But suppose you’re planning on doing machine learning or deep learning on the data using Python and (for example) Scikit-learn, PyTorch, or TensorFlow? While it’s possible to pass data from R ...

In today’s data-driven world, mastering data labeling is not just an advantage, it’s a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create ...

What is this book about? Data preprocessing is the first step in data visualization, data analytics, and machine learning, where data is prepared for analytics functions to get the best possible ...

you will learn how to analyze and visualize data and build machine-learning models using Python, SQL, and open-source tools and libraries. This curriculum includes nine lessons and one capstone ...

In the world of data cleaning, two approaches often stand out: Python, the go-to for coding enthusiasts, and Excel’s Power Query, a code-free, user-friendly alternative. Both have their merits ...

The ORM handles all the heavy lifting for your database, and you can concentrate on how your application uses the data. This article introduces six ORMs for the Python ecosystem. All provide ...

Now imagine combining Excel’s familiar interface with the analytical power of Python—suddenly, your workflow transforms. With this integration, you can connect to live external data sources ...

There is a phenomenon in the Python programming language that affects the efficiency of data representation and memory. I call it the "invisible line." This invisible line might seem innocuous at ...

Use Python libraries—developed for Python users of all experience levels—to clean up, explore, and analyze data within the familiar, secure Excel environment. No need to install anything. Anaconda is ...