Updated on 05th Apr, 22 6702 Views

The following topics will be covered in this blog :

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 can be defined 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. It has experienced the 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 us elaborate on AI vs. ML vs. DL.

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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 the customer experience, and meet business goals.

According to BCC Research, the global market for Machine Learning is expected to grow from $17.1 billion in 2021 to $90.1 billion by 2026 with a compound annual growth rate (CAGR) of 39.4% for the period of 2021-2026.

Moreover, Machine Learning Engineer is the fourth-fastest growing job as per LinkedIn.

Both Artificial Intelligence and Machine Learning are going to be imperative to the forthcoming society. Hence, this is the right time to learn Machine Learning.

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What are the different types of Machine Learning?

Machine Learning algorithms run on various programming languages and techniques. However, these algorithms are trained using various methods, out of which three main types are:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Supervised Learning

Supervised Learning is the most basic type of Machine Learning, where labeled data is used for training the machine learning algorithms. A dataset is given to the ML model for understanding and solving the problem. This dataset is a smaller version of a larger dataset and conveys the basic idea of the problem to the machine learning algorithm.

So in simple terms, supervised learning means a guide assigned to the algorithm teaches the model how and what to be done in a set problem environment. The algorithm establishes a cause and effect relationship between the variables based on the given parameters. Gradually, the algorithm learns and gets a fair idea of how to solve the problem and what data points to be dealt with.

Unsupervised Learning

Unsupervised Learning is the type of Machine Learning where no human intervention is required to make the data machine-readable and train the algorithm. Also, contrary to supervised learning, unlabeled data is used in the case of unsupervised learning.

Since there is no human intervention and unlabeled data is used, the algorithm can work on a larger data set. Unlike supervised learning, unsupervised learning does not require labels to establish relationships between two data points.

The algorithms are able to establish a cause-and-effect relationship without any manual interference. One of the major advantages of unsupervised learning is that the data sets used need not be defined since unsupervised machine learning algorithms are able to identify hidden structures within the data set.

Reinforcement Learning

Reinforcement Learning is the type of Machine Learning where the algorithm works upon itself and learns from new situations by using a trial-and-error method. Whether the output is favorable or not is decided based on the output result already fed to each iteration.

In case the output return is unfavorable, the algorithm is forced to reiterate until the favorable output is returned. Now, if a favorable output is returned, the solution is reinforced by the interpreter by rewarding the algorithm. One of the classic examples of reinforcement learning is that of finding the shortest path between two given points.

How does Machine Learning work?

Machine learning works on different types of algorithms and techniques. These algorithms are created with the help of various ML programming languages. Usually, a training dataset is fed to the algorithm to create a model.

Now, whenever input is provided to the ML algorithm, it returns a result value/predictions based on the model. Now, if the prediction is accurate, it is accepted and the algorithm is deployed. But if the prediction is not accurate, the algorithm is trained repeatedly with a training dataset to arrive at an accurate prediction/result.

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Consider this example:

If you wish to predict the weather patterns in a particular area, you can feed the past weather trends and patterns to the model through the algorithm. This will be the training dataset for the algorithm. Now if the model understands perfectly, the result will be accurate.

Machine Learning Algorithms and Processes

Machine Learning algorithms are sets of instructions that the model follows to return an acceptable result or prediction. Basically, the algorithms analyze the data fed to them and establish a relationship between the variables and data points to return the result.

Over time, these algorithms learn to become more efficient and optimize the processes when new data is fed into the model. There are three main categories in which these algorithms are divided- Supervised Learning, Unsupervised Learning, and Reinforcement Learning. These have already been discussed in the above sections.

ML Programming Languages

Now, when it comes to the implementation of Machine Learning, it is important to have a knowledge of programming languages that a computer can understand. The most common programming languages used in Machine Learning are given below.

