Data Science Algorithms

(1)Types Of Machine Learning.

Based on the way and style of machine learning from the data, Machine Learning is divided into 4 types.

Supervised Machine learning, is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately.
As input data is fed into the model, the model adjusts its weights until it has been fitted appropriately.
In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly.

Unsupervised learning is a method in which a machine learns without supervision.
The machine learns by itself in an Unsupervised Learning style.
The training is provided to the machine with the data set that has not been labeled, classified, or categorized, and the algorithm needs to act on that data without supervision.
Unsupervised learning aims to restructure the input data into new features or a group of objects with similar patterns.

Semi-supervised learning is a type of machine learning that falls in between supervised and unsupervised learning.
It is a method that uses a small amount of labeled data and a large amount of unlabeled data to train a model.
Semi-supervised learning is particularly useful when there is a large amount of unlabeled data available, but it’s too expensive or difficult to label all of it.
The basic procedure involved is that first, the programmer will cluster similar data using an unsupervised learning algorithm and then use the existing labeled data to label the rest of the unlabelled data.

Text classification: In text classification, the goal is to classify a given text into one or more predefined categories. Semi-supervised learning can be used to train a text classification model using a small amount of labeled data and a large amount of unlabeled text data.
Image classification: In image classification, the goal is to classify a given image into one or more predefined categories. Semi-supervised learning can be used to train an image classification model using a small amount of labeled data and a large amount of unlabeled image data.
Anomaly detection: In anomaly detection, the goal is to detect patterns or observations that are unusual or different from normal.

Reinforcement Learning is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions.
For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or a penalty.
In Reinforcement Learning, the agent learns automatically using feedback without any labeled data, unlike supervised learning.
Since there is no labeled data, the agent is bound to learn by its experience only.

RL solves a specific type of problem where decision-making is sequential, and the goal is long-term, such as game-playing, robotics, etc.
The agent interacts with the environment and explores it by itself. The primary goal of an agent in reinforcement learning is to improve performance by getting the maximum positive rewards.
The agent learns with the process of hit and trial, and based on the experience, it learns to perform the task in a better way.
Hence, we can say that “Reinforcement learning is a type of machine learning method where an intelligent agent (computer program) interacts with the environment and learns to act within that.”
How a Robotic dog learns the movement of his arms is an example of Reinforcement learning.
It is a core part of Artificial intelligence, and all AI agent works on the concept of reinforcement learning.
Here we do not need to pre-program the agent, as it learns from its own experience without any human intervention.

Suppose there is an AI agent present within a maze environment, and his goal is to find the diamond.
The agent interacts with the environment by performing some actions, and based on those actions, the state of the agent gets changed, and it also receives a reward or penalty as feedback.
The agent continues doing these three things (take action, change state/remain in the same state, and get feedback), and by doing these actions, he learns and explores the environment.
The agent learns that what actions lead to positive feedback or rewards and what actions lead to negative feedback penalties.
As a positive reward, the agent gets a positive point, and as a penalty, it gets a negative point.