Learning Objectives

By the end of this chapter, you will be able to:

  • Define Data Science and Machine Learning.
  • Understand the basic concept of a Neural Network.

Key Concepts in Artificial Intelligence

Data Science

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines elements of statistics, computer science, and domain expertise.

Machine Learning (ML)

Machine Learning is a subset of AI that focuses on building systems that can learn from data, identify patterns, and make decisions with minimal human intervention. Instead of being explicitly programmed, an ML model is “trained” on a large dataset.

Neural Networks

An Artificial Neural Network (ANN) is a type of machine learning model that is inspired by the structure and function of the human brain. It consists of interconnected nodes, or “neurons,” organized in layers. Neural networks are particularly effective at finding complex patterns in data and are the foundation of deep learning.

Summary

Data Science is the broad field of extracting insights from data. A key part of data science is Machine Learning, where systems learn from data to make predictions. Neural Networks, which are modeled after the human brain, are a powerful type of machine learning model that has led to many of the recent breakthroughs in Artificial Intelligence.

Key Takeaways

  • Data Science is about extracting knowledge from data.
  • Machine Learning is a subset of AI where systems learn from data.
  • Neural Networks are a type of ML model inspired by the brain.

Discussion Questions

  1. What is the relationship between Data Science and Machine Learning?
  2. What are some of the ethical concerns related to the use of machine learning?
  3. Why are neural networks so powerful for certain types of tasks?