This article compiles for you the 15 best cheat sheets in the web that help you get started with machine learning. If you’re short on time, here are the 15 direct PDF links (open in a new tab):
- Supervised Learning (Afshine Amidi)
- Unsupervised Learning (Afshine Amidi)
- Deep Learning (Afshine Amidi)
- Machine Learning Tips and Tricks (Afshine Amidi)
- Probabilities and Statistics (Afshine Amidi)
- Linear Algebra and Calculus (Afshine Amidi)
- Comprehensive Stanford Master Cheat Sheet (Afshine Amidi)
- Data Science Cheat Sheet (Datacamp)
- Keras Cheat Sheet (Datacamp)
- Deep Learning with Keras Cheat Sheet (RStudio)
- Visual Guide to Neural Network Infrastructures (Asimov Institute)
- Skicit-Learn Python Cheat Sheet (Datacamp)
- Scikit-learn Cheat Sheet: Choosing the Right Estimator (Scikit-learn.org)
- Tensorflow Cheat Sheet (Altoros)
- Machine Learning Test Cheat Sheet (Cheatography)
Each cheat sheet link points directly to the PDF file. So don’t lose any more time, and start learning faster with these 15 ML cheat sheets.
In the following video, I quickly describe you all 15 cheat sheets and their pros and cons:
(Article reading time: 12 minutes ||| Or watch the video)
Cheat sheets are the 80/20 principle applied to coding: learn 80% of the relevant material in 20% of the time.
If you love learning with cheat sheets, join my free cheat sheet academy:
This article compiles the list of all the best cheat sheets for machine learning. Are you a practitioner and want to move towards machine learning and data science? Are you a young data scientist just starting out with your career? Or are you a computer science student struggling to find a clear path of how to master the intimidating area of machine learning? Then check out these cheat sheets to make your life easier.
ALL LINKS OPEN IN A NEW TAB! 😉
Supervised Learning (Afshine Amidi)
This cheat sheet is the first part of a series of cheat sheets created for the Stanford Machine Learning Class. It gives you a short and concise introduction to supervised learning.
Topics include the following:
- Supervised learning notations,
- Linear regression,
- Logistic regression,
- Generalized linear models,
- Support vector machines,
- Generative learning,
- Gaussian discriminant analysis,
- Naive Bayes,
- Tree-based and ensemble methods, and
- General learning theory.
Unsupervised Learning (Afshine Amidi)
This cheat sheet is the second part of the introductory series for the Stanford Machine Learning Class. It provides a concise introduction to unsupervised learning.
You will learn about these topics:
- Expectation-maximization (EM),
- K-means clustering,
- Hierarchical clustering,
- Clustering assessment metrics,
- Principal component analysis, and
- Independent component analysis.
Deep Learning (Afshine Amidi)
This is the third part of the cheat sheet series provided by the Stanford Machine Learning Class. The cheat sheet is packed with dense information about deep learning. This cheat sheet offers a promising kickstart into the hot topic of deep learning.
The cheat sheet addresses topics such as
- Introduction to neural networks,
- Convolutional neural networks,
- Recurrent neural networks,
- Reinforcement learning, and
Of course, this covers only a subspace of the broad field of deep learning, but it will give you a short and effective start into this attractive area.
Machine Learning Tips and Tricks (Afshine Amidi)
The fourth part of the cheat sheet series provided as part of the Stanford Machine Learning Class promises small tips and tricks in machine learning. Although the author calls it that way (“Tips and Tricks”), I believe this is merely an understatement. In reality, this cheat sheet gives you valuable insights from a highly-skilled practitioner in the field.
The topics are not only limited to
- Model selection, and
A must-read for upcoming data scientists.
Probabilities and Statistics (Afshine Amidi)
The fifth part of the cheat sheet series of the Stanford Machine Learning Class gives you a quick start (they call it a “refresher”) in the crucial area of probability theory and statistics. No matter in which field you will end up working, statistics will always help you on your path to becoming a machine learning professional. This refresher is definitely worth a read (and an investment of your printer ink).
Here are the topics addressed in this cheat sheet:
- Introduction to probability and combinatorics,
- Conditional probability,
- Random variables,
- Joint distributions, and
- Parameter estimation.
Get this cheat sheet now!
Linear Algebra and Calculus (Afshine Amidi)
Although the sixth part of the popular cheat sheet series of the Stanford Machine Learning Class does not sound too sexy, it teaches a fundamental area each machine learning professional knows well: linear algebra.
Do you struggle understanding this critical topic? Your lack of understanding will cost you weeks as soon as you start implementing practical machine learning algorithms. Simply put: you have to master linear algebra, there is no way around. So do it now and do it well.
What are the precise topics included in this cheat sheet?
- Standard matrix notation,
- Matrix operations,
- Matrix properties, and
- Matrix calculus (gradient operations).
You see, it’s all about matrices. Before you even consider diving in practical libraries used in machine learning (such as Python’s numpy, check out my HUGE numpy tutorial), study this cheat sheet first.
