Introduction to Machine Learning and Scikit-Learn

Day 1 of 21 Days of ML with CODE WARRIORS

Table Of Contents

• Machine Learning
• Types of Machine Learning
• Application of Machine Learning
• Scikit-Learn

Focus on being productive instead of busy. - Tim Ferriss

Machine Learning

Field of study that gives computers the ability to learn without being explicitly programmed. - Arthur Samuel

Machine Learning is a type of Artificial Intelligence that allows software applications to learn from the data and become more accurate in predicting outcomes without human intervention.

It is also considered as a subset of Artificial Intelligence. It gives the new capability for computers.

Examples:

• Weather Prediction
• Classification of Mails (spam or not)
• Housing Price Prediction

Process of developing an ML model

Types of Machine Learning

Supervised Learning

This is a process of an algorithm, learning from the training dataset. This is task-driven. (Labelled data)

Types of Supervised Learning

1. Regression
2. Classification

Unsupervised Learning

This is a process where a model is trained using a piece of information that is not labeled. This is data-driven. (Unlabelled data)

Types of Unsupervised Learning

1. Clustering

Reinforcement Learning

Reinforcement Learning is learning by interacting with space or an environment. (trial n error)

Application of Machine Learning

AI is the new electricity.

-image recognition
-self-driving cars
-product recommendation
-stock market prediction
-online fraud detection

Scikit-Learn

Scikit-learn is probably the most useful library for machine learning in Python. The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction.

Components of scikit-learn:

-Supervised learning algorithms
-Cross-validation
-Unsupervised learning algorithms
-Various toy datasets
-Feature extraction