Machine Learning For Dummies: Part 1

By Aman Dasgupta

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Overview

Do you remember when Joey Tribbiani (from F.R.I.E.N.D.S, obviously!) buys the ‘V’ volume of an encyclopedia from a door-to-door salesman for $50? He did that because he always felt lost when the gang used esoteric words like “unconstitutional” and “Algonquin table.” Do you feel the same way when your friends talk about Machine Learning (ML)? Fret not because by the end of this two-part series, you will have all the technical know-how you need to talk about ML confidently (and maybe even build your very own Skynet-like AI, although we at TechDogs highly recommend not to). What’s more, we won’t even charge you $50 like the salesman did, so dive right in!

TechDogs-Let's Learn Machine Learning!-"Machine Learning For Dummies: Part 1"

What is the one thing that ties Amazon’s Alexa, Google’s self-driving car and Netflix’s personalized recommendations together? We’ll give you a minute to think. Ready?

If you said that they all make use of Machine Learning algorithms, well done. You are a true Ravenclaw! (Or did you simply read the title of the article?)

Machine Learning has touched industries spanning from Ecommerce to education to healthcare and is labeled as ‘the next big thing’ in the world of technology. So, what’s the excitement surrounding this incredible technology and how does it actually improve our day-to-day life? Is it really as complicated as it sounds? Keep on reading to find out the answers to all the questions you have ever had about Machine Learning.

Let’s Learn Machine Learning!

Before we jump into the fascinating and complex world of Machine Learning, let us start by looking at the two words “machine” and “learning.” In the simplest terms (as you might have already guessed), it is a virtual machine capable of learning new concepts without any human help. Sounds simple enough but we can assure you that there’s a lot more going on behind the scenes.

The advances in data processing using programming languages (like Python and R), along with access to vast amounts of numeric data, gave birth to the field of data science - the cradle of Machine Learning. Data scientists soon started developing computer models that could learn and adapt to newer data without human intervention (because we're lazy, right?). These computer models could improve from experience so we wouldn’t have to write new lines of code each time!

Put simply; any Machine Learning model learns and improves its operation over multiple iterations; the more iterations, the better it gets. This is the essence of Machine Learning. As impressive as that sounds, the greatest asset of Machine Learning, or ML in tech jargon, is that it reduces manual effort and eliminates any human flaws. Since a Machine Learning algorithm is based on numerous mathematical models, it can never be wrong, only less accurate. As Mr. Incredible once exclaimed, “Math is math!” and no one has the gall to argue with him, right? (except for Syndrome and The Underminer!)

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The Humble Beginnings Of Machine Learning

The term 'Machine Learning' was coined by Arthur Samuel, who created the first ML model (a program that played checkers) in 1952. His program could remember the position of all the pieces on the board and the outcome of that move. Armed with these two critical pieces of information, the program could run through multiple scenarios and predict the next best move, getting better with each round it played (after all, experience counts!). To reiterate, this was in 1952, a year before Queen Elizabeth II acceded the throne! Little did we know back then that this humble algorithm would start a technological revolution, the likes of which the world had never seen before.

We've come a long way from Arthur Samuel's checkers' program, though. One of the most astounding advances in this field was achieved recently, when NASA decided to send a spacecraft to asteroid Bennu, millions of miles away (200 million miles, to be precise), to bring a sample back to Earth.

In case you're reading this in April, this isn't an April Fool's prank! Scientists at NASA realized that it would be impossible to control the spacecraft remotely due to the 18-minute delay. The only real solution would be to 'teach' the spacecraft how to independently perform all the complex maneuvers. Machine Learning swooped to the rescue as scientists and technicians could now 'teach' the onboard computer to identify potential landing sites, avoid hazardous space debris and even abort the mission if the computer thought it was too risky to attempt a landing. At the time of writing this article, the mission was a success and the sample will land on Earth sometime in September 2023. (Better mark that date!)

Although ML has grown by leaps and bounds in the last few decades, it is still a continually developing technology and finds newer applications every day. As Microsoft founder Bill Gates once remarked, "A breakthrough in Machine Learning would be worth ten Microsoft."

Intrigued about how it works yet?

How Does Machine Learning Actually Work?

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We're sure that most of you must be wondering, how exactly does one go about teaching a machine? It sounds near impossible as we speak a completely different language than the only language (binary language) a computer can understand. It would be pretty cool if Robocop could use his Data Spike to interface with computers and upload the necessary data but alas, he's too busy catching criminals!

