Python leads the 11 top Data Science, Machine Learning platforms. This page brings you the latest KDnuggets Opinions and Tutorials related to Python, as well as our most popular - gold and silver-badge winning content. Enjoy!
Latest posts on Python
- How to Apply Transformers to Any Length of Text - Apr 12, 2021Read on to find how to restore the power of NLP for long sequences.
- E-commerce Data Analysis for Sales Strategy Using Python - Apr 7, 2021Check out this informative and concise case study applying data analysis using Python to a well-defined e-commerce scenario.
- KDnuggets™ News 21:n13, Apr 7: Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Making Machine Learning Models Understandable - Apr 7, 2021Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Shapash: Making Machine Learning Models Understandable; Easy AutoML in Python; The 8 Most Common Data Scientists; A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 1
- Automated Text Classification with EvalML - Apr 6, 2021Learn how EvalML leverages Woodwork, Featuretools and the nlp-primitives library to process text data and create a machine learning model that can detect spam text messages.
- The Best Machine Learning Frameworks & Extensions for TensorFlow - Apr 5, 2021Check out this curated list of useful frameworks and extensions for TensorFlow.
Most popular (badge-winning) recent posts on Python
- Shapash: Making Machine Learning Models Understandable [Gold Blog]Establishing an expectation for trust around AI technologies may soon become one of the most important skills provided by Data Scientists. Significant research investments are underway in this area, and new tools are being developed, such as Shapash, an open-source Python library that helps Data Scientists make machine learning models more transparent and understandable.
- Top 10 Python Libraries Data Scientists should know in 2021 [Gold Blog]So many Python libraries exist that offer powerful and efficient foundations for supporting your data science work and machine learning model development. While the list may seem overwhelming, there are certain libraries you should focus your time on, as they are some of the most commonly used today.
- Know your data much faster with the new Sweetviz Python library [Silver Blog]One of the latest exploratory data analysis libraries is a new open-source Python library called Sweetviz, for just the purposes of finding out data types, missing information, distribution of values, correlations, etc. Find out more about the library and how to use it here.
- Are You Still Using Pandas to Process Big Data in 2021? Here are two better options [Silver Blog]When its time to handle a lot of data -- so much that you are in the realm of Big Data -- what tools can you use to wrangle the data, especially in a notebook environment? Pandas doesn’t handle really Big Data very well, but two other libraries do. So, which one is better and faster?
- Data Science Learning Roadmap for 2021 [Gold Blog]Venturing into the world of Data Science is an exciting, interesting, and rewarding path to consider. There is a great deal to master, and this self-learning recommendation plan will guide you toward establishing a solid understanding of all that is foundational to data science as well as a solid portfolio to showcase your developed expertise.
- Powerful Exploratory Data Analysis in just two lines of code [Gold Blog]EDA is a fundamental early process for any Data Science investigation. Typical approaches for visualization and exploration are powerful, but can be cumbersome for getting to the heart of your data. Now, you can get to know your data much faster with only a few lines of code... and it might even be fun!
- Approaching (Almost) Any Machine Learning Problem [Silver Blog]This freely-available book is a fantastic walkthrough of practical approaches to machine learning problems.
- Essential Math for Data Science: Introduction to Matrices and the Matrix Product [Silver Blog]As vectors, matrices are data structures allowing you to organize numbers. They are square or rectangular arrays containing values organized in two dimensions: as rows and columns. You can think of them as a spreadsheet. Learn more here.
- Build Your First Data Science Application [Silver Blog]Check out these seven Python libraries to make your first data science MVP application.
- How to create stunning visualizations using python from scratch [Platinum Blog]Data science and data analytics can be beautiful things. Not only because of the insights and enhancements to decision-making they can provide, but because of the rich visualizations about the data that can be created. Following this step-by-step guide using the Matplotlib and Seaborn libraries will help you improve the presentation and effective communication of your work.
- Getting Started with 5 Essential Natural Language Processing Libraries [Silver Blog]This article is an overview of how to get started with 5 popular Python NLP libraries, from those for linguistic data visualization, to data preprocessing, to multi-task functionality, to state of the art language modeling, and beyond.
- Cleaner Data Analysis with Pandas Using Pipes [Silver Blog]Check out this practical guide on Pandas pipes.
- Best Python IDEs and Code Editors You Should Know [Platinum Blog]Developing machine learning algorithms requires implementing countless libraries and integrating many supporting tools and software packages. All this magic must be written by you in yet another tool -- the IDE -- that is fundamental to all your code work and can drive your productivity. These top Python IDEs and code editors are among the best tools available for you to consider, and are reviewed with their noteworthy features.
- 10 Underappreciated Python Packages for Machine Learning Practitioners [Gold Blog]Here are 10 underappreciated Python packages covering neural architecture design, calibration, UI creation and dissemination.
- 15 Free Data Science, Machine Learning & Statistics eBooks for 2021 [Platinum Blog]We present a curated list of 15 free eBooks compiled in a single location to close out the year.
- Generating Beautiful Neural Network Visualizations [Gold Blog]If you are looking to easily generate visualizations of neural network architectures, PlotNeuralNet is a project you should check out.
- Monte Carlo integration in Python [Gold Blog]A famous Casino-inspired trick for data science, statistics, and all of science. How to do it in Python?
- A Rising Library Beating Pandas in Performance [Gold Blog]This article compares the performance of the well-known pandas library with pypolars, a rising DataFrame library written in Rust. See how they compare.
- R or Python? Why Not Both? [Silver Blog]Do you use both R and Python, either in different projects or in the same? Check out prython, an IDE designed to handle your needs.
- Object-Oriented Programming Explained Simply for Data Scientists [Gold Blog]Read this simple but effective guide to start using Classes in Python 3.
- TabPy: Combining Python and Tableau [Platinum Blog]This article demonstrates how to get started using Python in Tableau.