Updated on 23rd Apr, 21 6218 Views
Introduction to Machine Learning

Human race has stepped into the era of Intelligent Revolution. This wave of Intelligent Revolution is going to be powered by Artificial Intelligence (AI) and Machine Learning (ML). Automating paper-based processes are soon becoming history as Artificial Intelligence and Machine Learning have started opening doors to new opportunities enabling computers to tackle tasks that have until now been carried out only by people. Let’s start this tutorial on Machine learning definition with a brief history of how Machine Learning came to be, what it is today.

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Even though the hype or the buzz around the words ‘Machine Learning’ and ‘Artificial Intelligence’ has increased pervasively in the past few years, the first historic timeline for Artificial Intelligence came into existence during ‘World War II’ when the Computer Scientist Alan Turing created the Bombe machine to crack the impossible German force’s Enigma Code. Bombe was ‘intelligent’ and was able to learn, and eventually it cracked the code. In a way, that machine has laid the foundation of what Machine Learning and Artificial Intelligence are today.

Now that we have access to a huge amount of data, Machine Learning researchers are constantly working on making use of this data in order to push the boundaries of computer intelligence. From driving cars to translating speeches, from impacting business growth to detecting fraud attacks, Machine Learning is driving an explosion in the capabilities of Artificial Intelligence helping software make sense of the unpredictable real world. In this module, we will be discussing about Machine Learning Languages.

Following is the list of all topics that we will cover in this tutorial on Machine Learning definition, in case you need to jump to a specific one.

So, without further ado, let’s get started with Machine Learning definition.

Machine Learning Definition

Even though there are various Machine Learning examples or applications that we use in our daily lives, people still get confused about machine learning, so let’s start by looking at the Machine Learning definition.

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In layman’s terms, Machine Learning definition can be given as the ability of a machine to learn something without having to be programmed for that specific thing. It is the field of study where computers use a massive set of data and apply algorithms for ‘training’ themselves and making predictions. Training in Machine Learning entails feeding a lot of data into the algorithm and allowing the machine itself to learn more about the processed information.

Answering whether the animal in a photo is a cat or a dog, spotting obstacles in front of a self-driving car, spam mail detection, and speech recognition of a YouTube video to generate captions are just a few examples out of a plethora of predictive Machine Learning models.

Another Machine Learning definition can be given as, Machine learning is a subset of Artificial Intelligence that comprises algorithms programmed to gather information without explicit instructions at each step. Machine learning has experienced colossal success of late. We have often seen confusion around the use of the words Artificial Intelligence and Machine Learning. They are very much related and often seem to be used interchangeably, yet both are different. Confused? Let me elaborate AI vs. ML vs. DL.

Difference Between AI and Machine Learning

There seems to be a lack of a bright-line distinction between what machine Learning is and what it is not. Moreover, everyone is using the labels ‘AI’ and ‘ML’ where they do not belong and that includes using the terms interchangeably.

Difference Between Artificial Intelligence and Machine Learning - Machine Learning definition

Artificial Intelligence is not a machine or a system. It is a concept which is implemented on machines. When we talk about Artificial Intelligence, it could be making a machine move or it could be making machine detect spam mail. For all these different implementations of AI, there are different sub-fields, and one such sub-field is Machine Learning. There are applications of Artificial Intelligence which are not Machine Learning. For example, symbolic logic: rules engines, expect systems, and knowledge graphs.

Watch this Artificial Intelligence vs Machine Learning vs Deep Learning video

Machine Learning uses large sets of data and hours of training to make predictions on probable outcomes. But when Machine Learning ‘comes to life’ and moves beyond simple programming, and reflect and interact with people even in the most basic level, AI comes into play. AI is the step beyond Machine Learning, yet it needs ML to reflect and optimize decisions. AI uses what it has gained from ML to simulated intelligence, the same way a human is constantly observing their surrounding environment and making intelligent decisions. AI leads to intelligence or wisdom and its end goal is to simulate natural intelligence to solve complex problems of the world.

Now that we have gathered an idea of Machine Learning definition and difference between AI and Machine Learning, let us move ahead and see why Machine Learning is important.

Why Machine Learning?

Let us start with an instance where a machine surpasses in a strategic game by self-learning. In 2016, the strongest Go player (Go is an abstract strategy board game invented in China more than 2,500 years ago) in the world, Lee Sedol, sat down for a match against Google DeepMind’s Machine Learning program, AlphaGo. AlphaGo won the 5-day long match.

Why Machine Learning

One thing to take away from this instance is not that a machine can learn to conquer Go, but the fact that the ways in which these revolutionary advances in Machine Learning—machines’ ability to mimic a human brain—can be applied are beyond imagination.

