Python has become the most popular data science and machine learning programming language. But in order to obtain effective data and results, it’s important that you have a basic understanding of how ...

Not necessarily for the data-science and machine-learning communities built around Python extensions like NumPy and SciPy, but as a general programming language.

The book leverages algorithms of machine learning in a unique way of describing real life applications. Though not mandatory, some experience with subject knowledge will fasten the learning process.

Learn about some of the best Python libraries for programming Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).

Use modern machine learning tools and python libraries. Explain how to deal with linearly-inseparable data. Compare logistic regression’s strengths and weaknesses. Explain what decision tree is & how ...

Discover five powerful Python libraries that enable data scientists to interpret and explain machine learning models effectively.

“Successful machine learning is only as good as the data available, which is why it needs new, updated data to provide the most accurate outputs or predictions for any given need,” said ...

Snowpark for Python gives data scientists a nice way to do DataFrame-style programming against the Snowflake data warehouse, including the ability to set up full-blown machine learning pipelines ...