Machine Learning

Machine learning has achieved remarkable successes in a variety of applications. These range from the postal service’s use of machine learning for reading handwritten zip codes to the development of voice recognition systems like Apple’s Siri. Other significant advances include movie recommendation systems, spam and malware detection, housing price prediction algorithms, and the ongoing development of autonomous vehicles.

The field of Artificial Intelligence (AI) has been evolving for several decades. Traditional AI systems, including some chess-playing machines, often relied on decision-making based on preset rules and knowledge representation. However, with the advent of data availability, machine learning has gained prominence. It focuses on decision-making through algorithms trained with data. In recent years, the terms AI and Machine Learning have been used interchangeably in many contexts, though they have distinct meanings. AI broadly refers to systems or applications that exhibit intelligent behavior, encompassing both rule-based approaches and machine learning. Machine Learning specifically involves learning from data to make decisions or predictions.

In this part of the book, we will delve into the concepts, ideas, and methodologies of machine learning. We will also demonstrate their practical application, using the example of recognizing handwritten digits, a classic problem that exemplifies the power and utility of machine learning techniques. Machine learning has achieved remarkable successes, ranging from the postal service’s handwritten zip code readers to voice recognition systems like Apple’s Siri. These advances also include movie recommendation systems, spam and malware detection, housing price prediction algorithms, and the development of driverless cars.