Python has rapidly became a leading language for Data Science and Machine Learning. In the latest KDnuggets Poll 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
- Pydon’ts – Write elegant Python code: Free Book Review - May 23, 2022The book consists of 200 actionable Python insights with a detailed explanation of how to write elegant, compelling, and expressive code.
- The 6 Python Machine Learning Tools Every Data Scientist Should Know About - May 20, 2022Let's look at six must-have tools every data scientist should use.
- Why You Need To Learn Python In 2022 - Apr 28, 2022If you don’t already know a programming language, or if you’re deciding to choose another language, have a read and see if Python is for you.
- How to Determine the Best Fitting Data Distribution Using Python - Apr 19, 2022Approaches to data sampling, modeling, and analysis can vary based on the distribution of your data, and so determining the best fit theoretical distribution can be an essential step in your data exploration process.
- 5 Different Ways to Load Data in Python - Apr 15, 2022Data is the bread and butter of a Data Scientist, so knowing many approaches to loading data for analysis is crucial. Here, five Python techniques to bring in your data are reviewed with code examples for you to follow.
Most popular (badge-winning) recent posts on Python
- What Makes Python An Ideal Programming Language For Startups [Silver Blog]In this blog, we will discuss what makes Python so popular, its features, and why you should consider Python as a programming language for your startup.
- 3 Tools to Track and Visualize the Execution of Your Python Code [Gold Blog]Avoid headaches when debugging in one line of code.
- Three R Libraries Every Data Scientist Should Know (Even if You Use Python) [Silver Blog]Check out these powerful R libraries built by the world’s biggest tech companies.
- Write Clean Python Code Using Pipes [Platinum Blog]A short and clean approach to processing iterables.
- ORDAINED: The Python Project Template [Silver Blog]Recently I decided to take the time to better understand the Python packaging ecosystem and create a project boilerplate template as an improvement over copying a directory tree and doing find and replace.
- Introduction to AutoEncoder and Variational AutoEncoder (VAE) [Silver Blog]Autoencoders and their variants are interesting and powerful artificial neural networks used in unsupervised learning scenarios. Learn how autoencoders perform in their different approaches and how to implement with Keras on the instructional data set of the MNIST digits.
- Deploying Your First Machine Learning API [Silver Blog]Effortless way to develop and deploy your machine learning API using FastAPI and Deta.
- The 20 Python Packages You Need For Machine Learning and Data Science [Gold Blog]Do you do Python? Do you do data science and machine learning? Then, you need to do these crucial Python libraries that enable nearly all you will want to do.
- Here’s Why You Need Python Skills as a Machine Learning Engineer [Silver Blog]If you want to learn how to apply Python programming skills in the context of AI applications, the UC San Diego Extension Machine Learning Engineering Bootcamp can help. Read on to find out more about how machine learning engineers use Python, and why the language dominates today’s machine learning landscape.
- Teaching AI to Classify Time-series Patterns with Synthetic Data [Silver Blog]How to build and train an AI model to identify various common anomaly patterns in time-series data.
- How To Build A Database Using Python [Silver Blog]Implement your database without handling the SQL using the Flask-SQLAlchemy library.
- Path to Full Stack Data Science [Gold Blog]Start your journey toward mastering all aspects of the field of Data Science with this focused list of in-depth self-learning resources. Curated with the beginner in mind, these recommendations will help you learn efficiently, and can also offer existing professionals useful highlights for review or help filling in any gaps in skills.
- How to be a Data Scientist without a STEM degree [Silver Blog]Breaking into data science as a professional does require technical skills, a well-honed knack for problem-solving, and a willingness to swim in oceans of data. Maybe you are coming in as a career change or ready to take a new learning path in life--without having previously earned an advanced degree in a STEM field. Follow these tips to find your way into this high-demand and interesting field.
- Do You Read Excel Files with Python? There is a 1000x Faster Way [Platinum Blog]In this article, I’ll show you five ways to load data in Python. Achieving a speedup of 3 orders of magnitude.
- Learning Data Science and Machine Learning: First Steps After The Roadmap [Silver Blog]Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML.
- Django’s 9 Most Common Applications [Gold Blog]Django is a Python web application framework enjoying widespread adoption in the data science community. But what else can you use Django for? Read this article for 9 use cases where you can put Django to work.
- Prefect: How to Write and Schedule Your First ETL Pipeline with Python [Gold Blog]Workflow management systems made easy — both locally and in the cloud.
- How to Query Your Pandas Dataframe [Gold Blog]A Data Scientist’s perspective on SQL-like Python functions.
- GPU-Powered Data Science (NOT Deep Learning) with RAPIDS [Gold Blog]How to utilize the power of your GPU for regular data science and machine learning even if you do not do a lot of deep learning work.
- Why and how should you learn “Productive Data Science”? [Gold Blog]What is Productive Data Science and what are some of its components?