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Are you looking for quick and useful references for a variety of topics in data science, machine learning, Python programming, data engineering, and artificial intelligence? Do you want to stay up to date while improving your skills in these areas? The collection of cheat sheets that KDnuggets has created throughout 2023 aims to help you achieve these goals.
You'll find these cheat sheets to be valuable resources that will help you stay ahead of some of this year's most useful and relevant tools, technologies, and concepts. Whether you're a seasoned data scientist, a budding machine learning enthusiast, or a data engineering professional, these professionally crafted resources are sure to provide you with nugget-sized bullet points of importance.
From practical applications of ChatGPT in data science to mastering valuable data tools like GitHub CLI, Plotly Express, and cuDF, each cheat sheet is designed to offer concise, actionable insights. Learn machine learning with Streamlit. Explore data cleansing with Python. Venture into the realm of ai with helpful Chrome extensions and generative ai tools. Consider this collection your gateway to mastering (and strengthening over time) complex concepts and tools, ensuring you stay ahead of the field.
Go ahead and check out the following KDnuggets cheat sheets and see what information is available.
ChatGPT Cheat Sheet for Data Science
ChatGPT (and indeed the more robust and recent versions of GPT3) is intended to help (that's right… help!) the humans who decide to use it as such, and with a little help from your friends at KDnuggets You'll be able to hone your engineering skills quickly to do useful things like generate code, assist in your research process, and analyze data.
GitHub CLI Cheat Sheet for Data Science
The GitHub CLI, as you would expect, is the GitHub tool that allows interaction with the GitHub platform with the command line interface. Mastering the most commonly used commands will allow you to become a productive development team, whether it's a web application development team or, more specifically for our purposes, a data science, data engineering, or machine learning engineering team.
Plotly Express Cheat Sheet for Data Visualization
The cheat sheet first addresses the first steps, such as installing the library and its basic syntax. The resources below cover creating common chart types with Plotly Express, including: scatterplot, histogram, density heatmap, pie chart, and boxplot. Finally, you'll get some exposure to plot customization, including adjusting markers and layouts.
RAPIDS cuDF Cheat Sheet
Getting started with cuDF is easy, especially if you have experience using Python and libraries like Pandas. While both cuDF and Pandas offer similar APIs for data manipulation, there are specific types of problems where cuDF can provide significant performance improvements over Pandas, including large-scale data sets, data preprocessing and engineering, analytics in real time and, of course, parallel applications. Processing. The larger the data set, the greater the performance benefits.
ChatGPT Interview Cheat Sheet for Data Science
Mastering data science interviews is a skill in itself and preparing for them is the key to success. Just as I was once told that learning to write college exams is a skill in itself, beyond learning the material on which you are being evaluated, specialized technical job interviews are very similar.
10 ChatGPT Plugins for Data Science Cheat Sheet
For an overview of what we believe are the 10 best ChatGPT plugins for data science, check out our latest cheat sheet, conveniently named 10 ChatGPT Plugins for Data Science Cheat Sheet. You'll find plugins for coding, analytics, web searching, document query, and more.
Streamlit Cheat Sheet for Machine Learning
Combining machine learning and Streamlit is a popular option for data scientists and other data professionals looking to experiment with data, create prototypes, or share results. Knowing how to quickly switch data applications is becoming an essential skill for people who work with data, and this combination certainly allows for that. If you don't know how to use Streamlit, we suggest you learn it now.
Machine Learning Cheat Sheet with ChatGPT
With ChatGPT, creating a machine learning project has never been easier. By simply writing follow-up prompts and analyzing the results, you can quickly and easily train the model to respond to user queries and provide useful insights. In this cheat sheet, learn how to use ChatGPT to help with the following machine learning tasks: project planning, feature engineering, data preprocessing, model selection, hyperparameter tuning, experiment tracking, and MLOps.
