Machine Learning for Developers

Machine Learning for Developers is a practical discipline focused on empowering software engineers to build and integrate intelligent features into applications, often without needing to invent new algorithms from scratch. It emphasizes the application of existing libraries and frameworks, such as TensorFlow, PyTorch, and scikit-learn, as well as the use of cloud-based AI services and APIs to train, deploy, and maintain models. The core objective is to bridge the gap between theoretical data science and applied software engineering, enabling developers to solve real-world problems by making their software learn from data to make predictions or decisions within a production environment.

1. Introduction to Machine Learning for Developers
   1. Defining Machine Learning
      1. Historical Context and Evolution
      2. Core Principles and Philosophy
      3. Data-Driven Decision Making
      4. Pattern Recognition Fundamentals
   2. Learning Paradigms
      1. Supervised Learning
      2. Unsupervised Learning
      3. Semi-Supervised Learning
      4. Reinforcement Learning
      5. Self-Supervised Learning
   3. Machine Learning vs Traditional Programming
      1. Rule-Based Systems
      2. Data-Driven Approaches
      3. Decision Criteria for ML Adoption
      4. Hybrid Approaches
   4. The Developer's Perspective
      1. Integration Challenges
      2. Collaboration with Data Scientists
      3. Technical Responsibilities
      4. Project Lifecycle Participation
   5. Fundamental Terminology
      1. Models and Algorithms
      2. Features and Feature Spaces
      3. Labels and Targets
      4. Training and Inference
      5. Parameters and Hyperparameters
      6. Datasets and Data Splits
      7. Overfitting and Underfitting
      8. Bias and Variance