Artificial Intelligence By Example: Develop machine intelligence from scratch using real artificial intelligence use cases

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(as of Dec 27,2024 03:17:24 UTC – Details)

Publisher ‏ : ‎ Packt Publishing (May 30, 2018)
Language ‏ : ‎ English
Paperback ‏ : ‎ 490 pages
ISBN-10 ‏ : ‎ 1788990544
ISBN-13 ‏ : ‎ 978-1788990547
Item Weight ‏ : ‎ 1.84 pounds
Dimensions ‏ : ‎ 7.5 x 0.99 x 9.25 inches

Artificial Intelligence By Example: Develop machine intelligence from scratch using real artificial intelligence use cases

Artificial Intelligence (AI) is revolutionizing industries across the globe, from healthcare to finance to transportation. But how exactly can you develop machine intelligence from scratch, using real AI use cases as examples?

In this post, we will explore some common AI use cases and walk you through the steps to develop machine intelligence for these scenarios.

1. Sentiment Analysis: Sentiment analysis is the process of determining whether a piece of text is positive, negative, or neutral. This is commonly used in social media monitoring, customer feedback analysis, and brand reputation management. To develop machine intelligence for sentiment analysis, you can start by collecting a dataset of labeled texts (positive, negative, neutral) and training a machine learning model, such as a Naive Bayes classifier or a deep learning model like a recurrent neural network (RNN).

2. Image Recognition: Image recognition is the task of identifying objects, people, places, and actions in images. This is used in applications like facial recognition, autonomous vehicles, and medical image analysis. To develop machine intelligence for image recognition, you can use a pre-trained deep learning model, such as a convolutional neural network (CNN), and fine-tune it on your specific dataset.

3. Fraud Detection: Fraud detection is the process of identifying fraudulent activities in financial transactions, insurance claims, and online transactions. To develop machine intelligence for fraud detection, you can use anomaly detection algorithms, such as isolation forests or one-class support vector machines, to detect unusual patterns in your data.

4. Chatbots: Chatbots are AI-powered virtual assistants that can interact with users through natural language processing. To develop machine intelligence for chatbots, you can use a natural language processing (NLP) model, such as a transformer model like BERT or GPT-3, and train it on a dataset of conversational data.

By following these examples and leveraging real AI use cases, you can develop machine intelligence from scratch and unlock the potential of artificial intelligence in your own projects. Whether you are a beginner or an experienced AI developer, these examples can serve as a guide to building intelligent systems that can make a real impact in the world.
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