Machine Learning with Python

Introduction of Artificial Intelligence and Machine Learning:

This bullet refers to providing an overview of the concepts of Artificial Intelligence (AI) and Machine Learning (ML) to the audience. AI refers to the simulation of human intelligence in machines to perform tasks that typically require human intelligence such as decision-making, speech recognition, and language translation. ML, on the other hand, is a subfield of AI that enables machines to automatically learn and improve from experience without being explicitly programmed.

Brief introduction to Machine Learning for AI:

This bullet specifically focuses on providing a brief overview of ML and how it is used in the broader field of AI. This may include discussing the different types of ML, such as supervised learning, unsupervised learning, and reinforcement learning.

Classification of Machine Learning:

This bullet refers to categorizing the different types of ML techniques based on how they learn from data. For example, supervised learning algorithms use labeled data to learn patterns and make predictions, while unsupervised learning algorithms discover patterns and relationships in unlabeled data.

Difference between Machine Learning and Artificial Intelligence:

This bullet is about explaining the distinctions between the broader field of AI and the subfield of ML. While ML is a component of AI, AI encompasses a broader set of technologies and techniques that are used to build intelligent systems.

Machine Learning Technique:

This bullet refers to a specific ML technique or algorithm used to build models that can make predictions or decisions based on data. Examples of ML techniques include linear regression, decision trees, and neural networks.