What is Machine Learning?

According to a 2023 AI and Machine Learning Research Report from Rackspace Technology, 72% of companies surveyed said that AI and ML are part of their IT and business strategies. 69% described AI/ML as the most important technology. Now you know the reason why machine learning is important in today’s world, so let’s get started.

Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.

How Do Machines Learn?

• To keep things simple, just know that machines “learn” by finding patterns in similar data. Think of data as information you acquire from the world. The more data given to a machine, the “smarter” it gets. Machines can predict the future, if the future doesn’t look too different from the past.

Why is machine learning so widely used today?

It turns out that there are a few basic things that we could program a machine to do, such as:

• How to find the shortest path from a to b, like in your GPS.
• Perform web search.
• Recognize human speech.
• Diagnose diseases from X-rays.
• Build a self-driving car and many more.

TYPES OF MACHINE LEARNING-

• Supervised learning: The machine learns from labelled data. Normally, the data is labelled by humans.
• Unsupervised learning: The machine learns from un-labelled data. Meaning, there is no “right” answer given to the machine to learn, but the machine must hopefully find patterns from the data to come up with an answer.
• Reinforcement learning: The machine learns through a reward-based system. The central concept of reinforcement learning is based around an “agent” (computer) that is interacting with an “environment”.

SUPERVISED LEARNING-

Supervised learning can be used to solve two types of problems:

Regression or Classification.

• Regression Problem:

— If you want to predict continuous values, such as trying to predict the cost of a house or the weather outside in degrees, you will use regression. This type of problem doesn’t have a specific value constraint because the value could be any number with no limits.

• Classification Problem:

— If you’re interested in a problem like: “Am I ugly?” Then this is a classification problem because you’re trying to classify the answer into two specific categories: yes or no. This is also called a binary classification problem.

UNSUPERVISED LEARNING-

— Since there is no labeled data for machines to learn from, the goal for unsupervised machine learning is to detect patterns in the data and to group them.

Depending on what you want to group together, unsupervised learning can group data together by: clustering or association.

~ Clustering Problem:

Unsupervised learning tries to solve this problem by looking for similarities in the data. If there is a common cluster or group, the algorithm would then categorize them in a certain form.

~ Association Problem:

Unsupervised learning tries to solve this problem by trying to understand the rules and meaning behind different groups. Finding a relationship between customer purchases is a common example of an association problem. Stores may want to know what type of products were purchased together and could possibly use this information to organize the placement of these products for easier access.

REINFORCEMENT LEARNING-

— This type of machine learning requires the use of a reward/penalty system. The goal is to reward the machine when it learns correctly and to penalize the machine when it learns incorrectly.

Reinforcement Machine Learning is a subset of Artificial Intelligence. With the wide range of possible answers from the data, the process of this type of learning is an iterative step. It continuously learns.

Examples: self-driving car, how to play chess(computer).

As machine learning continues to evolve, its potential to impact our lives grows ever wider. This technology holds the promise of a future filled with innovation and possibility, whether you’re a curious observer or a potential developer, understanding the core principles of machine learning empowers you to participate in this exciting journey and shape the world of tomorrow. So, keep exploring, keep learning, and remember: the future is powered by curiosity and knowledge, just like the machines that are helping to shape it.

I hope you found this summary of machine learning to be interesting. Thank you for reading, and keep checking back for our upcoming machine learning topics.

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