Explore the Navy SEALs approach to performance vs trust and discover how it relates to AI in software development. Learn why trust, not just performance, is crucial for building reliable AI systems and high-performing teams.
Discover what an AI data analyst really is. Learn about AutoML tools, people using AI for data analysis, and autonomous AI agents. Get practical examples and see how AI is changing data analysis for everyone.
Explore practical AutoML projects using 10 popular datasets for binary classification, multiclass classification, and regression tasks. Learn how to effectively use MLJAR AutoML in Python notebook.
Exploratory Data Analysis in Python simplified: Discover powerful tools like Skrub, ydata-profiling, Pygwalker, and AI assistants to quickly analyze data and uncover valuable insights.
You can use OpenAI ChatGPT in Python notebook for advanced data analysis and visualization. When using ChatGPT inside notebook you can control code excution,
Learn how to use ChatGPT inside Jupyter Notebook with MLJAR Studio. You can use OpenAI ChatGPT for Python coding to perform data analysis and visualization.
Let me introduce you an open-source Automated Machine Learning framework called mljar-supervised. It is available as Python package. It can be used with Python API or with graphical interface in the MLJAR Studio - our distribution of JupyterLab with user-friendly extensions.
Variable Inspector is a JupyterLab extension that helps you track variables' names, values, types, shapes, and sizes in real-time. Easily manage variables, preview DataFrames, and customize views - all in one place!
Learn how to simplify package management in JupyterLab! Jupyter Package Manager makes it easy to install, delete, and manage packages - no coding needed. Save time with this free extension!
Learn how to list installed packages in JupyterLab without using the terminal. Use a simple command or the Jupyter Packages Manager for a quick, no-code solution. Save time and code smarter!
Machine Learning teaches computers to learn from data instead of following exact step-by-step instructions. The article explains different algorithms—from simple ones like K-Nearest Neighbors to advanced techniques like Gradient Boosting and AutoML—and discusses their benefits.
Learn how to easily delete packages in JupyterLab without using the terminal. Use a simple command or the Jupyter Packages Manager for a quick, no-code solution. Save time and code smarter!
Discover two simple ways to install packages in JupyterLab without using the terminal. Learn how to use a notebook command or the Jupyter Packages Manager to save time and avoid errors.
When using machine learning models like LightGBM, it is important to keep the order of columns consistent in your data, even when using Pandas DataFrames. I was surprised to find that changing the column order after training can affect prediction results, a detail even ChatGPT missed.
Explore 8 open-source AutoML frameworks that automate machine learning tasks, from algorithm selection to hyperparameter tuning. Find the perfect tool for your next project with expert insights.
Learn the top programming languages for Data Science! Discover how Python, R, and SQL help with data analysis, visualization, and machine learning. Find out which one suits your needs best!
In this post, I will explore the evolution of no-code data science, discuss its benefits and pitfalls, and introduce a solution I am building to simplify data analysis for everyone.
Learn how to install and manage Python packages using pip. This article covers setting up virtual environments, installing packages, updating them, and managing packages in Jupyter Notebooks to make your projects easier!
