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
- Model Evaluation Metrics in Machine Learning - May 28, 2020A detailed explanation of model evaluation metrics to evaluate a classification machine learning model.
- Taming Complexity in MLOps - May 28, 2020A greatly expanded v2.0 of the open-source Orbyter toolkit helps data science teams continue to streamline machine learning delivery pipelines, with an emphasis on seamless deployment to production.
- KDnuggets™ News 20:n21, May 27: The Best NLP with Deep Learning Course is Free; Your First Machine Learning Web App - May 27, 2020Also: Python For Everybody: The Free eBook; Complex logic at breakneck speed: Try Julia for data science; An easy guide to choose the right Machine Learning algorithm; Dataset Splitting Best Practices in Python; Appropriately Handling Missing Values for Statistical Modelling and Prediction
- Dataset Splitting Best Practices in Python - May 26, 2020If you are splitting your dataset into training and testing data you need to keep some things in mind. This discussion of 3 best practices to keep in mind when doing so includes demonstration of how to implement these particular considerations in Python.
- 10 Useful Machine Learning Practices For Python Developers - May 25, 2020While you may be a data scientist, you are still a developer at the core. This means your code should be skillful. Follow these 10 tips to make sure you quickly deliver bug-free machine learning solutions.
Most popular (badge-winning) recent posts on Python
- Build and deploy your first machine learning web app [Silver Blog]A beginner’s guide to train and deploy machine learning pipelines in Python using PyCaret.
- Natural Language Processing Recipes: Best Practices and Examples [Gold Blog]Here is an overview of another great natural language processing resource, this time from Microsoft, which demonstrates best practices and implementation guidelines for a variety of tasks and scenarios.
- Five Cool Python Libraries for Data Science [Gold Blog]Check out these 5 cool Python libraries that the author has come across during an NLP project, and which have made their life easier.
- Coronavirus COVID-19 Genome Analysis using Biopython [Silver Blog]So in this article, we will interpret, analyze the COVID-19 DNA sequence data and try to get as many insights regarding the proteins that made it up. Later will compare COVID-19 DNA with MERS and SARS and we’ll understand the relationship among them.
- How to Do Hyperparameter Tuning on Any Python Script in 3 Easy Steps [Silver Blog]With your machine learning model in Python just working, it's time to optimize it for performance. Follow this guide to setup automated tuning using any optimization library in three steps.
- Stop Hurting Your Pandas! [Silver Blog]This post will address the issues that can arise when Pandas slicing is used improperly. If you see the warning that reads "A value is trying to be set on a copy of a slice from a DataFrame", this post is for you.
- Python for data analysis… is it really that simple?!? [Silver Blog]The article addresses a simple data analytics problem, comparing a Python and Pandas solution to an R solution (using plyr, dplyr, and data.table), as well as kdb+ and BigQuery solutions. Performance improvement tricks for these solutions are then covered, as are parallel/cluster computing approaches and their limitations.
- Python and R Courses for Data Science [Silver Blog]Since Python and R are a must for today's data scientists, continuous learning is paramount. Online courses are arguably the best and most flexible way to upskill throughout ones career.
- Fourier Transformation for a Data Scientist [Gold Blog]The article contains a brief intro into Fourier transformation mathematically and its applications in AI.
- How to Optimize Your Jupyter Notebook [Gold Blog]This article walks through some simple tricks on improving your Jupyter Notebook experience, and covers useful shortcuts, adding themes, automatically generated table of contents, and more.
- 10 Python Tips and Tricks You Should Learn Today [Silver Blog]Check out this collection of 10 Python snippets that can be taken as a reference for your daily work.
- Predict Electricity Consumption Using Time Series Analysis [Silver Blog]Time series forecasting is a technique for the prediction of events through a sequence of time. In this post, we will be taking a small forecasting problem and try to solve it till the end learning time series forecasting alongside.
- Build Pipelines with Pandas Using pdpipe [Gold Blog]We show how to build intuitive and useful pipelines with Pandas DataFrame using a wonderful little library called pdpipe.
- Plotnine: Python Alternative to ggplot2 [Silver Blog]Python's plotting libraries such as matplotlib and seaborn does allow the user to create elegant graphics as well, but lack of a standardized syntax for implementing the grammar of graphics compared to the simple, readable and layering approach of ggplot2 in R makes it more difficult to implement in Python.
- 10 Free Top Notch Machine Learning Courses [Gold Blog]Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.
- Open Source Projects by Google, Uber and Facebook for Data Science and AI [Gold Blog]Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public.
- Getting Started with Automated Text Summarization [Silver Blog]This article will walk through an extractive text summarization process, using a simple word frequency approach, implemented in Python.
- Automated Machine Learning Project Implementation Complexities [Silver Blog]To demonstrate the implementation complexity differences along the AutoML highway, let's have a look at how 3 specific software projects approach the implementation of just such an AutoML "solution," namely Keras Tuner, AutoKeras, and automl-gs.
- Python, Selenium & Google for Geocoding Automation: Free and Paid [Gold Blog]This tutorial will take you through two options that have automated the geocoding process for the user using Python, Selenium and Google Geocoding API.
- Data Science for Managers: Programming Languages [Silver Blog]In this article, we are going to talk about popular languages for Data Science and briefly describe each of them.
- How to Speed up Pandas by 4x with one line of code [Platinum Blog]While Pandas is the library for data processing in Python, it isn't really built for speed. Learn more about the new library, Modin, developed to distribute Pandas' computation to speedup your data prep.