Data Science Tutorials

This complete guide teaches you the fundamentals of Python backend development. Learn basic concepts, frameworks, and best practices to start building web applications.

Boosting improves machine learning performance by sequentially correcting errors and combining weak learners into strong predictors.

Hypothesis testing is a statistical method used to evaluate claims about populations based on sample data.

Use t-tests when dealing with small samples or unknown variance, and Z-tests when samples are large and variance is known.

Discover how to use \n to insert line breaks in strings and print() statements. Use the end parameter to keep your output on a single line. Improve your file handling skills with os.linesep for managing text across different systems.

Discover how to perform Count Distinct in Tableau using the Measure Names Shelf and Calculated Fields. Explore COUNTD() and FIXED LOD expressions for more advanced calculations.

Learn the foundational principles, applications, and practical benefits of variational autoencoders and follow a step-by-step implementation with PyTorch.

Understand how to copy lists in Python. Learn how to use the copy() and list() functions. Discover the difference between shallow and deep copies.

Get started with Apple's DCLM-7B large language model and learn how to set it up, use it, and fine-tune it for specific tasks.

The if __name__ == "__main__" block in Python allows you to define code that will only run when the file is executed directly as a script, but not when it's imported as a module into another script.

Learn how to set up PyTorch’s torchchat locally with Python in this hands-on tutorial, which provides step-by-step guidance and examples.

Learn about QR decomposition, the matrix factorization technique that decomposes matrix A into the product of an orthogonal matrix Q and an upper triangular matrix R.