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Complete Guide to Machine Learning
  • 👋Preface
  • Chapter 1: Introduction to Machine Learning
    • 1️⃣Defining Machine Learning
    • 2️⃣Types of Machine Learning
    • 3️⃣Applications of Machine Learning
    • 4️⃣Setting up the environment for Machine Learning
  • Chapter 2: Supervised Learning
    • 1️⃣Linear Regression
    • 2️⃣Logistic Regression
    • 3️⃣Decision Trees
    • 4️⃣Random Forest
    • 5️⃣Support Vector Machines
  • Chapter 3: Unsupervised Learning
    • 1️⃣K-Means Clustering
    • 2️⃣Hierarchical Clustering
    • 3️⃣Dimensionality Reduction (PCA, LLE, t-SNE)
  • Chapter 4: Reinforcement Learning
    • 1️⃣Q-Learning
    • 2️⃣SARSA
    • 3️⃣DDPG
  • Chapter 5: Deep Learning
    • 1️⃣Introduction to Neural Networks
  • 2️⃣Convolutional Neural Networks
  • 3️⃣Recurrent Neural Networks
  • 4️⃣Generative Adversarial Networks
  • Chapter 6: Transfer Learning
    • 1️⃣Understanding the concept of transfer learning
  • 2️⃣Pre-trained models and feature extraction
  • 3️⃣Fine-tuning pre-trained models
  • Chapter 7: Evaluation and Deployment
    • 1️⃣Model Evaluation Metrics
  • 2️⃣Model Selection
  • 3️⃣Deployment Strategies
  • Chapter 8: Case Studies and Applications
    • 1️⃣Image Classification
  • 2️⃣Natural Language Processing
  • 3️⃣Recommender Systems
  • Conclusion
    • 1️⃣Summary of main concepts
  • 2️⃣Additional Resources
  • 3️⃣Final project or challenge
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Preface

NextDefining Machine Learning

Last updated 2 years ago

This online book is supported by Enibly Technology (http://enibly.com). Enibly Technology is dedicated to providing cutting-edge technology solutions and services to help businesses and individuals succeed in today's digital world

Welcome to "Complete Guide to Machine Learning: Understanding the Basics with Analogies and Examples". This book is the first release, version 1, and published in January 2023.

The purpose of this book is to provide a comprehensive introduction to the field of Machine Learning for all levels of readers. The book is designed to be easy to understand and uses analogies and examples to explain the concepts. The author's goal is to provide a solid foundation in the basics of Machine Learning, so that readers can continue to explore and learn more about the field.

The book covers the main categories of Machine Learning, including supervised, unsupervised, and reinforcement learning. It also includes chapters on deep learning, evaluation, and deployment, as well as case studies and applications. The book provides hands-on examples and exercises to help readers practice and apply the concepts they learn.

This book is generated with the help of ChatGPT, a machine learning model developed by OpenAI

We are passionate about making Machine Learning accessible to everyone and is dedicated to providing a comprehensive and easy-to-understand introduction to the field.

Best Regards

Aries Harry Pratama

This online book is free and comes with all the resources and support to help you learn and practice the concepts covered in the book. We hopes that this book will serve as a valuable resource for anyone interested in learning about Machine Learning and that it will inspire you to continue exploring the field.

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Home | Enibly TechnologyMy Company