Machine Learning (ML)

Machine learning is another rapidly growing field within artificial intelligence that involves creating algorithms and models that allow computers to learn from data, recognize patterns, and make decisions without being explicitly programmed.

There are three main types of machine learning: supervised, unsupervised, and reinforcement learning.

    1. Supervised learning uses labelled data to train models for making predictions.

    2. Unsupervised learning identifies patterns and relationships in unlabelled data.

    3. Reinforcement learning trains an agent to make decisions based on feedback.

Common supervised learning algorithms include linear regression, logistic regression, and decision trees, while unsupervised learning uses algorithms such as k-means clustering, PCA, and auto encoders.

Machine learning has many applications, including speech recognition, natural language processing, and image recognition, with endless possibilities for innovation and progress.

Supervised Learning Algorithms

Supervised learning algorithms are trained on datasets where each example is paired with a target or response variable, known as the label. The goal is to learn a mapping function from input data to the corresponding output labels, enabling the model to make accurate predictions on unseen data.

Unsupervised Learning Algorithms

Unsupervised learning algorithms works with unlabelled data to discover hidden patterns or structures without predefined outputs. These are again divided into three main categories based on their purpose: Clustering, Association Rule Mining, and Dimensionality Reduction. First we'll see algorithms for Clustering, then dimensionality reduction and at last association.

Reinforcement Learning Algorithms

Reinforcement learning involves training agents to make a sequence of decisions by rewarding them for good actions and penalizing them for bad ones. Broadly categorized into Model-Based and Model-Free methods, these approaches differ in how they interact with the environment.

1. Linear Regression

Linear regression is used to predict a continuous value by finding the best-fit straight line between input (independent variable) and output (dependent variable). Minimises the difference between actual values and predicted values using a method called "least squares" to best fit the data. Predicting a person’s weight based on their height or predicting house prices based on size.

2. Logistic Regression

Logistic regression predicts probabilities and assigns data points to binary classes (e.g., spam or not spam). It uses a logistic function (S-shaped curve) to model the relationship between input features and class probabilities.

Used for classification tasks (binary or multi-class). Outputs probabilities to classify data into categories.

Example: Predicting whether a customer will buy a product online (yes/no) or diagnosing if a person has a disease (sick/not sick).

Decision Trees

A decision tree splits data into branches based on feature values, creating a tree-like structure. Each decision node represents a feature; leaf nodes provide the final prediction.  The process continues until a final prediction is made at the leaf nodes. Works for both classification and regression tasks.

Support Vector Machines (SVM)

SVMs find the best boundary (called a hyperplane) that separates data points into different classes. Uses support vectors (critical data points) to define the hyperplane. Can handle linear and non-linear problems using kernel functions focuses on maximizing the margin between classes, making it robust for high-dimensional data or complex patterns.