About This Book
Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that focuses on enabling machines to learn
from data and make decisions without being explicitly programmed. Instead of relying on predefined rules, ML
algorithms identify patterns in data, adapt to new information, and improve their performance over time. This
ability to learn and evolve makes ML a key technology in AI, where systems aim to perform tasks traditionally
requiring human intelligence, such as classification, prediction, and decision-making. There are three primary
types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In
supervised learning, the model is trained on labeled data to predict outcomes or classify input data.
Unsupervised learning, on the other hand, involves discovering patterns or structures in data without
predefined labels, often used in clustering or dimensionality reduction. Reinforcement learning teaches agents
to make decisions by rewarding them for actions that lead to desired outcomes, commonly used in robotics
and game-playing AI. Intelligence, in the context of machine learning, refers to the ability of a system to adapt,
learn from experience, and exhibit decision-making capabilities similar to human reasoning. The more data an
ML system processes, the better it becomes at generalizing and handling complex tasks, showcasing a form of
artificial intelligence that evolves and refines itself, making it crucial for numerous applications across
industries like healthcare, finance, and autonomous systems. Machine Learning and Intelligence explores the
core principles and algorithms behind machine learning, demonstrating how these techniques can mimic
human intelligence in solving complex problems.
Contents: 1. Introduction, 2. Automated Machine Learning, 3. Machine Learning and Knowledge Modeling,
4. Preparing for Intelligence Roles through Anticipatory Socialization, 5. AI Framework: A Visual Guide to
Machine Learning and Artificial Intelligence, 6. Online Machine Learning, 7. Machine Learning in Robotics in
Modern Applications, 8. Reinforcement Machine Learning, 9. Deep Learning Algorithms.
