Price: $0.99
(as of Dec 16,2024 15:54:07 UTC – Details)
ASIN : B0B216KMM4
Publication date : May 22, 2022
Language : English
File size : 66996 KB
Simultaneous device usage : Unlimited
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Enabled
Print length : 390 pages
Customers say
Customers find the book provides a detailed introduction to Machine Learning. They appreciate the clear explanations of terminology and subject areas, making it suitable for beginners. The writing style is described as good and simple.
AI-generated from the text of customer reviews
Machine learning is a powerful tool that has revolutionized industries such as healthcare, finance, and technology. One of the most popular and widely used techniques in machine learning is supervised learning. In this post, we will provide a gentle introduction to supervised learning, breaking down the complex concepts into easy-to-understand terms.
Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset. This means that the algorithm is given input data along with the corresponding correct output, allowing it to learn the relationship between the input and output. The goal of supervised learning is to make predictions on new, unseen data based on the patterns it has learned from the training data.
There are two main types of supervised learning: classification and regression. In classification, the algorithm predicts a discrete label or category for the input data, such as whether an email is spam or not. In regression, the algorithm predicts a continuous value for the input data, such as predicting the price of a house based on its features.
To train a supervised learning algorithm, the dataset is split into a training set and a test set. The algorithm is trained on the training set and evaluated on the test set to assess its performance. Common supervised learning algorithms include linear regression, logistic regression, support vector machines, decision trees, and neural networks.
Overall, supervised learning is a powerful tool that can be used to solve a wide range of problems, from predicting stock prices to diagnosing diseases. By understanding the basic concepts of supervised learning, you can start building your own machine learning models and unlocking the potential of this exciting field.
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