Top 10 AI and Data Science Cheat Sheets That Aspirants Should Refer To

by Aratrika Dutta
April 16, 2022

The best way to gain a grip on AI tools and techniques is by using AI and data science cheat sheets

There are various tools and techniques in AI and data science that you need to bear in mind. But it’s quite challenging for everyone to recall all the functions, formulas, and operations of each of the concepts. But the best way to gain a grip on them is by using AI and data science cheat sheets. AI and Data science cheat sheets are amazing resources for learning and practicing shortcut information about a certain topic. If you are looking for such information, then here are the top AI and data science cheat sheets for you.


Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models. In no time, this Keras cheat sheet will make you familiar with how you can load datasets from the library itself, preprocess the data, build up a model architecture, and compile, train, and evaluate it.


Given the fact that it’s one of the fundamental packages for scientific computing, NumPy is one of the packages that you must be able to use and know if you want to do AI with Python. It offers a great alternative to Python lists, as NumPy arrays are more compact, allow faster access to reading and writing items, and are more convenient and more efficient overall.


Pandas library is one of the most preferred tools for data manipulation and analysis, and you’ll have explored the fast, flexible, and expressive Pandas data structures, perhaps with the help of DataCamp’s Pandas Basics cheat sheet.

Use-Case Based Cheat Sheet

Use-Case Based Cheat Sheet by Yogita Kinha, currently Associate Managing Consultant at Mastercard. This one can be considered good because it does a good job aligning the algorithm with the use case. For instance, a regression can be used to predict product demand or sales figures, anomaly detection for fraud, and clustering for customer segmentation.


This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. Scikit-learn (formerly scikits. Learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means, and DBSCAN.


If you ever observe the specific AI tutorials, you will find that the code implementation is done using Jupyter Notebooks. Jupyter Notebooks are great for building various computer science applications and sharing your code with others. It can contain code, text, and visualization all in the same place.

Natural Language Processing

NLP is the most popular branch of AI in the market. It deals with enabling the computer to understand and comprehend natural language. NLP is a technology that enables many of today’s advanced technologies like automatic translators and virtual assistants.


Data visualization is a key concept and it is not just used in finding results from the beginning of the project to explore the data and know-how to analyze it but also to find patterns or trends within it. It is one of the best AI and data science cheat sheets to use for 2022.


While talking about visualization, you can design and create your visualization in Python using Matplotlib. And then from Matplotlib to data visualization Pandas are applied for data analysis. It is a potent and sufficient library that allows you to create various types of visualizations with ease.


Data science is a field that makes the collecting and analysis of data to predict future data and events. It helps businesses find trends, patterns, and others. It also helps people to better understand behavior and language. This is one of the best data science cheat sheets that cover the basics of statistics in a short manner. It covers all the information one needs to make decisions and predictions concerning the projects.

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