Machine Learning Predictions for 2020

By Srikanth
11 Min Read
Machine Learning Predictions

Machine learning (ML), an application of computer programs, makes algorithms and is capable of making decisions and generating outputs without any human involvement. 


Hailed as one of the most impactful and significant technological developments that we have seen in recent times, machine learning has already helped us perform key real-world calculations and analytics that conventional computing would take years to solve.

When it comes to the budding IT engineers and software developers, ML has been quite popular as a career choice. A lot of students have been suggested to take up a Machine Learning course and get industry-ready for the upcoming technological trend. As a matter of fact, jobs related to machine learning has seen incredible growth over the last couple of years. According to LinkedIn’s 2018 emerging jobs report, the number of machine learning jobs has seen a 12X growth in the last calendar year itself.

Basics of Machine Learning

With ML, computers are powered with the ability to perform functions without having to be explicitly programmed for it – which is the foundation of artificial intelligence. Coming to think of it, this is what makes machine learning interesting – using machine learning to make computers capable of working without human intervention, which itself is done by humans! 

Invaluable Potential of Machine Learning

Turning a distant dream like self-driving cars into a real possibility, Machine learning has already proven itself to be groundbreaking for the transportation industry. It has not just been about the emerging technologies – even the more conventional and usually traditional enterprises such as supply chain have been able to transform at break-neck speed because of machine learning. 

Not only this, but machine learning is also helping humans to lead safer lives by performing jobs that could be fatal. For instance, drones, and robots powered by ML have taken over dangerous jobs like defusing bombs and inspecting gas pipelines.

Machine Learning Predictions

Many experts believe that it is difficult to forecast the future of ML due to its rapid growth. According to Forbes, Artificial Intelligence (AI) and Machine Learning (ML) are set to create a total value of $2.6T in marketing and new-age tech by 2020, and an additional $2T in manufacturing and supply chain planning.

To meet the demands of modern-age consumers, the supply chains are getting shorter and leaner. This is being done by moving to manufacture closer to the end-user. To make these supply chains faster, ML along with neural networks is being leveraged. Experts at Gartner believe that by 2020, 95% of supply chain vendors will be relying on machine learning to streamline their operations. 

Major enterprises such as IBM and Microsoft, who had lost out to Google and Amazon in the search and mobile revolution, have geared themselves up to make a mark in AI and ML.  This competition has triggered rampant breakthroughs in the industry that are directly or indirectly related to ML. 

To make such breakthroughs possible, corporations are continuously on the look-out for talent. Many experts believe that companies are unable to find people who can meet their needs. As of July 2019, more than 50,000 jobs in AI and ML are vacant. Looking at the current scenario, these numbers will not be going down anytime soon. As a matter of fact, ML along with other components of AI such as neural networks and NLPs, will create around 2.3 million jobs by 2020. 

OpenAI, a brainchild of Elon Musk, is another key element in the AI and ML space. To accelerate the development and for making sure that the breakthroughs are used for a larger cause, OpenAI has partnered with numerous corporations and disruptive startups. 

Recently, Microsoft and OpenAI have announced a partnership to build artificial general intelligence. Their aim, as they have announced, is to build an AI that is better than humans at everything.

With so much going on in this domain, and so many proactive participants trying to create breakthroughs – the future does look promising. 

Is Pursuing a Career in Machine Learning, a good idea?

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If you are looking to pursue a career in the tech industry or looking for a change if you are already there – ML might be the right choice for you. Having so many applications across the various industries, there are a plethora of job profiles that involve ML. 

From big corporations to newly minted tech-unicorns or companies like McKinsey and Accenture who consult them, each of them is investing heavily in AI and ML. Because the industry is nascent, the companies are willing to offer lucrative compensation as well. 

Another reason why it might be a great idea to pursue a career in ML is the sheer shortage of qualified and skilled professionals. There are not enough professionals to meet the demands of companies in the AI and ML space. While some might take this as a challenge, the bright ones should consider it as an opportunity. 

Now, there is just one unanswered question, “how to land your dream job in machine learning?” The first thing you need to do is equip yourself with the much-needed skills that are essential for working with or creating new machine learning programs from scratch. Some of the skills required to land your first job in ML have been discussed in the below mentioned section.

Required Skills for Pursuing Machine Learning as a Career Option

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Proficiency in one of the key programming languages

To work as a profession in this space, the first thing to do is learn to work with one of the important programming languages used in ML. These languages include Python, R, Java, and even C++. In fact, many ML experts are often required to be proficient in more than one of these languages. 

Probability and Statistics

To build ML algorithms from scratch, you must have a good command over concepts like probability and statistics. Theories like Naive Bayes and Gaussian mixture models will be used quite commonly when you start working as an ML expert. 

Machine Learning Algorithms 

Having a crystal clear understanding of how algorithms work is important when you work as a professional. For instance, to measure the accuracy of an ML model or perform optimization, it is crucial to be able to understand how the model works in the first place. 

Staying abreast with the industry

This might not seem very important at first but can prove out to be really crucial in the long run. By keeping up with what is going around in the industry, not only can you stay updated about the various opportunities in the industry – but might also find solutions to a challenge you are facing in your project. 

The Need to take up a Machine Learning Course

For getting started with a career in ML, the first thing you need to do is equip yourself with the right knowledge and skill set.  A lot of people think that they can do it just with self-study or maybe take up a couple of projects. However, that is not the case. A lot of experts believe a good Machine Learning course that can be really helpful in starting your career in ML. 

Taking up a comprehensive and exhaustive course is important if you are serious about pursuing a career in ML. By taking up a course, you can get yourself the much-needed exposure and hands-on experience required to thrive while solving real-life problems. 

A well-structured course will give you the right direction on how to approach the various topics. Students who do not take up a course might overlook some key aspects of ML, such as, probability and even basic algorithm design in many cases. However, by engaging in a good ML course, you can tick these boxes by just being consistent.

When you take up a good course on ML, you get the chance to work on interesting problems that are similar to what you will be facing as a professional. Not only this, but a good ML course can also help you to furnish your basics and the way you work with algorithms.

So, before you embark upon this journey to be an industry-leading ML expert –  be sure to train yourself before you start training the algorithms. 

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