Start Here With Machine Learning

Do You Need Help Getting Started with
Applied Machine Learning?

This is The Step-by-Step Guide that You’ve Been Looking For!

Jason BrownleeHi, Jason here. I’m the guy behind Machine Learning Mastery.

My goal is to help you get started, make progress and kick butt with machine learning.

I teach a top-down and results-first approach designed for developers and engineers.
This is unlike most academic textbooks and university courses.

Access my best free tutorials on the blog or take the next step with my paid training material.

You may be feeling overwhelmed. You may have a lot of questions.
I created this page for you. It is your starting point.

Take your time. Bookmark this page. Find the answers to your questions.

Table of Contents

What do you need help with? Here are some quick links:

How Do I Get Started?

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.

Applied Machine Learning Process

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:

Linear Algebra

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.

Machine Learning Algorithms

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.

Study Machine Learning Algorithms

Machine learning algorithms make up a big part of applied machine learning.

There is a lot of benefit in studying machine learning algorithms and learning how to get the most out of them.

Below is a simple 5-step process that you can use to study and learn any machine learning algorithm.

For a detailed overview of this approach see the post:

Weka Machine Learning

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 Machine Learning

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.

Below are the steps that you can use to get started with Python machine learning:

You can see all Python machine learning posts here. Below is a selection of some of the most popular tutorials.

R Machine Learning

R is a platform for statistical computing and is the most popular platform among professional data scientists.

It’s popular because of the large number of techniques available, and because of excellent interfaces to these methods such as the powerful caret package.

Here’s how to get started with R machine learning:

You can see all R machine learning posts here. Below is a selection of some of the most popular tutorials.

Deep Learning

Deep learning is a fascinating and powerful field.

State-of-the-art results are coming from the field of deep learning and it is a sub-field of machine learning that cannot be ignored.

Here’s how to get started with deep learning:

You can see all deep learning posts here. Below is a selection of some of the most popular tutorials.

Long Short-Term Memory (LSTM)

Long Short-Term Memory (LSTM) Recurrent Neural Networks are designed for sequence prediction problems and are a state-of-the-art deep learning technique for challenging prediction problems.

Here’s how to get started with LSTMs in Python:

You can see all LSTM posts here. Below is a selection of some of the most popular tutorials using LSTMs in Python with the Keras deep learning library.

Natural Language Processing

Working with text data is hard because of the messy nature of natural language.

Text is not “solved” but to get state-of-the-art results on challenging NLP problems, you need to adopt deep learning methods

Here’s how to get started with deep learning for natural language processing:

You can see all deep learning for NLP posts here. Below is a selection of some of the most popular tutorials.

XGBoost

XGBoost is a highly optimized implementation of gradient boosted decision trees.

It is popular because it is being used by some of the best data scientists in the world to win machine learning competitions.

Here’s how to get started with XGBoost:

You can see all XGBoosts posts here. Below is a selection of some of the most popular tutorials.

Time Series Forecasting

Time series forecasting is an important topic in business applications.

Many datasets contain a time component, but the topic of time series is rarely covered in much depth from a machine learning perspective.

Here’s how to get started with Time Series Forecasting:

You can see all Time Series Forecasting posts here. Below is a selection of some of the most popular tutorials.

Need More Help?

I’m here to help you become awesome at applied machine learning.

If you still have questions and need help, you have some options: