Welcome back.
Now let's learn about machine learning.
Machine learning is a subfield of artificial intelligence, which is defined as the capability of a
machine to simulate intelligent human behavior and to perform complex tasks in a manner that is similar
to the way humans solve problems.
To understand machine learning, you need to know the algorithms that drive the opportunities of machine
learning and its limitations.
In general, machine learning algorithms are used in a wide range of applications like fraud detection,
computer vision, autonomous vehicle predictive analytics where it is not computationally feasible to
develop conventional algorithms that meet the requirements of real time and predictive nature of work.
Now, let's deep dive into the machine learning and why it's different from traditional programming.
In traditional programming, you give the computer strict rules.
If it's a circle, then it's a ball.
So you get data and program, give it to the computer, and the computer give you the output.
This is the coding.
Simple coding.
Simple program.
If it's a circle, go and label it as a ball.
While in machine learning you give the computer examples and answers.
Show many pictures labeled ball or not ball.
For example, uh, you give it the data and output to the computer, and the computer figures out the
rules itself.
So you you give the computer circles and, and, uh, and images for balls and not balls, for example,
a computer, uh, keyboard, mouse images of many objects.
And tell the computer that those are not balls.
Okay.
So the computer will recognize and figure out the rules itself.
So if you give it another object, it will detect if it is a ball or not.
So the idea here is giving the computer examples and answers.
Examples and data.
And the computer will generate the rule itself from Netflix recommendations and Amazon Alexa to self-driving
cars and your photo applications.
Image recognition.
ML is rapidly becoming part of our daily lives.
Instead of hard coding every possibility, we show the computer thousands of examples.
It looks for patterns and builds its own model to make predictions or decisions on new data it has never
seen before.
Think of it like teaching a child.
Machine learning is a branch of artificial intelligence that focuses on developing models and algorithms
that let computers learn from data without being explicitly programmed for every task.
In simple words, ML teaches the systems to think and understand like a human by learning from the data.
ML algorithms.
Machine learning algorithms are used in a wide range of applications.
We talked about fraud detections, computer vision, autonomous vehicle predictive analytics where it
is not computationally feasible to develop conventional algorithms that meet the requirements of real
time and predictive nature of work.
Also, machine learning in marketing can help you better understand customers for tailored campaigns
in sales, ML can predict customer behavior and personalize the customer experience at scale.
In the next video, we're gonna talk about the real life ML example.