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Machine Learning Cheat sheets Every ML Engineer Should Know About

In the past couple of decades, machine learning has drastically changed the way things work and how decisions are made. Today, almost every industry is making efficient use of different machine learning concepts in one way or the other. Due to this, there has been a drastic increase in the number of machines learning-related jobs, and more and more job seekers and freshers are trying their best to learn machine learning skills.

We all know that machine learning is a vast field, and there are numerous concepts that one needs to remember, even if he/she is frequently exposed to similar tasks. Hence, it becomes effortless for learners to revise and revisit the basic concepts and tricks if they have access to some short notes. It helps them prepare for interviews, refer while making new changes, and even quickly discover a new concept. Hence, in this article, we will list the top machine learning cheat sheets that will help the professionals and learners of machine learning.

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Python Cheat Sheet by Dave Child

To start with any digital development, one needs a programming language. Python is the most preferred programming language for machine learning enthusiasts due to its ease of use, full accessibility, and excellent community support. Hence, keeping the syntax and basic tricks in handy helps whenever you need to brush up the functioning of the language.

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This beautiful sheet by Dave Child contains all the essential functions of strings, lists, etc. It also has a vast set of information on the system and local variables, slicing, and data formatting methods. Hence, for machine learning enthusiasts, this cheat sheet for Python satisfies the purpose of quick remembering and referencing. 

The Python cheat sheet for machine learning enthusiasts by Dave Child can be found here.

Numpy Cheat Sheet by Justin

We all know that machine learning is all about numbers. In fact, in machine learning, we have a large set or large arrays of numbers. Although there are inbuilt options like lists and tuples to manage this data, they are not as usable as per the requirements. Hence, most of the machine learning enthusiasts use a library dedicated to numerical computations called Numpy.

Numpy is one of the most popular libraries that can handle large arrays and manipulate them according to user needs. While playing with a broad set of data, Numpy saves a lot of time for the user and makes it easier for him/her to intuitively understand the flow and structure of the data.

This beautiful cheat sheet by Justin covers all the primary syntactical techniques used in Numpy. It includes all the primary array operations, multidimensional access, etc. A quick view of the ordinary and binomial distribution is also provided.

The Numpy machine learning cheat sheet can be accessed here.

Pandas Cheat Sheet by Sanjeev

If you are doing intensive machine learning, there are high chances that you will be reading and writing different kinds of data regularly. Although Python has some inbuilt libraries to do the task, it does not stand as per the expectations for reading and analyzing vast amounts of complex data. For this, most of the machine learning professionals and learners use Pandas.

Pandas is a library that makes it very easy for the users to read complex data forms, select important information, and write data accordingly. Hence, keeping a cheat sheet handy helps in quickly referencing syntax and techniques.

This cheat sheet provides a quick look into the essential functions like reading the data, selecting sorting, etc. On top of this, it also includes basic data queries like joins, merges, etc. 

The Pandas machine learning cheat sheet can be accessed here.

Matplotlib cheat sheet by Justin

Matplotlib can quickly draw complex graphs and diagrams. 

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When you are supposed to work with a huge amount of data, it becomes sometimes challenging to analyze and visualize the type and flow of data. Before making any algorithms, it is imperative to understand how the data is behaving. For this purpose, we use visual representations. There are several graphs and plots like a bar graph, box plot, line graphs, etc. that can be plotted for this purpose.

Matplotlib is a beautifully designed library that helps the users to plot multiple kinds of graphs in one place. It is trendy for its ease of use and flexibility. 

This cheat sheet gives you instant access to plot basic diagrams and figures. It shows all the syntax of matplotlib’s popular component Pyplot for plotting bar graphs, line graphs, legends, pie charts, etc.

The Matplotlib machine learning cheat sheet can be found here.

Scikit Learn Cheat Sheet by Sati

Now we have all the necessary cheat sheets required for handling the data. Once we get the data, we tend to apply algorithms and machine learning models to it in a quest to make a better sense out of the structured data. Writing models from scratch is a very tedious and repetitive task. Hence, professionals have developed specific libraries to run these models and train more and more new models on the datasets we get. 

One of such libraries is Scikit Learn. This is one of the most popular libraries used to train new models and test them on real data. Different algorithms from logistic regression to complex clustering can be used with the help of this library. Hence, it is essential to keep all the syntax and basic concepts handy.

This cheat sheet includes all the basic syntax and theory for regression, cross-validation, clustering, etc. topped with trivial visualizations.

The machine learning cheat sheet for Scikit Learn can be accessed here.

Deep Learning Cheat Sheet 1webzem

Deep learning models give better accuracy over a large amount of data.

