Machine Learning is the future: Here’s everything you must know

What is Machine Learning (ML)?

This is arguably the most exciting times in the world of technology, owing to deeper penetration of mobile phones, Internet and newer emerging technologies. Computers have changed the world as we see it. They are faster than us when it comes to solving mathematical problems. On the other hand, the intelligent human mind cannot really process data at the speeds which a computer can. But, computers are not ‘intelligent’ as they can’t do anything themselves. Despite the access to all the information of the world, a computer hasn’t been able to put it to use on its own. Now, what if we could teach a computer to do it? That is exactly when Machine Learning comes to the rescue. The most basic definition of Machine Learning is to teach computers to be ‘smarter’ or ‘intelligent’.

Are Artificial Intelligence (AI) and Machine Learning the same?

Leading tech giants believe that Artificial Intelligence (AI) is the next big thing, following the current mobile revolution. Now Machine Learning and Artificial Intelligence are often interchangeably used terms but they do not mean the same thing though related. Artificial Intelligence is meant to mimic human brain capabilities in machines and machine learning is an application of AI to process data and learn from it.

What is neural network and deep learning?

Frameworks have been experimented with to build algorithms that will allow machines to deal with data just like humans do. These include sampling data and making predictions of the outcome. And, artificial neural networks have consistently proven its usefulness. Open neural network libraries are available like Google TensorFlow    among others, which can be used to build models to process and predict application-specific cases. Tensorflow, an open source software library for machine intelligence was released in November 2015. It can run on GPUs, CPUs, desktop, server and mobile computing platforms.

Researchers have found that computers can be adapted to work just like the human brain. Artificial neural networks work like a real brain with connected neurons. Each neuron can process data and pass on the information to another connected neuron. Based on the data passed, the network changes and adapts. This way it can deal with the next data that passes and learn just like our brain does. And, machine learning derived from such deep networks is known as deep neural networks, which means more complex simulations of human learning.  The process in which these machines understand and communicate with us humans, owing to Machine Learning, is called natural language processing (NLP).

Types of Machine Learning algorithms

To understand the types of machine learning algorithms, you must first understand the three most basic Machine Learning tasks.

MACHINE LEARNING

(i)Supervised Learning

It is a value or string based learning wherein the system is fed with examples with specific outcomes, and the machine learns using these examples and predicts the correct response even when new examples are fed in. In such an algorithm, humans act as teachers to help the machine learn by providing samples with fixed outcome and the machine learns the patterns based on it.

(ii)Unsupervised Learning

In unsupervised learning, the machine is provided with examples with no specific outcomes or expected results. This means that the machine has to derive patterns or make correlations on its own. This method is very useful in large size random data analysis, providing us with new insights that are otherwise difficult to derive.

(iii) Reinforcement Learning

It is a decision driven algorithm where the machine is provided with examples that are similar to those fed during unsupervised learning, but the machine is given feedback (positive or negative) depending upon the solution provided by algorithms. This means the algorithm will learn just how humans learn with trial and error.

For example, when computers learn to play video games by themselves, it learns that a certain method may not succeed or a certain way is less likely to be fruitful than others.

Moving on, now let’s understand some of the most common algorithms.

(a) Regression

In predictive analysis, there are various types of regression techniques, but linear and logistic regressions are the most popular algorithms. Linear Regression has to establish a relationship between a dependent variable (Y) and one or more independent variables (X) using the best suited regression line. The equation it represents is Y=a+b*X + e. a is a constant, b is slope of the line and e will be the error. Logistic Regression is used when the dependent variable is binary in nature. This technique is widely used for classification problems and requires large sample size.

(b) Classification

While regression is to predict continuing quantity, the classification model is used to predict a label. A classification model will try to conclude using observed values. If it is given some input, the algorithm will predict the outcome (or label) values.

(c)  Clustering

Clustering means to assign a set of observations into subsets known as clusters. Clusters are formed on the basis of placing similar data points together, and the dissimilarity between each cluster must be as high as possible. Clustering is an unsupervised learning, and is commonly used when dealing with statistical data analysis.

(d) Decision Tree

It is based on the concept of entropy. This is a mechanical way to make a decision by dividing the inputs into smaller decisions and the approach is to look at the decisions and the factors that have led to that decision. So, a tree is divided in branch nodes that are alternatives and the leaf node is a decision. Tress learn and train themselves from the examples fed to them and predict circumstances not seen before.

(e) K-Means

Among the many ways to cluster data, the K-Means algorithm is the most widely used as it improves the similarity with the group while keeping as distinct as possible from other groups. It is an iterative process of clustering that will keep iterating until it finds the best solution or clusters for the problem area.

Do you need to learn programming languages?

I’m new in data science, so what is the best language for machine learning? This is often a question that crosses students’ minds.

Yes, to get started with machine learning, it is important that you know at least one programming language. Machine learning is more about maths and calculus.

Programming languages such as Python, R and SQL are the forerunners when it comes to data access. But, it’s Python that leads the pack as it is not just widely used but also most preferred by researchers. In the past two years, there deep learning Python frameworks have evolved. R is also highly prioritised. Other prominent programming languages are C, Java, C++, Scala, Julia and JavaScript.

Why Machine Learning is so important?

Machine Learning isn’t just a fad, but something that is going to the most relevant part of AI, which is slowly yet steadily being incorporated into our daily lives. Due to the immense data flowing from the increasing use of connected devices, it is now possible to leverage this data to solve real-life problems. This has paved way of Machine Learning into our everyday lives, and that is why you have been hearing about Artificial Intelligence, ML, neural networks and deep learning taking centre stage at conversations at institutes and workplaces.

Among the big announcements at the Union Budget 2018, Finance Minister Arun Jaitley said how “technologies such as Machine Learning, Artificial Intelligence and others are the technologies of the future” and a national programme will be established to conduct research and development in these areas. Companies have started looking to hire data scientist and engineers to work at business intelligence. Across industry application means the number of professionals required will be enormous. Gartner predicts that more than half of all large organizations worldwide will use advanced analytics and algorithms built on them to be more competitive by 2018.

The shift has already begun, going by the LinkedIn report, ML engineers, Data scientists and Big Data Engineers have been ranked among the top emerging jobs.

AI has been making deep roots into fields like Healthcare, Finance, Auto, Telecom, among others. Today our lives have gone online, making online customer service the most important aspect. AI helps better understand customer pain points and resolve problems. For instance, machine learning can match products exactly as per your interests, just how a shop assistant helps you buy a product at a brick and mortar store.

The growing importance of machine learning has also led to a rise in the acquisition of companies related to ML. In 2017, Facebook acquired AI startup Ozlo, Google snapped AIMatter, Microsoft bought Canadian startup Maluuba while Apple bought Turi, a Seattle-based startup specializing in machine learning and AI.

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