Comprehensive Stanford Master Cheat Sheet (Afshine Amidi)
This cheat sheet comprises six cheat sheets of the Stanford Machine Learning Class. It is an awesome resource, packed with information in many important subfields in Machine Learning. I highly recommend downloading this resource and studying it a whole day. It will boost your machine learning skills in little time.
The widely distributed topics of this 16-page cheat sheet include
- Supervised Learning,
- Unsupervised learning,
- Deep learning,
- Machine learning tips and tricks,
- Probabilities and statistics, and
- Linear algebra and calculus.
Don’t lose any more time reading the rest of this article and download this cheat sheet. Thanks, Afshine, for this awesome resource!
Data Science Cheat Sheet (Datacamp)
The datacamp cheat sheets are always worth a look. However, I would recommend this cheat sheet only for absolute beginners in the field of data science. If you focus on learning core machine learning concepts and you already have some experience, please skip this cheat sheet. But if you are just starting out with data science and machine learning – and you want to use Python as your programming language – this 1-page data science cheat sheet is for you.
The basic topics of this cheat sheet are
- Installing Python,
- Python variables and data types,
- Strings and string operations,
- Lists and list methods, and
- Basic numpy functionality (numpy is the Python library for basic linear algebra and matrix operations).
Keras Cheat Sheet (Datacamp)
This 1-page cheat sheet is worth your time if you are looking into the specialized machine learning tool Keras. I have not yet used Keras myself but it is considered to be the best abstraction layer for deep learning and neural networks.
Wikipedia defines Keras as follows.
“Keras is an open source neural network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible”.
With such a broad applicability, I am so convinced, I will check out Keras after finishing this blog post. Will you, too?
The Keras Cheat Sheet addresses the following points (from a code-centric perspective).
- Basic usage,
- Data and data structures,
- Multilayer perceptron,
- Convolutional neural networks,
- Recurrent neural networks, and
- Model training, inference, & fine-tuning.
Deep Learning with Keras Cheat Sheet (RStudio)
Simply put: I love this cheat sheet. It’s about deep learning with the open-source neural network library Keras. It is visual, to the point, comprehensive, and understandable. I highly recommend checking out this cheat sheet!
- The 2-page cheat sheet gives you a quick overview of the Keras pipeline for deep learning.
- It shows you how to work with models (e.g. definition, training, prediction, fitting, and evaluation).
- Furthermore, it gives you a visual overview of how to access the diverse layers in the neural network.
- Finally, it provides a short but insightful example of the standard demo problem of handwriting recognition.
Visual Guide to Neural Network Infrastructures (Asimov Institute)
This 1-page visual guide gives you a quick overview of all the most common neural network infrastructures that you will find in the wild. The sheet showcases 27 different architectures. As a machine learning newbie, you will not get much out of this sheet. However, if you are a practitioner in the field of neural networks, you will like it.
The cheat sheet shows 27 neural network architectures including
- Feedforward, Radial basis network, Deep feedforward,
- Recurrent neural network, long / short term memory (LSTM), gated recurrent unit,
- Autoencoder, variational autoencoder, denoising autoencoder, sparse autoencoder,
- Markov chain, Hopfield network,
- Boltzmann machine, restricted Boltzmann machine, deep belief network, and
- Finally, deep convolutional network, deconvolutional network, deep convolutional inverse graphics network, generative adversarial network, liquid state machine, extreme learning machine, echo state network, deep residual network, kohonen network, support vector machine, and neural turing machine.
Pheww, what a list!
Skicit-Learn Python Cheat Sheet (Datacamp)
Another 1-page PDF cheat sheet that gives you a headstart in Python’s library for machine learning scikit-learn. This library is the best single-CPU, general-purpose libraries for machine learning in Python. Python is the most popular programming language in the field of machine learning, so this cheat sheet gives you a lot of value. Get this cheat sheet if you use Python for machine learning.
The topics include
- Basic functionality such as loading and preprocessing the training data,
- Creating the model,
- Model fitting,
- Prediction and inference, and
- Evaluation metrics such as classification metrics, regression metrics, clustering metrics, cross-validation, and model tuning.
Be warned that these concepts are not explained in detail. It only shows how to use them in the skicit-learn library.
Scikit-learn Cheat Sheet: Choosing the Right Estimator (Scikit-learn.org)
This cheat sheet is so valuable – I cannot even describe it in words. Thanks, scikit-learn creators, for posting this awesome piece of art!
It helps you figure out which algorithm to use for which kind of problem. You simply follow the questions in the cheat sheet. As a result, you will reach the recommended algorithm for your problem at hand. This is why I love cheat sheets – they can deliver complex information in little time.
The cheat sheet divides the estimators into four classes:
- Regression, and
- Dimensionality reduction.
Although those classes are not explored in depth, you will already know in which direction to look further. Of course, if you are already an experienced practitioner, the provided information may be too simplistic – but isn’t this true for every cheat sheet?
Build your own opinion now! (Do it.)
Tensorflow Cheat Sheet (Altoros)
Although this cheat sheet is not the most sophisticated one, it is still valuable being one of the few TensorFlow cheat sheets out there.
