Start Here with Machine Learning

Making developers awesome at machine learning
Making Developers Awesome at Machine Learning
Making developers awesome at machine learning
Making Developers Awesome at Machine Learning
The most common question I’m asked is: “how do I get started?”
My best advice for getting started in machine learning is broken down into a 5-step process:
For more on this top-down approach, see:
Many of my students have used this approach to go on and do well in Kaggle competitions and get jobs as Machine Learning Engineers and Data Scientists.
The benefit of machine learning are the predictions and the models that make predictions.
To have skill at applied machine learning means knowing how to consistently and reliably deliver high-quality predictions on problem after problem. You need to follow a systematic process.
Below is a 5-step process that you can follow to consistently achieve above average results on predictive modeling problems:
For a good summary of this process, see the posts:
Probability is the mathematics of quantifying and harnessing uncertainty. It is the bedrock of many fields of mathematics (like statistics) and is critical for applied machine learning.
Below is the 3 step process that you can use to get up-to-speed with probability for machine learning, fast.
You can see all of the tutorials on probability here. Below is a selection of some of the most popular tutorials.
Statistical Methods an important foundation area of mathematics required for achieving a deeper understanding of the behavior of machine learning algorithms.
Below is the 3 step process that you can use to get up-to-speed with statistical methods for machine learning, fast.
You can see all of the statistical methods posts here. Below is a selection of some of the most popular tutorials.
Linear algebra is an important foundation area of mathematics required for achieving a deeper understanding of machine learning algorithms.
Below is the 3 step process that you can use to get up-to-speed with linear algebra for machine learning, fast.
You can see all linear algebra posts here. Below is a selection of some of the most popular tutorials.
Optimization is the core of all machine learning algorithms. When we train a machine learning model, it is doing optimization with the given dataset.
You can get familiar with optimization for machine learning in 3 steps, fast.
You can see all optimization posts here. Below is a selection of some of the most popular tutorials.
Calculus is the hidden driver for the success of many machine learning algorithms. When we talk about the gradient descent optimization part of a machine learning algorithm, the gradient is found using calculus.
You can get familiar with calculus for machine learning in 3 steps.
You can see all calculus posts here. Below is a selection of some of the most popular tutorials.
Python is the lingua franca of machine learning projects. Not only a lot of machine learning libraries are in Python, but also it is effective to help us finish our machine learning projects quick and neatly. Having good Python programming skills can let you get more done in shorter time!
You can get familiar with Python for machine learning in 3 steps.
You can see all Python posts here. But don’t miss Python for Machine Learning (my book). Below is a selection of some of the most popular tutorials.
Machine learning is about machine learning algorithms.
You need to know what algorithms are available for a given problem, how they work, and how to get the most out of them.
Here’s how to get started with machine learning algorithms:
You can see all machine learning algorithm posts here. Below is a selection of some of the most popular tutorials.
Weka is a platform that you can use to get started in applied machine learning.
It has a graphical user interface meaning that no programming is required and it offers a suite of state of the art algorithms.
Here’s how you can get started with Weka:
You can see all Weka machine learning posts here. Below is a selection of some of the most popular tutorials.
Python is one of the fastest growing platforms for applied machine learning.
You can use the same tools like pandas and scikit-learn in the development and operational deployment of your model.