Overview of Machine Learning Introduction

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1. Machine Learning Basics </aside>

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1. Different Applications of Machine Learning </aside>

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1. Types of Learning in Machine Learning </aside>

Machine Learning Basics

1. Introduction to Machine Learning Basics
   1. Machine Learning (ML) is a subset of artificial intelligence (AI) that focuses on building systems that can learn from data and improve their performance over time without being explicitly programmed. The core idea is to enable machines to learn from experience, much like humans do. This is achieved through the use of algorithms that iteratively learn from data, identify patterns, and make decisions with minimal human intervention.
   2. Key Concepts in Machine Learning:
      1. Data: The foundation of any ML model. Data can be structured (e.g., databases) or unstructured (e.g., images, text).
      2. Model: A mathematical representation of a real-world process. The model is trained using data to make predictions or decisions.
      3. Training: The process of teaching a model to make predictions by feeding it data and allowing it to adjust its parameters.
      4. Inference: The phase where the trained model is used to make predictions on new, unseen data.
      5. Features: Individual measurable properties or characteristics of the data that are used as input to the model.
      6. Labels: The output or target variable that the model is trying to predict in supervised learning.