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Machine Learning is a complex subject area. Our goal in this lesson is to introduce you to some of the most common terms and ideas used in Machine Learning. I will then walk you through the different steps involved in Machine Learning (ML) and finish with a series of examples that use Machine Learning to solve real-world situations.
Machine learning is creating rapid and exciting changes across all levels of society.
- It is the engine behind the recent advancements in industries such as autonomous vehicles.
- It allows for more accurate and rapid translation of the text into hundreds of languages.
- It powers the AI assistants you might find in your home.
- It can help improve worker safety.
- It can speed up drug design
After reading this book, you will understand everything that comes into the scope of supervised Machine Learning. You will be able to not only understand nitty-gritty details of mathematics, but also explain to anyone how things work on a high level.
Machine Learning (ML) is a modern software development technique and a type of artificial intelligence (AI) that enables computers to solve problems by using examples of real-world data. It allows computers to automatically learn and improve from experience without being explicitly programmed to do so.
Machine Learning is part of the broader field of artificial intelligence. This field is concerned with the capability of machines to perform activities using human-like intelligence. Within machine learning there are several different kinds of tasks or techniques:
- Supervised learning, is a type of machine learning technique in which every training sample from the dataset has a corresponding label or output value associated with it. As a result, the algorithm learns to predict labels or output values. You can use supervised learning to do things like, predict the selling price of a house, or classify objects in an image. We will learn more about supervised learning in this lesson.
- In unsupervised learning, there are no labels for the training data. The algorithm tried to learn underlying patterns or distributions that govern the data. We will explore this in-depth in this lesson.
- In reinforcement learning, the algorithm figures out which actions to take in a situation to maximize a reward (in the form of a number) on the way to reaching a specific goal. This is a completely different approach than supervised and unsupervised learning. We will dive deep into this in the next lesson