AI Foundations of Machine Learning: Easy To Read Guide Introducing the Foundations Of Machine Learning

Price: $16.99
(as of Dec 17,2024 09:50:02 UTC – Details)

ASIN ‏ : ‎ B0CTND3XKG
Publisher ‏ : ‎ Green Mountain Computing; 1st edition (January 30, 2024)
Publication date ‏ : ‎ January 30, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 285 KB
Simultaneous device usage ‏ : ‎ Unlimited
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 133 pages

Are you interested in learning more about the foundations of machine learning but don’t know where to start? Look no further! In this easy-to-read guide, we will introduce you to the basics of machine learning and the key concepts that form the foundation of this exciting field.

What is Machine Learning?

Machine learning is a branch of artificial intelligence (AI) that focuses on developing algorithms and models that allow computers to learn from and make predictions or decisions based on data. In essence, machine learning enables computers to learn from experience and improve over time without being explicitly programmed.

Key Concepts in Machine Learning:

1. Data: Data is the cornerstone of machine learning. It can come in various forms, such as text, images, or numbers, and is used to train machine learning models. The quality and quantity of data are crucial for the success of a machine learning project.

2. Algorithms: Algorithms are the mathematical formulas or rules that machine learning models use to learn from data and make predictions. There are many different types of algorithms, each suited for different types of tasks, such as classification, regression, clustering, and reinforcement learning.

3. Training and Testing: In machine learning, models are trained on a subset of the data, known as the training set, and then evaluated on a separate subset, called the testing set. This allows us to assess how well the model generalizes to new, unseen data.

4. Features: Features are the individual data points or attributes that are used to train a machine learning model. For example, in a spam detection system, features could include the presence of certain keywords in an email.

5. Supervised vs. Unsupervised Learning: In supervised learning, the model is trained on labeled data, where each example is paired with the correct output. In unsupervised learning, the model is trained on unlabeled data and must find patterns or structure in the data on its own.

6. Overfitting and Underfitting: Overfitting occurs when a model performs well on the training data but poorly on new data, indicating that it has learned noise rather than the underlying patterns. Underfitting, on the other hand, occurs when a model is too simple to capture the complexity of the data.

These are just a few of the key concepts that form the foundation of machine learning. By understanding these basics, you’ll be well on your way to mastering this exciting and rapidly growing field. Stay tuned for more in-depth guides on machine learning techniques and applications!
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