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Machine Learning Explained

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Why Buy This Book?

Practical Approach: Real-world applications and hands-on examples make complex ML concepts accessible.

Problem Solving: Books that focus on solving common challenges and providing practical solutions.

Clear Explanations: Simple language and clear explanations demystify complex ML concepts for beginners.

Case Studies: Learning from real-world case studies demonstrates the practical impact of ML in various industries.

Machine Learning Explained

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Table of content

Part I. Getting Started with Machine Learning
    1. Introduction to Machine Learning
       
        1.1 What is Machine Learning?
        1.2 Why Machine Learning Matters
       
       
    2. Types of Machine Learning
       
       
        2.1 Learning with Examples (Supervised)
        2.2 Discovering Patterns (Unsupervised)
        2.3 Learning from Experience (Reinforcement)
       
Part II. Your First Steps in Machine Learning        
    3. Understanding Data
       
        3.1 What is Data in Machine Learning?
        3.2 Cleaning and Preparing Data
       
    4. Basic Algorithms
       
        4.1 Predicting with Linear Regression
        4.2 Making Decisions with Decision Trees
        4.3 Grouping with Clustering
       
Part III. Diving Deeper into Machine Learning        
    5. Improving Predictions
       
        5.1 Support Vector Machines
        5.2 Learning Nearest Neighbors
       
       
    6. The Power of Ensembles
       
        6.1 Combining Predictions
        6.2 Random Forests
       
       
    7. Introduction to Neural Networks
       
        7.1 What are Neural Networks?
        7.2 Training Neural Networks
       
Part IV. Solving Real-World Problems        
    8. Working with Text (Natural Language Processing)
       
        8.1 Understanding and Analyzing Text
        8.2 Making Sense of Sentiments
       
       
    9. Making Sense of Images (Computer Vision)
       
        9.1 Identifying Objects
        9.2 Understanding Images
       
Part V. Improving Your Machine Learning Skills        
    10. Evaluating and Tuning Models
       
        10.1 How Well is Your Model Doing?
        10.2 Making Your Model Better
       
Part VI. Looking Ahead in Machine Learning        
    11. Challenges and Considerations
       
        11.1 Being Ethical in Machine Learning
        11.2 What’s Next in Machine Learning?

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