Scikit-learn cheat sheet for machine learning
Scikit-learn's unified API interface makes learning to implement a variety of algorithms and tasks much easier than it would otherwise be. Once you learn the pattern of how to make Scikit learning calls, you'll be up and running. The only thing you need after this, beyond your imagination and determination, is a useful reference. This cheat sheet covers the basics of what it takes to learn how to use Scikit-learn for machine learning and provides a reference to move forward with your machine learning projects.
Docker Cheat Sheet for Data Science
Docker has become an essential data science tool to assist in building reproducible and scalable environments. Docker allows you to package code and dependencies in containers, allowing data scientists to distribute their models across different platforms. This helps in both development and production, and works to avoid errors and inconsistencies that can arise from different versions of software or hardware configurations.
Introduction to Graph Database Query Cheat Sheet
In graph queries we lose some of the SQL syntax and gain another syntax. SELECT has been replaced by MATCH. FROM and JOIN have been discarded. But the WHERE and ORDER BY commands are used in the same way. All the added functions like SUM and AVG are there, but GROUP BY has been dropped. Most importantly, however, we gain the ability to query patterns on the graph using node relationships. In the attached cheat sheet you will see a list of the most commonly used query approaches.
Data Cleaning with Python Cheat Sheet
In this cheat sheet, we move from detecting and handling missing data, dealing with duplicates and finding solutions for duplicates, outlier detection, label encoding, and single encoding of categorical features, to transformations such as MinMax normalization and standard normalization. Additionally, this guide exploits methods provided by three of the most popular Python libraries, Pandas, Scikit-Learn, and Seaborn, to display graphs.
Python Control Flow Cheat Sheet
The state of flow control has come a long way since the days of goto. There are numerous common execution patterns that are available in most modern programming languages, although their syntax differs from language to language. Python has its own set of flow controls, usually quite readable, and that's what our latest cheat sheet focuses on. Get ready to learn how to control the flow and have a useful reference to move forward as you conquer the world of coding.
Chrome ai Extensions Cheat Sheet for Data Scientists
The selection of tools presented in this cheat sheet includes SciSpace Copilot, an ai-powered research assistant designed to help you understand text, mathematics, and tables from scientific literature. Also introduced is Fireflies, an artificial intelligence assistant powered by GPT-4. This revolutionary tool can browse the web and summarize various types of content, including articles, YouTube videos, and emails, with human-like efficiency. And more.
Cheat Sheet of the Best Python Tools for Building Generative ai Applications
Some highlights covered include OpenAI for accessing models like ChatGPT, Transformers for training and tuning, Gradio for quickly creating UI for demo models, LangChain for chaining multiple models, and LlamaIndex for ingesting and managing private data. Overall, this cheat sheet contains a lot of practical guidance on a single page. Both beginners looking to get started with generative ai in Python and seasoned professionals can benefit from having this condensed reference of the best tools and libraries at their fingertips.
LangChain Cheat Sheet
With LangChain, developers can create capable applications based on ai languages without having to reinvent the wheel. Its composable structure makes it easy to mix and match components such as LLM, message templates, external tools, and memory. This speeds up prototyping and allows for seamless integration of new capabilities over time. Whether you're looking to build a chatbot, QA bot, or multi-step reasoning agent, LangChain provides the building blocks to assemble advanced ai quickly.
10 ChatGPT Projects Cheat Sheet
The cheat sheet links to tutorials for each project, walking through the implementation step by step taking advantage of ChatGPT's conversational prompts. Highlights include using ChatGPT for a loan approval classifier model, resume analyzer, real-time language translator, exploratory data analysis, and even integrating its capabilities into Google Sheets. Whether you're new to ChatGPT or looking to push its limits, this collection of projects acts as a launchpad to boost productivity and accelerate ai-assisted development.
Matthew May (@mattmayo13) has a master's degree in computer science and a postgraduate diploma in data mining. As Editor-in-Chief of KDnuggets, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, machine learning algorithms, and exploring emerging ai. He is driven by the mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.