Although Scikit covers a wide range of machine learning algorithms, when the data grows more massive, and patterns become complex, those algorithms tend towards a saturation point in terms of accuracy. Hence, we need more sophisticated and robust models powered by Deep Learning. The mathematics and theory involved in Deep Learning algorithms are very complex and need frequent revision. Hence, using a cheat sheet is very advisable.

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The deep learning cheat sheet by 1webzem contains most of the underlying algorithms, the syntax of the most popular deep learning library– Keras, and a few theoretical concepts that are used frequently.

The machine learning cheat sheet for deep learning can be accessed here.

Also Read: Tensorflow Cheat Sheet

Azure ML Algorithm Cheat Sheet by Microsoft

The Microsoft Azure team has developed this cheat sheet itself to help you select the correct algorithm for a predictive analysis model. It is important to remember that your choice depends on the nature of the data and the subsequent goal you want to compete with it. 

The Azure machine learning algorithms cheat sheet gives you a clear idea about the nature of the data. It is helpful to navigate the massive library of algorithms from clustering, regression, classification, recommender systems, text analytics and anomaly detection families.

Each algorithm helps you solve a different machine learning problem. Remember the cheat sheet is a starting point, and you may have issues requiring multiple algorithms to solve or just one to tackle other issues. Trial and error is a good way to understand which one works best.

The Azure machine learning cheat sheet can be found here.

Neural Network Architectures Cheat Sheet by Fjodor Van Veen

One of the best machine learning cheat sheets out there, the neural network architectures cheat sheet helps you navigate the new and old neural network architects and their abbreviations.

Since new architectures are being developed every day, the list is not comprehensive as it came out a while back. However, you will find several useful neural networks to achieve your objectives. You can also refer to the explanations provided in the blog to learn more about those that you might not be familiar with.

The neural network architectures cheat sheet can be found here.

SAS Algorithm Flowchart by Hui Li

The SAS algorithm flowchart is a great ml cheat sheet for beginners and those familiar with the basics of machine learning. Anyone struggling to identify and use ml algorithms will find this comprehensive cheat sheet useful. You can easily refer to the flowchart to identify and address your problem.

Since beginners often face the question of using the right algorithm for their purpose, the cheat sheet gives them multiple options to choose from and apply the one that seems the most suitable for their problem.

Read the blog to gain insight into how to use the cheat sheet. The algorithms offered in the cheat sheet have been taken from tips and feedback offered by various machine learning experts and developers. Further algorithms will be added subsequently as the available methods grow, and the library increases. 

The SAS algorithm flowchart can be found here.

PySpark Cheat Sheet by Data Camp

A basic PySpark cheat sheet, this document will tell you how to initialize Spark and load the data, retrieve RDD information, sort, filter and sample the data. You will also be able to learn how to merge, iterate, repartition and save the data, along with stopping the SparkContext. 

There are various other PySpark cheat sheets available on the website that you can use to solve your data issues. When you go through the document, you should remember that it has taken small data sets as examples of portraying certain data functions. In real life, you will be analyzing big data using Spark. 

The PySpark cheat sheet can be found here.

The Road Ahead

If you are a machine learning enthusiast and want to emerge further into your career, you should opt for upGrad’s PG Diploma in Machine Learning & AI. This program is mentored by one of the best instructors from IIIT-B. It will cover all the essential topics like data visualization, machine learning, deep learning, etc. followed by real-life industry projects.

What are the skills needed to become a machine learning engineer?

You should definitely have a good grasp of software engineering and programming concepts. In addition, you should be familiar with concepts like NLP, reinforcement learning, etc. Apart from the technical skills, some soft skills are also required. You must know how to communicate with your clients and team members. Last but not the least, you should have a thirst to learn more about ML in order to grow and eventually perform well.

What are the mandatory certifications required if you are willing to become an ML engineer?

Most machine learning engineering jobs need a bachelor's degree in a related subject like computer science, mathematics, or statistics, and some even demand a master's degree or Ph.D. in machine learning, computer vision, neural networks, deep learning, or another similar topic. Certifications in machine learning, artificial intelligence, or data science are beneficial outside of higher education since they offer applicable skills.

Should I learn SQL if I want to become a machine learning engineer?

In machine learning, pattern detection is a crucial step. By organizing enormous amounts of data, SQL considerably improves pattern recognition. SQL is the simplest language for querying data. Additionally, mastering SQL will allow you to take advantage of efficiencies later on by combining SQL with Python. Hence, SQL leverages the benefits of the R language when used in combination with a relational database for machine learning applications. If you want to be a machine learning engineer, understanding SQL is not only necessary, but it will also make a lot of your job easier.

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