Machine Learning as an essential skill to survive in the 21st century

Over the past decade, the advancement in Artificial Intelligence and Machine Learning has changed the outlook of scientists and engineers on how the future will look. With increasing competition in the industrial and corporate world, companies are moving towards digital transformation and automation. Enterprises like Amazon, Netflix and Starbucks have already started depending upon Machine Learning for repetitive and mundane tasks and have seen great success. It is the future and it has become essential to have knowledge of it.

Scope of Machine Learning

In today’s world, most industries dealing with large amounts of data have recognized the importance of machine learning technology. Governments, Financial Sectors, Health Care, Hospitality and Retail Sector are employing machine learning to provide better services. The growing demand for it in every industry is compelling for employees to learn new skills. With the increase in demand, there is a rumour and confusion among the employees that the onset of machine learning will eventually lead to an increase in unemployment. This is not true. According to Gartner, Artificial Intelligence is expected to create 2.3 million jobs in the year 2020. Also, the business value created by AI will reach $3.9 trillion by year 2022. According to the Mckinsey Global Institute, machine learning has the potential to create an additional $2.6 trillion in value for marketing and sales and $2 trillion in manufacturing and supply chain planning by 2020. Over the years, the job opportunities for machine learning engineers have increased with a growth rate of 344% and the current average salary is $146,085. Top recruiters include some of the biggest enterprises in the world such as Amazon, Google, Microsoft, Apple, Wipro, Accenture, Infosys, etc.

What is Machine Learning?

Machine learning is a branch of Artificial Intelligence. It is based on the idea that machines can access data and learn from it themselves by identifying patterns. The primary aim is to develop a computer system that learns and takes actions automatically without any human intervention or assistance. Machine learning algorithms have a wide variety of applications, such as, getting recommendations on Netflix, email filtering, virtual assistants, self-driving cars, etc.

Types of Machine Learning

There are four types of widely adopted machine learning algorithms. These algorithms have their own advantages and disadvantages. These algorithms have a different approach towards the task or problem they intend to solve.

  1. Supervised Learning – Supervised Learning is one of the most basic types of machine learning. It is called supervised learning because the algorithms are trained using labelled datasets in which the desired output of the input is known. This labelled dataset can be considered as the teacher, supervising the learning process. As the correct output is known, the algorithms learn by finding the errors between the actual and correct output and then modifies the algorithm accordingly. Supervised learning is classified into Classification Problems and Regression Problems. This method is used in a majority of machine learning projects where historical data is present. For example, identifying visual identity, forecasting number of students who will be eligible for any particular test, identifying fraudulent banking transactions etc.
  2. Unsupervised Learning – Unsupervised Learning is called so because unlike supervised learning, there is no teacher or supervisor and it has only unlabelled data. This means, in unsupervised machine learning projects, there are no correct answers as we only have input data and no corresponding output data variables. The goal is to explore data and find hidden structures, patterns, properties, or relationships between data points, without any input required from human beings. Unsupervised learning is classified into Clustering Problems and Association Problems. The most popular example is K-Means Clustering Model which is used in cricket to select batsmen and bowlers. The data is combined with the information from the previous matches, and then the cluster of data is analysed to select batsmen with the most runs and bowlers with the most wickets.
  3. Semi-Supervised Learning – Semi-supervised machine learning method sits between supervised and unsupervised machine learning. It uses both labelled and unlabelled data, typically large amounts of unlabelled data and small amounts of labelled data. Many real-world problems fall into this category. This is because, it is awfully expensive and time consuming to get labelled data as it requires hiring of experts while on the other hand unlabelled data is cheap and requires less effort to acquire. Semi supervised learning is generally useful in projects where the budget is low, and the cost associated with the labelling process is high. Identifying and categorising of objects or persons from photo archives, is an example of semi supervised learning.
  4. Reinforcement Learning– Reinforcement machine learning is comparatively different form Supervised, Unsupervised and Semi-supervised learning. It takes direct inspiration from human beings’ ability to learn from mistakes and past experiences. Reinforcement algorithm is placed in an environment and in the beginning, it will make lots of mistakes. So some sort of a signal has to be provided to the algorithm, so it can understand that positive outcomes will be encouraged and negative outcomes will be discouraged. In this way we can reinforce our algorithm to give preference to positive outcomes over negative outcomes. An online chess game, finding the fastest route from one point to another, robotics, are few examples of Reinforcement Learning.

Machine learning has enabled us to analyse massive quantities of data with minimal or no human intervention. It can help in identifying profitable opportunities or dangerous risks more rapidly and accurately. As machine learning is developing and evolving on a daily basis, it requires continuous training and resources to deliver results with perfection.

Importance of Machine Learning for Everyone

Machine learning is becoming an increasingly popular area of research, because it can help us to live much happier and productive lives. It may sound complex, but unknowingly it is becoming a major part of our daily routine-searching for a nearby coffee shop on Google, checking your social media news feed, mobile banking or online shopping, machine learning is everywhere and we are becoming increasingly dependent on it. This usage is only going to increase in the future with more innovations and advancement in field of AI. As our life is starting to revolve around AI, it is a must for everyone irrespective of their age and profession to have a basic knowledge of it.

With CoLLearn, you can begin your journey in the world of AI and ML. We provide basics to advanced courses in machine learning, Natural Language Processing and Data Sciences where you will learn everything about Artificial Intelligence and Machine Learning Algorithms. Beginners or Individuals with NON-IT background can opt for our beginner’s course to build their foundation and gradually move up to the advanced level. For IT professionals, however, we have an advanced course to offer, which will make them ready for the corporate world. Feel free to contact us to know more about the courses or for any other query.

Author:

  • Mohit Goel, an economics graduate with an MBA in Business Management.

Machine Learning as an essential skill to survive in the 21st century
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