Year: 2025

Explore the tradeoff between AI performance and trust in high-stakes environments, with practical lessons for data and ML teams.

What is an AI Data Analyst? Learn the key meanings, real workflows, and how AI tools support modern data analysis work.

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.

Learn how to use open-source AutoML in Python with MLJAR. Train models, compare results, and generate clear reports with code examples.

Learn how Variable Inspector for JupyterLab helps track variable names, types, shapes, and values while you work in notebooks.

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!

Discover 10 machine learning algorithms, when to use each one, and practical guidance for selecting the right model for your data.

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.

Learn why LightGBM predictions can change with DataFrame column order and how to prevent errors in Python ML pipelines.

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 Python packages with pip, manage virtual environments, and handle dependencies in scripts and Jupyter notebooks.