PYTHON Tutorial

Introduction to ML

What is Machine Learning?

Machine learning is a field of artificial intelligence that enables computers to learn from data without explicit programming. It empowers computers to make predictions, classify data, and make informed decisions.

Applications of Machine Learning:

  • Image and speech recognition
  • Prediction (e.g., weather, stock prices)
  • Fraud detection
  • Recommendation systems (e.g., Netflix, Amazon)

Types of Machine Learning:

  • Supervised learning: Involves training a model on labeled data (i.e., data with known outcomes).
  • Unsupervised learning: Trained on unlabeled data to find patterns and structures.
  • Reinforcement learning: The model learns through interactions with its environment and receives feedback on its actions.

Basic Concepts and Definitions:

  • Data: The input used to train machine learning models.
  • Model: A mathematical representation of the relationship between input and output data.
  • Features: The individual elements of the input data.
  • Label: The known outcome associated with a data point in supervised learning.
  • Training: The process of fitting a model to the data.
  • Evaluation: Assessing the performance of a trained model on a separate dataset.

Predicting House Prices:

  • Data: House features, prices, and location.
  • Label: House price.
  • Model: A regression model trained to predict the house price based on its features.
  • Evaluation: The model is evaluated on a test dataset to assess its accuracy in predicting house prices.

This example illustrates how machine learning can be applied to solve real-world problems by learning from data and making data-driven predictions.