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Artificial Intelligence, Machine Learning, and Deep Learning
Artificial Intelligence, Machine Learning, and Deep Learning

Artificial Intelligence, Machine Learning, and Deep Learning: A Practical Guide to Advanced AI Techniques

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Profile Icon Mercury Learning and Information Profile Icon Oswald Campesato
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€44.99
eBook Jul 2024 338 pages 1st Edition
eBook
€44.99
Arrow left icon
Profile Icon Mercury Learning and Information Profile Icon Oswald Campesato
Arrow right icon
€44.99
eBook Jul 2024 338 pages 1st Edition
eBook
€44.99
eBook
€44.99

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Key benefits

  • In-depth coverage of AI, machine learning, and deep learning concepts
  • Practical examples and hands-on tutorials
  • Integration with Keras, TensorFlow, and Pandas for real-world applications

Description

This book introduces AI, then explores machine learning, deep learning, natural language processing (NLP), and reinforcement learning. Readers learn about classifiers like logistic regression, k-NN, decision trees, random forests, and SVMs. It delves into deep learning architectures such as CNNs, RNNs, LSTMs, and autoencoders, with Keras-based code samples supplementing the theory. Starting with a foundational AI overview, the course progresses into machine learning, explaining classifiers and their applications. It continues with deep learning, focusing on architectures like CNNs and RNNs. Advanced topics include LSTMs and autoencoders, essential for modern AI. The book also covers NLP and reinforcement learning, emphasizing their importance. Understanding these concepts is vital for developing advanced AI systems. This book transitions you from beginner to proficient AI practitioner, combining theoretical knowledge and practical skills. Appendices on Keras, TensorFlow 2, and Pandas enrich the learning experience. By the end, readers will understand AI principles and be ready to apply them in real-world scenarios.

Who is this book for?

Developers, data scientists, and AI enthusiasts looking to deepen their knowledge of AI, machine learning, and deep learning. Basic understanding of programming and statistics is recommended. Familiarity with Python is beneficial.

What you will learn

  • Understand AI and its components
  • Apply machine learning algorithms
  • Implement deep learning models
  • Work with RNNs and LSTMs
  • Explore NLP techniques
  • Utilize Keras, TensorFlow, and Pandas effectively

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 23, 2024
Length: 338 pages
Edition : 1st
Language : English
ISBN-13 : 9781836642763
Category :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning

Product Details

Publication date : Jul 23, 2024
Length: 338 pages
Edition : 1st
Language : English
ISBN-13 : 9781836642763
Category :

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Table of Contents

7 Chapters
Chapter 1: Introduction to AI Chevron down icon Chevron up icon
Chapter 2: Introduction to Machine Learning Chevron down icon Chevron up icon
Chapter 3: Classifiers in Machine Learning Chevron down icon Chevron up icon
Chapter 4: Deep Learning Introduction Chevron down icon Chevron up icon
Chapter 5: Deep Learning: RNNs and LSTMs Chevron down icon Chevron up icon
Chapter 6: NLP and Reinforcement Learning Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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