Welcome back.
In general, machine learning algorithms are used in a wide range of applications like fraud detection,
computer vision, autonomous vehicle predictive analytics where it's not computationally feasible to
develop conventional algorithms that meet the requirements of real time and predictive nature of work
in the field of machine learning.
There are multiple algorithms that help us reach descriptive, predictive, and prescriptive result
sets based on parameters defined.
Descriptive explaining with the help of data.
Predictive predicting with the help of data.
Prescriptive suggesting with the help of data.
We understand the functions of machine learning from the information and in the previous slides.
But the art of making those functions work is in the way in which the algorithms are designed and used
to execute those functions.
Let's start with the first type, which is the supervised Learning.
Supervised learning is a type of machine learning where a model learns from the labeled data, meaning
every input has a corresponding correct output.
The model makes predictions and compares them with the true outputs, adjusting itself to reduce errors
and improve accuracy over time.
The goal is to make accurate predictions on new, unseen data.
For example, a model trained on images of handwritten digits can recognize new digits in digits it
has never seen before.
The second type is semi supervised learning.
Semi-supervised learning system is like a supervised learning system, but it uses both labeled and
unlabeled data in addition to supervised data.
The term labeled data refers to information that has a meaningful tag that allows the algorithm to understand
the data, whereas unlabeled data doesn't have such a tag, which means that ML algorithms can be told
to label data that has not been labeled.
Another type is the unsupervised learning.
Unsupervised learning is a type of machine learning that analyzes and models data without labeled responses
or predefined categories.
Unlike supervised learning, where the algorithms and the algorithm learns from input output pairs,
unsupervised learning algorithms work slow solely with input data and aim to discover hidden patterns,
structures, or relationships within the data set independently, without any human intervention or
prior knowledge of that data's meaning.
Another type is reinforcement learning.
Reinforcement learning RL is a branch of machine learning that focuses on how agents can learn to make
decisions through trial and error to maximize cumulative rewards.
RL allows machines to learn by interacting with an environment and receiving feedback based on their
actions.
This feedback comes into the form of rewards or penalties.
Don't worry.
We're going to learn about those in the next videos.
And as a quick recap, we use the supervised learning for classification and regression, classification,
fraud detection, email spam detection, diagnostics, image classification and in regression, risk
assessment and score predictions.
The unsupervised learning used for reduction, text mining, data visualization, face detection and
voice detection.
Regression.
City planning and targeted marketing.
Reinforcement learning.
Finances.
Manufacturing, stock management, autonomous cars, and others.
In the next video, we're gonna deep dive into those types of ML algorithms, and we're going to learn
about their nature and how it how they work.