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The Scope & Future of Machine Learning
There is no escaping the fact that we have become increasingly reliant on the disruptive technologies emerging today, as it has deeply penetrated the way we live, function and survive in this constantly connected and integrated tech world. In this light, it would be worthy to note the fact that the future of Machine Learning is unstoppable as data becomes the new oil for fueling critical business decision-making and positively influencing consumer lives.
“Artificial Intelligence and Machine Learning have reached a critical tipping point and will increasingly augment and extend virtually every technology-enabled service, thing or application.” - Gartner
Machine Learning is more of a megatrend rather than a passing trend as it holds immense power in transforming business and society to a large extent through vast data computations, tracking of metrics and the development of more intelligent algorithms to simplify complex tasks. Machine Learning is the key driving force for data analytics.
Every major technology company today has already realized that its future and sustainability lies in the data and the real-time processing of data to identify opportunities & problems, and develop insights and solutions respectively, so data analytics will continue to be the most dominant focal points of the digital transformation of the future. In effect, the acquisition, investment, and growth of companies like Alteryx, SAS and Tibco are on the rise while on the other hand, those companies which haven’t yet tapped the potential in data analytics will fizz out and be out of business in the coming 2 to 3 years.
The value that Artificial Intelligence and Machine Learning serves to data analytics can be distilled into the value propositions of scale, speed, and convenience coupled with its ability to be easily transferable and accessible on the cloud, intuitive and much more reliable to use than traditional data analysis software and human workforce. A report published by KPMG reveals that 80% of the B2C interactions will be conducted by a bot or machine by the year 2020, which will have huge implications for organizations in terms of future staffing needs and business processes. The digitally-driven future of the businesses through data and analytics will present you with almost unlimited possibilities to create huge value in terms of return on investment and revenue generation.
How the Job Scenario is Being Affected by Machine Learning?
Machine Learning is one of the most popular career choices available today and the demand for these jobs is at an all-time high, so it is about the right time to step foot into a growing industry. According to one of Gartner’s recent reports, Artificial Intelligence will create 2.3 million Machine Learning jobs by 2020.
Following the trend, India too has recognized the power of Machine Learning and Artificial Intelligence. While organizations are using real-time data to provide actionable intelligence in the decision-making process, many new aspirants are looking at them as new career opportunities rather than applying for traditional roles in IT.
A NASSCOM study indicates that the Indian data analytics industry is expected to witness an eight-fold growth and is going to be worth $16 billion by 2025, and further adds on that the median salary of professionals in the data analytics domain will be growing year on year. For freshers, the salary range of Machine Learning jobs may vary between Rs. 6,99,807 - 8,91,326 while for more experienced professionals with sound knowledge of the Machine Learning may earn approximately a salary of Rs. 9, 00,000 or above.
Over 40 percent of all data science tasks will be automated by 2020 according to a Gartner report, thus, it becomes all the more necessary that the modern data analysts should seek to hone a few key skills to grab a good machine learning job such as quantitative analysis, computer science fundamentals & programming, data modeling and evaluation, experimental analysis, machine learning algorithms & libraries, expertise in UNIX tools, data scaling, automation tools, signal processing, distributed computing software engineering & system design to name a few.
Importance of Having a Portfolio of Machine Learning Projects
When it comes to machine learning, artificial intelligence and data science jobs, India massively lacks the talent to fill those jobs, and with more and more jobs becoming increasingly data-driven, the need for professionals to upskill themselves in order to stay relevant is of paramount importance. The companies are now on the lookout for professionals who are very well-versed in the kind of data they are dealing with and possess industry-specific experience.
This is where building a strong portfolio comes in as it helps the job applicants to present themselves in a better light, differentiate themselves amongst other potential candidates and catch the attention of the hiring managers. The portfolio will not only serve to build upon a previously earned advanced degree but will also become an indicator of the ability to communicate, a demonstration of the drive and the demonstration of specific skills.
A few pointers and properties can be kept in mind when designing your machine learning portfolio:
5 Types of Machine Learning Projects You Should Have in your Portfolio
When working on your Machine Learning portfolio, the best approach would be to choose projects that address practical issues in daily life, in other words, have a wider appeal. In addition, it should also illustrate a well-rounded set of technical skills. presentation skills, good coding practices (including documentation and a readme), version control and most importantly a good business sense.
Here are 5 types of projects that can serve as a source of inspiration for you to get started on building your Machine Learning portfolio:
1. Social Media Sentiment Analyzer
The mining of the big ocean of data available on social media / digital platforms like YouTube and Facebook is beneficial for understanding user/customer opinions, sentiments, and reaction towards a particular product or service. This project is ideal for beginners in python who can build their models to classify the data as negative or positive. To understand the functionality of this project, click here.
2. Advanced Uber Customer Support
Uber, a ride-sharing application which handles billions of rides each year, needs a strong support system to resolve customer issues quickly and efficiently. In order to improve the effectiveness of the entire process, it has incorporated a “human-in-the-loop” model architecture, known as Customer Obsession Ticket Assistant (COTA), and used deep learning techniques to discover the impact on customer satisfaction, revenue and ticket handling time. To understand more about this project, click here.
3. Music and Art Generation with Machine Intelligence:
Magenta, a ready-to-use package/library gifted by the Google Brain Team and Python-enabled, helps in generating images, songs and other similar materials using algorithms. It gets its inspiration from brain.fm. The source code is available here and the directions for the installation of Python are available here.
TensorFlow is a part of the Google Brain team in Google’s Machine Intelligence Research organization and is one of the most popular machines learning open source projects. It includes APIs for Java, has an easy to use python interface and uses data flow graphs for numerical computations. To understand the functionality of this project, click here.
Neon, the open-source machine learning project, is best for the experts or senior developers. This tool is a python-based deep learning library provided by Intel Nervana which provides high performance with its extensibility and ease-of-use features. It supports commonly used models including LSTMs and RNNs, convents and autoencoders. It is a framework for visualization and has a hardware back-end. To understand more about this project, click here.
While older foundational technologies will remain center stage for most of the digital transformation, Machine Learning will continue to inform the trajectory of successful digital transformation programs of the future.
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