Here's how it's done - we let the machines teach themselves! Sounds like sci-fi, doesn't it? Instead of having a programmer spend hours writing a code, we allow the model to develop and improve independently. The computer model explores vast volumes of data and analyzes it to find patterns and trends. The more data it analyzes, the better it gets at finding such patterns.

Hence, ML has become instrumental in making predictions when humans can't manually go through the data to identify the trends. This technology has improved several areas of forecasting - from stock market analysis to weather predictions. No more carrying around an umbrella because the weather app said it might rain!

Example Time!

Imagine that we want to create a basic ML model that can count the number of cars and bikes appearing in a video. Let's call our model Chappie, cause why not? To begin, we would have to "teach" Chappie the difference between cars and bikes. The easiest way to do this is to show Chappie several images of cars and bikes - till it can accurately identify a bike from a car - be it a sports bike or a chopper.

This is the first step in Supervised Learning, a Machine Learning technique where the model is initially presented with labeled data called the training set. Once the model has analyzed the data from the training set, it can be served the data you actually want to process. To train Chappie, we would have to show him labeled images of cars and bikes. The more images it learns from, the higher its precision will be.

Another technique of Machine Learning, called Unsupervised Learning, takes only a test set as input. It identifies trends, patterns and commonalities in a dataset that has no previous training data available. Banks use such an Unsupervised Learning technique called Clustering to group customers (hence the name 'clustering'), who are more likely to enroll in a specific scheme to save on unnecessary marketing effort. Another clustering application is the spam mail filter, which analyzes a mail's content and sorts it either into your inbox or the spam folder with great accuracy. (No more emails saying that you’ve won $100,000 in the Powerball lottery!)

One more popular ML algorithm is the Reinforcement Learning model. It essentially assigns a score to every decision (think positive or negative), which helps the model reinforce certain decisions. During the initial phase, the model makes many mistakes, however, it improves through trial and error. Think of AI like the Terminator who uses extraordinarily complex ML algorithms. Save John Connor, the leader of the Human Resistance? Yes. Enable Skynet and destroy all of humanity? No. Had the Terminator used a Reinforcement Learning model, it would have been so much easier (spoiler alert!) to save John Connor from dying.

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What’s Next For Machine Learning?

While Machine Learning is still spreading its influence to more niche industries, some have already integrated it into day-to-day processes. From NASA to NASDAQ, every sector can use ML to improve business functions, process automation and forecasting. If your firm generates data - be it sales reports or your employees' coffee consumption per month - Machine Learning can help you identify key trends and make accurate predictions for the future. We wouldn't want to run out of coffee now, do we?

However, ML can do much more than just make predictions. Join us in the next article of this two-part series where we will discuss the commercial applications of Machine Learning, where it is headed next and why top researchers and scientists have called for a ban on using Machine Learning — intrigued yet?

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Frequently Asked Questions

What is Machine Learning and why is it significant?

Machine Learning, often abbreviated as ML, is a branch of artificial intelligence (AI) that enables computers to learn and improve from experience without being explicitly programmed. This technology allows machines to analyze vast amounts of data, identify patterns, and make data-driven predictions or decisions. From virtual assistants like Amazon's Alexa to self-driving cars and personalized recommendation systems like Netflix, Machine Learning has become ubiquitous across various industries. Its significance lies in its ability to automate processes, enhance efficiency, and enable predictive analytics, ultimately revolutionizing the way businesses operate and improving day-to-day life.

How does Machine Learning actually work?

Machine Learning operates by allowing computer models to learn from data and improve their performance over time. Instead of relying on explicit programming, these models explore large datasets to identify patterns and trends. There are several techniques used in Machine Learning, including Supervised Learning, Unsupervised Learning, and Reinforcement Learning. In Supervised Learning, models are trained on labeled data to make predictions, while Unsupervised Learning identifies patterns in unlabeled data. Reinforcement Learning involves reinforcing decisions based on positive or negative feedback. Through these techniques, Machine Learning enables computers to make accurate predictions, automate tasks, and optimize processes.

What are the commercial applications and future prospects of Machine Learning?

Machine Learning has a wide range of commercial applications across various industries, including finance, healthcare, e-commerce, and manufacturing. Organizations can use ML to improve customer service, optimize supply chain operations, detect fraud, and personalize user experiences. As technology continues to advance, Machine Learning is expected to play an increasingly important role in driving innovation and transformation. However, there are also concerns about ethical implications and potential risks associated with ML, leading to discussions about regulation and responsible use.

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Machine Learning (ML)Artificial intelligence Machine Learning in Autonomous vehicles Big Data Data Science Deep Learning Clustering Supervised Learning Unsupervised Learning Reinforcement Learning

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