Machine learning has become a term that is thrown around so often that it’s becoming more difficult to understand precisely what it is and what it’s supposed to do. Machine learning definitions are either too broad or too narrow to understand how Machine Learning really works and there are so many machine learning cheat sheets out there that things often get confusing.
What is Machine Learning?
The most comprehensive definition of Machine Learning is, “ the science of getting computers to learn and act like humans do, and improve their learning over time in an autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”
Another good definition has been given by Dr Danko Nikolic, at the CSC and Max-Planck Institute who defines Machine Learning as, “the science of getting computers to act without being explicitly programmed, but instead letting them learn a few tricks on their own.”
The main goal of any Machine Learning algorithm is to create generalizations from training samples that enable the computer to interpret data it has never seen before.
Basic Concepts of Machine Learning
Machine learning algorithms can be classified in one of the two ways.
- By learning style which depends on how much supervision is needed for the machine to learn. This includes supervised learning, unsupervised learning, and semi-supervised learning.
- By the form in which it is built; such as classification, regression, decision tree, clustering, and deep learning. The approach that you use differs from a basic decision tree to clustering to layers of artificial neural networks, depending on the task you want to accomplish and the amount of data that you have. Some visual examples are given below.
Decision Tree Model
Gaussian Mixture Model
Why do you need Machine Learning Cheat Sheets?
Now is a great time to be a Machine Learning(ML) engineer. Gone are the days where ML engineers had to worry about building platforms and hand-rolling numerical algorithms.
Nowadays, there are so many ML tools and resources available; from AWS’s deep learning AMIs to Android’s NN API, that a Machine Learning engineer can just focus on solving critical problems instead of worrying about the heavy lifting.
Then why is it that there continue to be such few Machine Learning engineers in the world today? A major reason is that most engineers have a very unidimensional approach to Machine Learning.
They understand the purpose of Machine Learning, Machine Learning execution and then just learn and use just 2-3 algorithms on their current project.
On the other hand, the biggest challenge and steepest learning curve in Machine Learning come from understanding more advanced algorithms and techniques.
In fact, the best part of Machine Learning is coming up with unique solutions when none of the Machine Learning algorithms is performing as expected. This usually happens when there is a problem with the training data or when Machine Learning is being applied to a new domain altogether.
Unfortunately, most engineers stop at 2-3 Machine Learning algorithms and then feel that Machine Learning is very computation heavy and that they can’t improve their ML models beyond a point.
What they don’t realize is that they are in fact very close to acquiring the level of expertise that can make them invaluable in the job market as Machine Learning engineers. This is where Machine Learning algorithm cheat sheets come in as they can enable ML engineers to learn continuously and really up their game.
Some important Machine Learning cheat sheets
There are different aspects to Machine Learning and the right machine learning cheat sheet can help you with both basic concepts of Machine Learning and advanced detail whether its Machine Learning algorithms, neural network architecture or the underlying mathematical concepts like regression and probability.
Here’s a list of some of the most important Machine Learning cheat sheets that you must have if you’re tackling Machine Learning.
Machine learning cheat sheets on the basic concepts
There are some fantastic machine learning cheat sheets that outline some of the most critical Machine Learning concepts in a very succinct way. Here are four must-have cheat sheets on some critical ML concepts.
Supervised Learning Cheat Sheet
Supervised learning, is a type of Machine Learning model in which both input and desired output data are provided to the system. Input and output data are labeled for classification so that the machine now has a basis for future data processing.
Unsupervised Learning Cheat Sheet
Unsupervised Learning is a kind of Machine Learning technique which helps find the patterns in data. In this case, the data is not labeled, which means only the input variables(X) are given without any corresponding output variables.
Deep Learning Cheat Sheet
Deep learning is a branch of Machine Learning which uses algorithms called artificial neural networks. These algorithms are inspired by the way our brain functions and many experts believe are therefore our best shot to moving art towards real AI (Artificial Intelligence).
Deep learning is becoming especially exciting now as we have more amounts of data and larger neural networks to work with. Moreover, the performance of neural networks improves as they grow bigger and work with more and more data, unlike other Machine Learning algorithms which can reach a plateau after a point.
This machine learning cheat sheet on types of neural networks helps you understand the complete fundamentals of deep learning.
Library and Machine Learning Algorithm Cheat Sheets
There are some excellent open-source libraries for different aspects of Machine Learning from deep learning to scientific computing. Here are the must-have machine learning cheat sheets for some of the most important libraries.
Keras Cheat Sheet
Keras is a great tool for deep learning and neural networks. It’s a powerful and comprehensive library for Theano and TensorFlow that can help you evaluate and develop deep learning models. It’s great for advanced Machine Learning engineers who want to understand neural networks and deep learning.
NumPy Cheat Sheet
NumPy is a fundamental tool when it comes to scientific computing in Python. NumPy is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, random simulation, basic linear algebra and so on.
Here’s the ultimate NumPy cheat sheet with all the basics from data types to creating, inspecting and manipulating arrays.
SciPy Cheat Sheet
SciPy is another package that is essential for scientific computing and a great one to pick up once you master NumPy. It provides mathematical algorithms and convenience functions that are built on NumPy.
SciPy contains modules for optimization, integration, FFT, linear algebra, interpolation, signal and image processing, ODE solvers and much more.
Here’s a great machine learning cheat sheet that will help you with the most commonly used functions in SciPy.
Scikit-Learn Cheat Sheet
Scikit learn is an open-source Machine Learning library in Python. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN. It has been designed to work in conjunction with NumPy and SciPy.
Here’s a great Scikit-Learn cheat sheet that shows you how to create, evaluate and tune your Machine Learning model.
Matlplotlib
Matplotlib is a 2D plotting library for Python. It has been built for use by the scientific computing library NumPy. It provides an object-oriented API for embedding plots into applications with general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+.
It produces a variety of publication-quality figures in many hardcopy formats and interactive environments across different platforms.
Here’s the ultimate machine learning cheat sheet which guides you on how to create, customize and show plots using Matplotlib.
Here’s a video which also has a great collection of machine learning cheat sheets that you can refer to.
How to use Machine Learning Cheat Sheets
Most machine learning cheat sheets are as good as code guides and can really give you the boost you need to go from being a Machine Learning skeptic to an advocate.
However, cheat sheets do have their limitations and should, therefore, be used with caution.
The major limitation of relying completely on machine learning algorithm cheat sheets is that they are more like code guides and information repositories.
Once you know which algorithm or library you need to use and how you need to go about solving a particular Machine Learning problem, cheat sheets can be extremely useful.
However, a big part of what makes a great Machine Learning engineer is the ability to choose from among different machine learning models.
Getting these decisions right is key to developing great Machine Learning based products. In order to make these choices, you need to know the ins and outs of the best machine learning models.
This can only come if you have an in-depth understanding of how ML algorithms work and what their underlying assumptions and behavior are. Machine learning cheat sheets cannot help much in this scenario.
The right approach to becoming a good machine learning engineer is by beginning with a fundamental understanding. Join the Machine Learning Course and learn the different models and behavior patterns of Machine Learning.
Once that is in place, cheat sheets will help you as you solve actual Machine Learning problems and will allow you to keep growing and transforming into an extraordinary Machine Learning engineer.
