up your results and learnings in semi-formal work products (blog posts, presentations, tech reports) and share them publicly to demonstrate your growing machine learning skills and capabilities and engage like minded practitioners.

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