Book Image

AI for Absolute Beginners: A Clear Guide to Tomorrow

By : Oliver Theobald
4 (1)
Book Image

AI for Absolute Beginners: A Clear Guide to Tomorrow

4 (1)
By: Oliver Theobald

Overview of this book

The course begins with an engaging introduction to the world of Artificial Intelligence, making it approachable for absolute beginners. We unravel the mysteries of AI's evolution, from its historical roots to the cutting-edge technologies shaping our future. By explaining complex concepts in simple terms, this course aims to illuminate the path for those curious about how AI impacts our world. The course focuses on the core components of AI, including machine learning, deep learning, and natural language processing, before advancing to more specialized topics like generative AI and computer vision. Each module is designed to build a comprehensive understanding, emphasizing why these technologies are crucial for solving real-world problems and how they're transforming industries. The course wraps up by exploring the ethical considerations and privacy concerns associated with AI, along with a visionary look at the future of work in an AI-driven world. It offers a treasure trove of further resources, ensuring learners have everything they need to continue their exploration of AI.
Table of Contents (13 chapters)
Free Chapter
1
Introduction
2
A Brief History
3
AI Building Blocks
4
The 3 Stages of AI Development
5
Machine Learning
6
Deep Learning
7
Natural Language Processing
8
Generative AI
9
Recommender Systems
10
Computer Vision
11
Privacy & Ethical Considerations
12
The Future of Work
13
Further Resources

Machine Learning

 

No matter where your exploration of AI takes you, the path will invariably intersect with the field of machine learning. Whether it's forecasting stock prices, detecting fraudulent transactions, or powering speech recognition in virtual assistants, machine learning provides the core for many AI applications.

As a subfield of AI, the power of machine learning lies in its ability to learn from data and make predictions without being directly programmed. Given a set of inputs, the model will make a prediction about what it thinks will happen next based on the patterns learned from existing data. This might involve predicting the price of a house based on features such as its location, size, year built, and sales history.

This process of learning and understanding patterns in the data is known as training, whereby an algorithm is fed data, called a training set, and studies that data in order to learn patterns. If we take the example of a house price prediction...