🎰
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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  1. Chapter 7: Evaluation and Deployment

Model Evaluation Metrics

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