Machine-Learning

This document introduces the fundamentals of machine learning, its types, and how ML models differ from traditional algorithms. It explains supervised unsupervised, and reinforcement learning with real-world examples and use cases.

This document introduces key terms and concepts in artificial intelligence including machine learning, deep learning, and neural networks. It explains AI categories and highlights how these technologies enable intelligent systems and real-world applications.

This document explores practical use cases of machine learning in daily life including NLP, mobile apps, finance, cybersecurity, healthcare, and marketing. It highlights real-world applications and the impact of ML across industries.

This module introduces the concept of artificial intelligence, its history types, and foundational principles, including the evolution from early computing to modern AI applications.

Methods for retrieving data from SQL and NoSQL databases, APIs, and Cloud data sources with practical considerations and Python code examples

Methods for retrieving data from various sources including CSV and JSON files with practical considerations using Python and Pandas

Overview of Machine Learning and Deep Learning, including their definitions differences, applications, and the importance of features and targets in ML projects

An overview of Artificial Intelligence and Machine Learning, their history applications, and impact.