Machine Learning (ML)

In this article we will talk about MACHINE LEARNING (ML)

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computer to "self learn" from training data and improve over time ,without being explicity programmed . Machine Learning algorithm are able to detect pattern in data and learn from them ,in order to make their own prediction.

What is machine learning ?

Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.

Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.”

The definition holds true, according to a lecturer at MIT Sloan and head of machine learning at Kensho, which specializes in artificial intelligence for the finance and U.S. intelligence communities. He compared the traditional way of programming computers, or “software 1.0,” to baking, where a recipe calls for precise amounts of ingredients and tells the baker to mix for an exact amount of time. Traditional programming similarly requires creating detailed instructions for the computer to follow.

But in some cases, writing a program for the machine to follow is time-consuming or impossible, such as training a computer to recognize pictures of different people. While humans can do this task easily, it’s difficult to tell a computer how to do it. Machine learning takes the approach of letting computers learn to program themselves through experience.

Machine learning starts with data — numbers, photos, or text, like bank transactions, pictures of people or even bakery items, repair records, time series data from sensors, or sales reports. The data is gathered and prepared to be used as training data, or the information the machine learning model will be trained on. The more data, the better the program.

From there, programmers choose a machine learning model to use, supply the data, and let the computer model train itself to find patterns or make predictions. Over time the human programmer can also tweak the model, including changing its parameters, to help push it toward more accurate results. (Research scientist Janelle Shane’s website AI Weirdness is an entertaining look at how machine learning algorithms learn and how they can get things wrong — as happened when an algorithm tried to generate recipes and created Chocolate Chicken Chicken Cake.)

“The function of a machine learning system can be descriptive, meaning that the system uses the data to explain what happened; predictive, meaning the system uses the data to predict what will happen; or prescriptive, meaning the system will use the data to make suggestions about what action to take,” the researchers wrote.  

There are three subcategories of machine learning:

Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. For example, an algorithm would be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own. Supervised machine learning is the most common type used today.

In unsupervised machine learning, a program looks for patterns in unlabeled data. Unsupervised machine learning can find patterns or trends that people aren’t explicitly looking for. For example, an unsupervised machine learning program could look through online sales data and identify different types of clients making purchases.

Reinforcement machine learning trains machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions, which helps it learn over time what actions it should take.

How Machine Learning Works ?

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.

Machine Learning Algorithms and Processes

Machine Learning Algorithm 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

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-

• 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.

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.

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 a 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

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

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.

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

Advantages and Disadvantages of Machine Learning

• Easily identifies trends and patterns

Machine Learning can review large volumes of data and discover specific trends and patterns that would not be apparent to humans. For instance, for e-commerce websites like Amazon and Flipkart, it serves to understand the browsing behaviors and purchase histories of its users to help cater to the right products, deals, and reminders relevant to them. It uses the results to reveal relevant advertisements to them.

• Continuous Improvement

We are continuously generating new data and when we provide this data to the Machine Learning model which helps it to upgrade with time and increase its performance and accuracy. We can say it is like gaining experience as they keep improving in accuracy and efficiency. This lets them make better decisions.

• Handling multidimensional and multi-variety data

Machine Learning algorithms are good at handling data that are multidimensional and multi-variety, and they can do this in dynamic or uncertain environments.

•  Wide Applications

You could be an e-tailer or a healthcare provider and make Machine Learning work for you. Where it does apply, it holds the capability to help deliver a much more personal experience to customers while also targeting the right customers.

Disadvantages of Machine Learning

• Data Acquisition

Machine Learning requires a massive amount of data sets to train on, and these should be inclusive/unbiased, and of good quality. There can also be times where we must wait for new data to be generated.

• Time and Resources

Machine Learning needs enough time to let the algorithms learn and develop enough to fulfill their purpose with a considerable amount of accuracy and relevancy. It also needs massive resources to function. This can mean additional requirements of computer power for you.

• Interpretation of Results

Another major challenge is the ability to accurately interpret results generated by the algorithms. You must also carefully choose the algorithms for your purpose. Sometimes, based on some analysis you might select an algorithm but it is not necessary that this model is best for the problem.

• High error-susceptibility

Machine Learning is autonomous but highly susceptible to errors. Suppose you train an algorithm with data sets small enough to not be inclusive. You end up with biased predictions coming from a biased training set. This leads to irrelevant advertisements being displayed to customers. In the case of Machine Learning, such blunders can set off a chain of errors that can go undetected for long periods of time. And when they do get noticed, it takes quite some time to recognize the source of the issue, and even longer to correct it.

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 2023. 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.

Prerequisites for Machine Learning

• 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 the 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.

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.