This is The Step-by-Step Guide to Machine Learning
You’ve Been Looking For!
Get Started and Get Good at Applied Machine Learning
Hi, 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.
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?
- Applied Machine Learning Process
- Machine Learning Algorithms
- Study Machine Learning Algorithms
- Weka Machine Learning
- Python Machine Learning
- R Machine Learning
- Deep Learning
- Time Series Forecasting
- More Help
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:
- Step 1: Adjust Mindset. Believe you can practice and apply machine learning.
- Step 2: Pick a Process. Use a systemic process to work through problems.
- Step 3: Pick a Tool. Select a tool for your level and map it onto your process.
- Step 4: Practice on Datasets. Select datasets to work on and practice the process.
- Step 5: Build a Portfolio. Gather results and demonstrate your skills.
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:
- Step 1: Define your problem.
- Step 2: Prepare your data.
- Step 3: Spot-check algorithms.
- Step 4: Improve results.
- Step 5: Present results.
For a good summary of this process, see the posts:
- Applied Machine Learning Process
- How to Use a Machine Learning Checklist to Get Accurate Predictions
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:
- Step 1: Discover the different types of machine learning algorithms.
- Step 2: Discover the foundations of machine learning algorithms.
- Step 3: Discover how top machine learning algorithms work.
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.
- Step 1: Create lists of machine learning algorithms
- Step 2: Research machine learning algorithms
- Step 3: Create your own algorithm descriptions
- Step 4: Investigate algorithm behavior
- Step 5: Implement machine learning algorithms
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:
- Step 1: Discover the features of the Weka platform.
- Step 2: Discover how to get around the Weka platform.
- Step 3: Discover how to deliver results with Weka.
You can see all Weka machine learning posts here. Below is a selection of some of the most popular tutorials.
Prepare Data in Weka
- How To Load CSV Machine Learning Data in Weka
- How to Better Understand Your Machine Learning Data in Weka
- How to Normalize and Standardize Your Machine Learning Data in Weka
- How To Handle Missing Values In Machine Learning Data With Weka
- How to Perform Feature Selection With Machine Learning Data in Weka
Weka Algorithm 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:
- Step 1: Discover Python for machine learning
- Step 2: Discover the ecosystem for Python machine learning.
- Step 3: Discover how to work through problems using machine learning in Python.
You can see all Python machine learning posts here. Below is a selection of some of the most popular tutorials.
Prepare Data in Python
Machine Learning in Python
- Evaluate the Performance of Machine Learning Algorithms
- Metrics To Evaluate Machine Learning Algorithms in Python
- Spot-Check Classification Machine Learning Algorithms in Python with scikit-learn
- Spot-Check Regression Machine Learning Algorithms in Python with scikit-learn
- How To Compare Machine Learning Algorithms in Python with scikit-learn
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:
- Step 1: Discover the R platform and why it is so popular.
- Step 2: Discover machine learning algorithms in R.
- Step 3: Discover how to work through problems using machine learning in R.
You can see all R machine learning posts here. Below is a selection of some of the most popular tutorials.
Data Preparation in R
Applied Machine Learning in R
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:
- Step 1: Discover what deep learning is all about.
- Step 2: Discover the best tools and libraries.
- Step 3: Discover how to work through problems and deliver results.
You can see all deep learning posts here. Below is a selection of some of the most popular tutorials.
- Crash Course On Multi-Layer Perceptron Neural Networks
- Crash Course in Convolutional Neural Networks for Machine Learning
- Crash Course in Recurrent Neural Networks for Deep Learning
Convolutional Neural Networks
- Handwritten Digit Recognition using Convolutional Neural Networks in Python with Keras
- Object Recognition with Convolutional Neural Networks in the Keras Deep Learning Library
- Predict Sentiment From Movie Reviews Using Deep Learning
Recurrent Neural Networks
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:
- Step 1: Discover the Gradient Boosting Algorithm.
- Step 2: Discover XGBoost.
- Step 3: Discover how to get good at delivering results with XGBoost.
You can see all XGBoosts posts here. Below is a selection of some of the most popular tutorials.
- How to Configure the Gradient Boosting Algorithm
- Tune Learning Rate for Gradient Boosting with XGBoost in Python
- Stochastic Gradient Boosting with XGBoost and scikit-learn in Python
- How to Tune the Number and Size of Decision Trees with XGBoost in Python
- How to Best Tune Multithreading Support for XGBoost in Python
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:
- Step 1: Discover Time Series Forecasting.
- Step 2: Discover Time Series as Supervised Learning.
- Step 3: Discover how to get good at delivering results with Time Series Forecasting.
You can see all Time Series Forecasting posts here. Below is a selection of some of the most popular tutorials.
Data Preparation Tutorials
- How to Make Baseline Predictions for Time Series Forecasting with Python
- How to Check if Time Series Data is Stationary with Python
- How to Create an ARIMA Model for Time Series Forecasting with Python
- How to Grid Search ARIMA Model Hyperparameters with Python
- How to Work Through a Time Series Forecast Project
Need More Help?
I’m here to help you become awesome with machine learning.
If you still have questions and need help, you have some options:
- Ebooks: I sell a catalog of Ebooks that show you how to get results with machine learning, fast.
- Blog: I write a lot about applied machine learning on the blog, try the search feature.
- Contact: You can contact me with your question, but one question at a time please.