According to the GitHub 2021report, the below-given table ranks as the most popular programming language for Machine Learning in 2021:

RankProgramming Language
1JavaScript
2Python
3Java
4Go
5TypeScript
6C++
7Ruby
8PHP
9C#
10C

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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 Learning.
  • Scikit-learn: Scikit-learn, also known as Sklearn, is a Python library that 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 the NumPy library.

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

Artificial Intelligence is not a machine or a system. It is a concept that is implemented on machines. When we talk about Artificial Intelligence, it could be making a machine move or it could be making a 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 that are not related to Machine Learning. For example, symbolic logic: rules engines, expert systems, and knowledge graphs.

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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 reflects and interacts with people even at 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 simulate 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 What Machine Learning is and the difference between AI and Machine Learning, let us move ahead and see why Machine Learning is important.

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.

Fraud Detection

Fraud detection refers to the act of illicitly drawing out money from people by deceiving them. Machine Learning can go a long way in decreasing instances of fraud detection and save many individuals and organizations from losing their money.

For example- by feeding an algorithm into the model, spam emails can be easily detected. Also, the right machine learning models can easily detect fraudulent transactions or suspicious online banking activities.

In fact, fraud detection ML algorithms are nowadays being considered as much more effective than humans.

Moley’s Robotic Kitchen

Moley Robotic Kitchen

Machine Learning can do wonders in the food and beverage industry too. Consider this example- The 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?

Netflix Movie Recommendation

Netflix Movie Recommendation

The 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, the Machine Learning algorithm is one of the pillars of Netflix.

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Alexa

Amazon Alexa

The latest innovations of Amazon have the brain and the voice of Alexa. Now, for those who are not aware of Alexa, it 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 smart and gets updated through the Cloud and learns all the time, by itself.

But, does Alexa understand commands? How does it learn by itself? Everything is a gift of the Machine Learning algorithm.

Amazon Product Recommendation

Amazon Product Recommendation

We are 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 Amazon makes those recommendations? Well again, Amazon uses the Machine Learning algorithm to do so.

Google Maps

Google Map

How 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 the 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 a Machine Learning algorithm.

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Scope of Machine Learning

The scope of Machine Learning covers varied industries and sectors. It is expanding across all fields such as Banking and Finance, Information Technology, Media & Entertainment, Gaming, and the Automotive industry. As the Machine Learning scope is very high, there are some areas where researchers are working toward revolutionizing the world for the future.

The scope of Machine Learning in India, as well as in other parts of the world, is high in comparison to other career fields when it comes to job opportunities.

According to Gartner, there will be 2.3 million jobs in the field of Artificial Intelligence and Machine Learning by 2022. Also, the salary of a Machine Learning Engineer is much higher than the salaries offered to other job profiles. According to Forbes, the average salary of an ML Engineer in the United States is US$99,007.

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Prerequisites for Machine Learning

Prerequisites to building a career in Machine Learning include knowledge of the following:

  • Statistics- Knowledge of statistical tools and techniques is a basic requirement to understand Machine Learning. You should be well trained in using various types of statistics such as descriptive statistics and inferential statistics to extract useful information from raw data.
  • Probability- Machine Learning is built on probability. The very possibility of occurrence of an event is known as probability.
  • Programming languages- It is very important that an ML engineer knows which machine-readable programming language to be used.
  • Calculus- The working of Machine Learning algorithms depends on how Calculus and related concepts such as Integration and Differentiation are used. Hence, it is very important that you understand and are well acquainted with Calculus.
  • Linear Algebra- Vectors, Matrices, and Linear Transformations form an important part of Linear Algebra and play an important role in dataset operations.

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Conclusion

This module focuses on the meaning of Machine Learning, common Machine Learning definitions, the difference between AI and Machine Learning, why Machine Learning matters, prerequisites, and types of machine learning. We have also highlighted different Machine Learning tools, as well as discussed some of the applications of Machine Learning. If you want to have a deeper understanding of Machine Learning, refer to the Machine Learning tutorial. See you there!

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