Learn Artificial Intelligence From Scratch: A Beginner’s Guide To Understanding & Building AI Systems: A Comprehensive Introduction To The Field Of AI, Covering Fundamental Concepts & Key Techniques

Publishing, VectorMind

ISBN 13: 9798290047089

Verlag: Independently published, 2025

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Bibliografische Details

• Titel: Learn Artificial Intelligence From Scratch: A Beginner?s Guide To Understanding & Building AI Systems: A Comprehensive Introduction To The Field Of AI, Covering Fundamental Concepts & Key Techniques
• Verlag: Independently published
• Erscheinungsjahr: 2025
• Sprache: Englisch
• ISBN-13: 9798290047089
• Einband: Softcover
• Zustand: New

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Inhaltsangabe

You'll Learn:

• Understand the Core Concepts of AI: Grasp the fundamental definitions of Artificial Intelligence, Machine Learning, and Deep Learning, and understand the historical context and different types of AI (ANI, AGI, ASI).

• Set Up Your AI Development Environment: Learn to install and configure essential tools like Python, Anaconda, and key libraries such as NumPy, Pandas, and Matplotlib to create your personal "AI workbench" for hands-on practice.

• Master the Role of Data in AI: Discover why data is the foundation of AI systems, learn to handle different data types, and gain practical skills in loading, exploring, and visualizing datasets using the Pandas library.

• Distinguish Different AI Learning Paradigms: Differentiate between rule-based systems and machine learning, and gain a clear understanding of the three core machine learning paradigms: supervised, unsupervised, and reinforcement learning.

• Build and Evaluate Predictive Models: Implement and train classic supervised learning models like Linear Regression for predicting numbers and Logistic Regression and Decision Trees for classification tasks using the Scikit-learn library.

• Discover Patterns in Unlabeled Data: Learn the principles of unsupervised learning and apply the K-Means clustering algorithm to group similar data points and find hidden structures within datasets.

• Assess and Improve Model Performance: Go beyond basic accuracy by learning about crucial evaluation metrics like the Confusion Matrix, Precision, Recall, and F1-Score. You'll also learn techniques like cross-validation and basic hyperparameter tuning to build more robust models.

• Build and Train Simple Neural Networks: Gain an intuitive understanding of how neural networks mimic the brain, learning about perceptrons, layers, and neurons. You'll then use a powerful library like Keras to build and train your first neural network classifier.

• Explore Key AI Applications: Get a foundational overview of specialized AI fields like Computer Vision (enabling machines to "see") and Natural Language Processing (enabling machines to understand language), including a brief introduction to Convolutional Neural Networks (CNNs).

• Understand the Ethical Implications of AI: Critically examine important considerations like algorithmic bias, privacy, and fairness in AI systems, and explore the future societal impact and career opportunities in the field.