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 ...

Machine learning is a branch of artificial intelligence that deals with predictive modeling and analysis based on historical data. In simple terms, it uses complex mathematical algorithms to ...

This course aims to provide an introduction to the quantitative analysis of data, blending classical statistical methods with recent advances in computational and machine learning. You will cover key ...

MLDS-413 teaches data engineering skills that are essential for “data science” practitioners, in particular, how to model, organize, store, and analyze data in modern relational database management ...

See How It Works for details. We are excited to inform you the current Machine Learning: Theory and Hands-On Practice with Python Specialization (taught by Professor Geena Kim) is being retired and ...

machine learning methods, and software that provide new ways to build models, diagnose problems, and make informed decisions An introduction to new sensor technologies, including spectral imaging and ...

The book “Introduction to Machine Learning with Python“ has made explanation ... Currently, he is working in the field of Data Networking, MPLS Technology. He has done BTech in Computer ...

Do you need to make sure that the machine learning models you use are unbiased, even if your data is biased? If you answered "yes" to either of these questions, this article is for you.

Today’s feature focuses on Stanislaw Zak, professor of electrical and computer engineering, and his book, “An Introduction to Optimization: With Applications to Machine Learning, 5th Edition.” A new ...