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Mastering Machine Learning
Are you ready to dive into the exciting world of machine learning and harness its incredible potential? Look no further! "Mastering Machine Learning" by Kris Hermans is the comprehensive guide that will equip you with the knowledge and skills needed to become a proficient machine learning practitioner.
Machine learning has revolutionized countless industries, from healthcare and finance to entertainment and transportation. It is the driving force behind intelligent systems that can make predictions, recognize patterns, and extract valuable insights from vast amounts of data. Whether you're a seasoned programmer, a data scientist, or simply someone with a passion for technology, this book will take you on a transformative journey through the core concepts and practical applications of machine learning.

Mastering Machine Learning

£7.95Price

  • 1.Introduction
    1.1.The Evolution of Machine Learning
    1.2.Applications and Impact
    1.3.Objectives of this Book

    2.Foundational Concepts
    2.1.Introduction to Artificial Intelligence
    2.2.Differentiating AI, Machine Learning, and Deep Learning
    2.3.Data Representation
    2.3.1.Types of Data
    2.3.2.Data Preprocessing
    2.4.Algorithms and Models
    2.4.1.Supervised Learning

    3.2.4.2 Unsupervised Learning
    3.1.1.Reinforcement Learning
    3.2.Evaluation Metrics
    3.3.Tools and Libraries

    4.Supervised Learning
    4.1.Introduction
    4.2.Linear Regression
    4.2.1.Understanding Linear Regression
    4.2.2.Implementation

    5.3.3 Real-world Applications
    5.1.Decision Trees
    5.2.Random Forests
    5.3.Support Vector Machines
    5.4.K-Nearest Neighbors
    5.5.Neural Networks
    5.6.Ensemble Methods
    5.7.Handling Overfitting and Underfitting

    6.Unsupervised Learning
    6.1.Introduction
    6.2.Clustering
    6.2.1.K-means
    6.2.2.Hierarchical Clustering
    6.3.Dimensionality Reduction
    6.4.Association Rules
    6.5.Principal Component Analysis
    6.6.Autoencoders

    7.Deep Learning
    7.1.Introduction
    7.2.Neural Networks Deep Dive
    7.2.1.Feedforward Neural Networks
    7.2.2.Convolutional Neural Networks
    7.2.3.Recurrent Neural Networks
    7.3.Activation Functions
    7.4.Backpropagation
    7.5.Advanced Deep Learning Techniques
    7.5.1.Generative Adversarial Networks
    7.5.2.Transfer Learning
    7.5.3.Attention Mechanisms
    7.6.Applications of Deep Learning

    8.Reinforcement Learning
    8.1.Introduction
    8.2.The Reinforcement Learning Process
    8.3.Markov Decision Processes
    8.4.Q-Learning
    8.5.Policy Gradients
    8.6.Deep Reinforcement Learning
    8.7.Applications in Robotics and Games

    9.Handling Real-World Data
    9.1.Data Cleaning
    9.2.Feature Engineering
    9.3.Feature Scaling
    9.4.Dealing with Imbalanced Data
    9.5.Data Augmentation Techniques

    10.Model Selection and Evaluation
    10.1.Train-Test Split
    10.2.Cross-Validation
    10.3.Hyperparameter Tuning
    10.4.Model Evaluation Metrics
    10.5.Model Interpretability and Explainability

    11.Special Topics
    11.1.Natural Language Processing
    11.2.Computer Vision
    11.3.Time-Series Analysis
    11.4.Recommender Systems
    11.5.Anomaly Detection

    12.Ethical Considerations
    12.1.Bias and Fairness
    12.2.Privacy and Security
    12.3.Accountability
    12.4.Transparency

    13.Future Trends and Technologies
    13.1.Quantum Machine Learning
    13.2.Edge AI
    13.3.Automated Machine Learning (AutoML)
    13.4.The Future of AI and Humanity

    14.Practical Projects and Case Studies
    14.1.Project 1: Image Classification
    14.2.Project 2: Sentiment Analysis
    14.3.Project 3: Stock Price Prediction
    14.4.Project 4: Chatbot Creation
    14.5.Case Studies in Healthcare, Finance, Marketing, and More

    15.Conclusion
    15.1.Recap
    15.2.The Future Landscape
    15.3.Final Thoughts and Encouragement

    16.Appendix
    16.1.Glossary
    16.2.Additional Resources
    16.3.About the author

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