Machine Learning has paved its way into various business industries across the world. It is all because of the incredible ability of Machine Learning to drive organizational growth, automate manual and mundane jobs, enrich customer experience, and meet business goals.

According to a BCC Research, the worldwide Machine Learning market totaled $1.4 billion in 2017. It is also predicted to reach $8.8 billion by 2022 as the Machine Learning industry is evolving quickly. ML-based startups are always hopping into space.

Moreover, according to a recent report published by Indeed, Machine Learning Engineer is the best job of 2020.

Why Machine Learning

Both Artificial Intelligence and Machine Learning is going to be imperative to the coming society. This is the right time to learn Machine Learning.

Applications of Machine Learning

As mentioned earlier, the human race has already stepped into the future world with machines. The pervasive growth of Machine Learning can be seen in almost every other field. Let me list out a few real-life applications of Machine Learning.

Moley’s Robotic Kitchen

Moley Robotic KitchenThe kitchen comes up with a pair of robotic arms, an oven, a shelf for food and utensils, and a touch screen.

Moley’s kitchen is a gift of Machine Learning: it will learn n number of recipes for you, will cook with remarkable precision, and will also clean up by itself. It sounds great, doesn’t it?

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Netflix Movie Recommendation

Netflix Movie RecommendationThe algorithm that Netflix uses to recommend movies is nothing but Machine Learning. More than 80 percent of the shows and movies are discovered through the recommendation section.

To recommend movies, it goes through threads within the content rather than relying on the genre board in order to make predictions. According to Todd Yellin, VP of Product at Netflix, Machine Learning algorithm is one of the pillars of Netflix.

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Amazon Alexa

Amazon AlexaThe latest innovations of Amazon have the brain and the voice of Alexa. Now, for those who are not aware of Alexa, Alexa is the voice-controlled Amazon ‘personal assistant’ in Amazon Echo devices. Alexa can play music, provide information, deliver news and sports scores, tell you the weather, control your smart home, and even allow prime members to order products that they’ve ordered before. Alexa is very smart. She gets updated through the Cloud and learns all the time, by herself.

But, do Alexa understand commands? How does she learn by herself? Everything is a gift of Machine Learning algorithm.

Amazon Product Recommendation

Amazon Product RecommendationI am sure that you might have noticed, while buying something online from Amazon, it recommends a set of items that are bought together or items that are often bought together, along with your ordered item.

Have you ever wondered how does Amazon recommend you that? Well again, Amazon uses Machine Learning algorithm to do so.

Google Maps

Google MapHow does Google Maps predict traffic on a particular route? How does it tell you the estimated time for a certain trip?

Google Maps anonymously sends real-time data from the Google Maps users on the same route back to Google. Google uses Machine Learning algorithm on these data to predict accurately the traffic on that route.

These are some of the Machine Learning examples that we see or use in our daily lives. Let us go ahead and discuss how we can implement Machine Learning algorithm.

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Machine Learning Languages

Now when it comes to the implementation of Machine Learning, we need to have a programming language that a computer can understand. The most common programming languages are given below.

According to the GitHub 2018 Octoverse report, the below given table ranks the most popular programming language for Machine Learning in 2018.

Rank Programming Language
1 Python
2 C++
3 JavaScript
4 Java
5 C#
6 Julia
7 Shell
8 R
9 TypeScript
10 Scala

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Machine Learning Tools

Machine Learning open-source tools are nothing but libraries used in programming languages like Python, R, C++, Java, Scala, Javascript, etc. to make the most out of Machine Learning algorithms.

  • Keras: Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow.
  • PyTorch: PyTorch is an open-source Machine Learning library for Python, based on Torch, used for applications such as natural language processing.
  • TensorFlow: Created by the Google Brain team, TensorFlow is an open-source library for numerical computation and large-scale Machine L
  • Scikit-learn: Scikit-learn, also known as Sklearn, is a Python library which has become very popular for solving science, math, and statistics problems–because of its easy-to-adopt nature and its wide range of applications in the field of Machine Learning.
  • Shogun: Shogun can be used with Java, Python, R, Ruby, and MATLAB. It offers a wide range of efficient and unified Machine Learning methods.
  • Spark MLlib: Spark MLlib is the Machine Learning library used in Apache Spark and Apache Hadoop. Although Java is the primary language for working in MLlib, Python users are also allowed to connect to MLlib through NumPy library.

What Did We Learn so Far?

This module is focused on the meaning of Machine Learning, common Machine Learning definition, the difference between AI and Machine Learning, and why Machine Learning matters. We have also highlighted different Machine Learning tools, as well as discussed some of the applications of Machine Learning. If you want to have deeper understanding of Machine Learning, refer to the Machine Learning tutorial. See you there.

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