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 Out-Of-The-Box The above-mentioned procedure seems quite complex and expensive for most organizations. Luckily, there is no need to reinvent the wheel.

Welcome! This repository encapsulates all materials that I created for the course ISE-364 / 464, titled "Introduction to Machine Learning". This includes all original lecture slides as well as ...

Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support ...

Abstract Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, ... Introduction to ...

Machine Learning: a Probabilistic Perspective, Kevin Murphy; Pattern Recognition and Machine Learning, Chris Bishop, ISBN; Machine Learning, Tom Mitchell. Information Theory, Inference, and Learning ...

Apply machine learning to NLP and develop NLP applications. Implement supervised learning for tasks like sentiment analysis and text classification. Fine-tune pre-trained LLMs using frameworks like ...

The advancement in technology in the past decade has been due to the introduction of Machine Learning. Today, Machine Learning has escalated Artificial Intelligence Revolution, be it in Fraud ...

My first ai project as part of my take on the amazing online course "Introduction to Machine Learning for Coders" taught by Jeremy Howard. I will be contributing to the Kaggle competition "Bike ...

Machine Learning is becoming the primary mechanism by which information is extracted from Big Data, and a primary pillar that Artificial Intelligence is built upon. This course is designed for Ph.D.