Data Science Tutorials

Learn how the binomial distribution models multiple binary outcomes and is used in fields like finance, healthcare, and machine learning.

Discover how the Bernoulli distribution captures binary outcomes and is applied in everything from coin flips to customer predictions.

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.

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.

Explore cosine distance and cosine similarity. Discover calculations, applications, and comparisons with other metrics. Learn to implement in R and Python using numpy.

Learn how to calculate and apply Manhattan Distance with coding examples in Python and R, and explore its use in machine learning and pathfinding.

Learn the fundamentals of data version control in DVC and how to use it for large datasets alongside Git to manage data science and machine learning projects.

Time complexity is the measure of how an algorithm's runtime scales with input size, often expressed using Big-O notation, which provides an upper bound on the worst-case scenario.

Learn about memory management in Python with advanced techniques for coding memory-efficient classes. Explore practical exercises for optimal performance.

Explore Cohere Command R+ online and locally, learn about the unique features of the Cohere Python API, and build a multi-step AI agent using LangChain and Cohere.

One-hot encoding is a technique used to convert categorical data into a binary format where each category is represented by a separate column with a 1 indicating its presence and 0s for all other categories.