What is Machine Learning and how do we use it?

Why Use Machine Learning?

Machine learning can be branched out into the following categories:

- Supervised Learning
- Unsupervised Learning

<a href="https://www.codecademy.com/articles/machine-learning-supervised-vs-unsupervised" target="_blank">Supervised Learning</a> is where the data is labeled and the program learns to predict the output from the input data. For instance, a supervised learning algorithm for credit card fraud detection would take as input a set of recorded transactions. For each transaction, the program would predict if it is fraudulent or not.

Supervised learning problems can be further grouped into regression and classification problems.

**Regression:**

In regression problems, we are trying to predict a continuous-valued output. Examples are:

- What is the housing price in Neo York?
- What is the value of cryptocurrencies?

**Classification:**

In classification problems, we are trying to predict a discrete number of values. Examples are:

- Is this a picture of a human or a picture of an AI?
- Is this email spam?

For a quick preview, we will show you an example of supervised learning.

Supervised Learning

The year is 2049... 

Neo York is overrun by bots and web crawlers. The capabilities of Machine Learning have reached new heights and the world as we know it will never be the same.

- Facial recognition technology that helps users tag and share photos of friends can now tag *future* friends; night drones are on the prowl.

- Machine learning powered self-driving cars (and flying cars) are now massively available to consumers. The steering wheel has become a thing of the past.

- Recommendation engines that suggest what VR shows to watch and what products to buy will now display a different environment for each user group.

At the dawn of a new age, you can't help but wonder, what is Machine Learning and how did it pivot our world so drastically?


Introduction to Machine Learning

*DING DING DING!* 

It was but a dream and there's still time. Understanding Machine Learning will help you change this bleak future, or be ready for it. 

As you begin your journey through our Machine Learning curriculum, you will learn the different applications of this powerful field. You will build your own models, test them, and try to improve them. You will analyze bigger, and more complex data and deliver faster, more accurate results — even on a very large scale using:

- **Supervised Learning**
- **Unsupervised Learning**

Throughout your exploration of ML, we hope you gain an understanding of how to harness predictive models to do good, and enhance human life rather than replace it!

Character waking up to study machine learning on Codecademy.

Begin Your Journey

<a href="https://www.codecademy.com/articles/machine-learning-supervised-vs-unsupervised" target="_blank">Unsupervised Learning</a> is a type of machine learning where the program learns the inherent structure of the data based on unlabeled examples.

**Clustering** is a common unsupervised machine learning approach that finds patterns and structures in unlabeled data by grouping them into clusters.

Some examples:

- Social networks clustering topics in their news feed
- Consumer sites clustering users for recommendations
- Search engines to group similar objects in one cluster

For a quick preview, we will show you an example of unsupervised learning.

Unsupervised Learning

While at IBM, [Arthur Samuel](https://en.wikipedia.org/wiki/Arthur_Samuel) developed a program that learned how to play checkers (1959). He called it:

> "The field of study that gives computers the ability to learn without being explicitly programmed."

What does this mean?

As programmers, we often approach problems in a methodical, logic-based way. We try to determine what our desired outputs should be, and then create the proper rules that will transform our inputs into those outputs.

Machine learning flips the script. We want the program itself to learn the rules that describe our data the best, by finding patterns in what we know and applying those patterns to what we don't know.

These algorithms are able to *learn*. Their performance gets better and better with each iteration, as it uncovers more hidden trends in the data.