Data Science Basic | Introduction to Machine Learning by Diljeet Singh | Learn Smarter
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Introduction to Machine Learning

Machine Learning focuses on creating algorithms that can learn from data and make predictions or decisions autonomously. It covers types of learning, including supervised and unsupervised, alongside the basic workflow for building models using tools like scikit-learn. The importance of splitting data for training and evaluation, as well as understanding key evaluation metrics, are also emphasized.

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Sections

  • 1

    What Is Machine Learning?

    Machine Learning is a subset of AI that focuses on systems that learn from data to make decisions with minimal human intervention.

    Learning Practice

  • 2

    Types Of Machine Learning

    This section covers the three main types of machine learning: supervised, unsupervised, and reinforcement learning, providing definitions and examples for each.

    Learning Practice

  • 2.1

    Supervised Learning

    Supervised learning is a type of machine learning where models are trained on labeled data to make predictions.

    Learning Practice

  • 2.2

    Unsupervised Learning

    Unsupervised Learning focuses on identifying patterns in data without predefined labels, allowing for the discovery of hidden structures.

    Learning Practice

  • 2.3

    Reinforcement Learning

    Reinforcement Learning (RL) is a type of machine learning that allows systems to learn optimal behaviors through trial-and-error interactions within an environment.

    Learning Practice

  • 3

    Basic Ml Workflow

    The basic ML workflow outlines the key steps involved in building a machine learning model, from data importation to performance evaluation.

    Learning Practice

  • 4

    Building A Simple Model (Supervised Learning)

    This section demonstrates how to build a predictive model using supervised learning techniques, focusing on the relationship between hours studied and student scores.

    Learning Practice

  • 5

    Key Ml Terminology

    This section covers critical terminology related to Machine Learning that is essential for understanding its concepts.

    Learning Practice

  • 6

    Model Evaluation Metrics

    Model evaluation metrics quantitatively measure how well a machine learning model performs based on specific tasks.

    Learning Practice

References

Chapter 9_ Introduction to Machine Learning.pdf

Class Notes

Memorization

What we have learnt

  • Machine learning involves t...
  • Supervised learning uses la...
  • scikit-learn simplifies mod...

Revision Tests