You know TensorFlow, don’t you? TensorFlow is one of the most popular Github projects and it’s created by Google. Its machine learning API is tailored to deep learning on a heterogeneous computing environment (including GPUs). Nowadays, if you push in the field of deep learning, there is no way you can avoid TensorFlow.
Get a first impression with this cheat sheet and then dive into Google’s TensorFlow system. By the way, you can also use Keras on top of TensorFlow as a more high-level abstraction layer. Check out the Keras cheat sheet described earlier.
The cheat sheet gives you hints about
- The correct installation method,
- Helper functions,
- The name of some important functions in TensorFlow, and
To be frank, I would not recommend learning TensorFlow with this cheat sheet. Why? Because it is not focused on education. Yet, I felt obliged to include the link because there are no better alternatives for TensorFlow. If you know a better resource, please let me know.
Machine Learning Test Cheat Sheet (Cheatography)
Do you know cheatography? It’s like Wikipedia for cheat sheets. Everybody can submit cheat sheets (user-generated content).
After going through most machine learning cheat sheets at Cheatography, I found that this one will be most helpful for most of our readers. It is a well-structured overview of some important machine learning algorithms.
- It shows you that there are three common problems in machine learning: regression, clustering, and classification.
- It gives you the general steps for training a model.
- Finally, it glances over a collection of specific algorithms that you should know when starting out in the field of machine learning. Those are logistic regression, decision tree, random forest, k-means, naive Bayes, k nearest neighbors, and support vector machines.
I know that it is only a first dip into the ocean. But if you are a beginner or intermediate machine learning practitioner, this may just be what you have looked for.
This excellent cheat sheet provides you a quick overview of the most important algorithms and how they are interrelated. It’s a great way to gain an overview of the field of artificial intelligence and machine learning.
Did you enjoy this collection of the best machine learning cheat sheets on the web? I recommend to download all sheets, print them and work through each of them. This will give you a first overview of the field of machine learning. Later, you can decide in which area to dive in further.
Bonus: Many hot machine learning systems (e.g. TensorFlow) require excellent Python programming skills. Do you know all the features, tips, and tricks of Python? If not, I recommend to check out this free Python cheat sheet email course.
The email course will not only provide you with 5 Python cheat sheets (80% of the learning in 20% of the time, remember?) but also with a constant stream of Python programming lectures. It’s 100% free, you can unsubscribe at any time, and I will not spam you. It’s pure value (and occasionally I will send you information about my books and courses). So check it out!
Python is at the core of machine learning today. It has the best library support for machine learning among all programming languages. So, to become a better ML engineer, you may need to study Python. What better way than to download a cheat sheet PDF?
This is the best single cheat sheet. It uses every inch of the page to deliver value and covers everything you need to know to go from beginner to intermediate. Topics covered include container types, conversions, modules, maths, conditionals and formatting to name a few. A highly recommended 2-page sheet!
Here’s a quick download for you: I created this cheating sheet to explain some important NumPy concepts to my coding students.
NumPy is a widely used Python scientific computing package. It simplifies linear algebra, matrix computations, and speeds up data analysis. Knowing NumPy is a prerequisite for other Python packages like pandas or Scikit-Learn.
This Scikit-Learn cheat sheet from DataCamp will kick start your data science project by introducing you to the basic concepts of machine learning algorithms successfully. This cheat sheet is for those who have already started to learn Python packages and for those who would like to take a quick look to get a first idea of the basics for total beginners!
The cheat sheet is from DataCamp.com and is chock full of information for you to consume. You will learn to interact with Numpy and know which functions and methods to use for linear algebra and of course a help section. This is one I would hang behind my monitor behind the wall!
This one is from the pandas guys, so it makes sense that this is a comprehensive and inclusive cheat sheet. It covers the vast majority of what most pandas users will ever need to do to a DataFrame. Have you already used pandas for a little while? And are you looking to up your game? This is your cheat sheet! However, if you are newer to pandas and this cheat sheet is a bit overwhelming, don’t worry! You definitely don’t need to understand everything in this cheat sheet to get started.
Where to Go From Here?
Enough theory. Let’s get some practice!
Coders get paid six figures and more because they can solve problems more effectively using machine intelligence and automation.
To become more successful in coding, solve more real problems for real people. That’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?
You build high-value coding skills by working on practical coding projects!
Do you want to stop learning with toy projects and focus on practical code projects that earn you money and solve real problems for people?
🚀 If your answer is YES!, consider becoming a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.
If you just want to learn about the freelancing opportunity, feel free to watch my free webinar “How to Build Your High-Income Skill Python” and learn how I grew my coding business online and how you can, too—from the comfort of your own home.
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While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.
To help students reach higher levels of Python success, he founded the programming education website Finxter.com that has taught exponential skills to millions of coders worldwide. He’s the author of the best-selling programming books Python One-Liners (NoStarch 2020), The Art of Clean Code (NoStarch 2022), and The Book of Dash (NoStarch 2022). Chris also coauthored the Coffee Break Python series of self-published books. He’s a computer science enthusiast, freelancer, and owner of one of the top 10 largest Python blogs worldwide.
His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.