AI and Machine Learning – The Messi & Ronaldo of the IT World

AI and Machine Learning

By now, we know that Artificial Intelligence is the simulation of human behaviour by a machine/computer. In the 21st century, AI has reached almost every house, like Alexa, Siri, product recommendations to name a few.

According to a study by Creative Strategies, only 2% of iPhone owners have never used Siri, and only 4% of Android owners have never leveraged the power of OK Google. When it comes to usage, 51% use voice assistants in the car, 6% in public, and 1.3% at work.

While studying AI, there is one term that you are going to come across a lot, and it is Machine Learning (ML). Now, even if you are not acquainted with the latest updates in the tech world, I am pretty sure you must have heard about AI and ML creating a storm there.

But what exactly is Machine Learning? And how is it related to AI? Read this AI and Machine Learning tutorial to find out! Before anything else, let us start with understanding Machine Learning.

“AI and its offshoot, machine learning, will be a foundational tool for creating social good as well as business success.” ~Mark Hurd

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What is Machine Learning?

University of Washington defines Machine Learning as Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.”

ML expert Tom M Mitchell states that “Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience.”

In simple words, machine learning involves algorithms that allow computers to learn automatically from previous interactions with users, without being distinctly programmed with the help of neural networks. It gives computers the skill to learn from previous data without an expert having to program it. With the help of machine learning, a system takes decisions based on previous patterns.

Now you must be wondering what a neural network is. A neural network is a series of algorithms that are somewhat like a biological neural network (revolving around animal brains). These algorithms are such that they facilitate the recognition of relationships in a set of data.

Know more about the neural network in detail.

Very much technical? Let’s go through this everyday example which will make you understand ML in a better manner.

My friend’s birthday is coming up. Knowing that he is an Ironman fan, I decided to gift him something related to that. I started looking for products on Amazon and Flipkart, and soon I found a combo of a diary and a poster- both featuring Ironman. I ordered the combo and started casually surfing the internet.

While reading an article, I found that all the ads being displayed on the page were recommendations of Marvel merchandise – assorted especially for me. At the same time, I decided to open the exact same website on my mother’s phone to check if she’s getting similar ads. Upon opening the link on her phone, I could see ads of kurtas from Myntra.

This brought me to the conclusion that on the basis of the interactions of me with various websites, I was receiving product recommendations.

This means that e-commerce websites like Amazon and Flipkart are using AI to gather information about my preferences, to provide me a tailor-made experience.

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Components of Machine Learning

ML experts develop thousands of ML algorithms every year. Below mentioned are the three vital components that every algorithm has:

1. Representation

It includes the selection of a model that represents data. Decision trees, instances, set of rules, etc are some examples of this component. A decision tree is a tree-like model comprising of various decisions and their consequences. Instance-based learning occurs when the machine compares new problems with previously occurred instances.

2. Evaluation

Evaluation is that component which provides the machine to evaluate and optimize hypotheses (candidate programs). It is also known as objective, utility, or scoring function. Some examples of evaluation are accuracy, squared error, posterior probability, etc. Accuracy basically measures or evaluates classification models. Posterior probability is that probability which arises upon taking into account updated information.

3. Optimization

Optimization is the way in which hypotheses are generated. Examples of optimization include combinatorial optimization, convex optimization, and constrained optimization. Combinatorial optimization uses combinatorial techniques to solve discrete combination problems.

“A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning.” ~Dave Waters

Types of Machine Learning

1. Supervised Learning

Supervised Learning Diagram - Machine Learning Basics

In supervised learning, we have labeled dataset which means that we already know the input and their correspondings output. We train the model using an algorithm that maps the input to their outputs. Here, we try to minimize the error and then we can use new data to predict their outcomes. Supervised learning tasks include classification problems and regression problems.

2. Unsupervised Learning

The unsupervised learning technique is used when we don’t have labeled data. So the machine only knows input data and it has to act on the information without any guidance or output data. Therefore the machine is restricted to find hidden patterns and similarities within input data. Unsupervised learning is used for clustering and association problems.

unsupervised learning - Ml Algorithm

Any doubts in AI and machine learning article till now? If yes mention in the comment section.

3. Semi-Supervised Learning

Labeled data are expensive and hard to find when the problem you are working on is not so common. In semi-supervised learning, we use some amount of labeled data with unlabelled data. The models accuracy of an unlabelled data can be increased by using some of the labeled data.

4. Reinforcement Learning

In Reinforcement learning, the machine uses previous data to evolve and learn. It uses rewards and punishments to train the algorithm by taking positive rewards for good decisions and negative rewards for bad decisions. This learning doesn’t require a dataset to train. It’s a self-sustained system that learns to improve itself from the real-world environment.

reinforcement learning diagram - machine learning basics

Jump into a detailed explanation of Types of Machine Learning Algorithms.

Example of Machine Learning application in Artificial Intelligence

There is no denying that Cortana, a virtual assistant developed by Microsoft for Windows 10, is the result of progress in AI. It functions exactly like Siri or the Google Assistant. Cortana helps you find information on everything when requested using voice- even questions like What will the weather be like tomorrow? Or it even does any calculations for you. It also performs particular functions or commands other applications to perform an activity (setting an alarm, placing calls).

AI is an integral part of this assistant, as it gathers data on the basis of user interaction and then provides customized results. Cool, isn’t it?

But there’s more to Cortana than just answering questions. Microsoft claims that with each interaction, Cortana constantly keeps learning about its users, and tries to anticipate the requirements of the users. Which means that it constantly uses machine learning to intelligently operate to cater to the user’s needs.

Now in AI and machine learning article let’s discuss what is the difference between them.

Difference between AI and Machine Learning

Artificial IntelligenceMachine Learning
AI is a broad concept involving other concepts, such as machine learning, neural networks, NLP (Natural Language Processing)ML is a subset of AI in that it is a technique used to implement AI
Intelligence is the acquisition of knowledge and the ability to apply itLearning is using past instances to make future decisions
It focuses more on automating a task or a system, like carsIt focuses on gaining and applying knowledge from the external environment, like Cortana
AI enables the machines to think and perform routine jobs that humans do, such as assembly line operations in a factoryML provides solutions on the basis of a constantly evolving neural network
AI aims at increasing the probability of success, instead of accuracyML is more aimed at being accurate, instead of being successful
An AI-enabled system is programmed in a way that it simulates human behaviorML tends to create self-learning algorithms
It mimics human intelligence to solve complex problemsML learns from previously fetched data to maximize performance

AI and ML sound so different, but in reality, it can cause confusion. Machine Learning is an application of AI which implies that with sufficient progress, machines can learn and enhance with each user interaction. Artificial Intelligence is a more extensive concept involving the ability of machines to carry out a variety of tasks.

Now!! explore in detail AI vs ML vs Deep learning vs Data science

Summary

I believe it is clear now that Artificial Intelligence is like an umbrella that houses Machine Learning, along with other concepts. AI is more like a study aimed at training computers to simplify and automate tasks, whereas ML induces a machine to learn on its own.

On this note, I end my article on AI and Machine Learning. I hope you now have a better understanding of these concepts. Also, if there are any doubts you wish to ask, please put them up in the comments section below!

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