While human being is the creator of technology, today almost no one can imagine their life without technology. That’s how profoundly technology is controlling its creator now. Mankind is literally at the cusp of rapid technological advancement.
Around 73% of all cubicle-related jobs will be automated by 2030 which is equivalent to over 20 million jobs. (infographic)
On the brighter side, this is also true that only humans hold the power of thinking and creating. Keeping pace with the technological advancements and getting versed in trending technology shall unequivocally let the working class stay valuable in the global technology job market. Tech enthusiasts today, to confront the changing technology ecosystem are increasingly showing interest in taking up top IT Courses and get trained in top technical skills. And when we talk about tech skills, mentioning Machine learning certainly becomes imperative.
What is Machine Learning?
Google the meaning of Machine Learning, and you will end up finding countless definitions in technical language. In layman’s term, Machine Learning which was coined by Arthur Samuel back in 1959, is a subset of Artificial Intelligence that deals with allowing the machine to comprehend and learn huge data themselves through examples and experiences. In ML, extraction of patterns from large data sets is made quite easy, thus enabling develop significant logic based on the given data.
“Machine intelligence is the last invention that humanity will ever need to make.”
-Nick Bostrom, Professor, University of Oxford
Machine Learning Applications:
The application of Machine Learning is transforming almost every industry today where businesses are using ML for capitalizing its data to gain meaningful insights, intensifying sales, improving the relationship with customers, just to name a few.
Machine Learning in Travel Industry:
Travel is one of the industries which require maintaining a high customer satisfaction rate for better business outcomes. To improvise the overall customer experience, over the years travel industry has been applying machine learning in the form of developing smarter intelligent travel assistants, AI-based personalization to boost customer loyalty programs, prepare tailored offers for customers and many more. ML is also beneficial for travelers, where ML assists the travelers immensely in providing customized suggestions after analyzing the preference and needs of the customers rigorously.
To summarize, Machine Learning is exceptionally enabling travel marketing analytics to become way more predictive. The impact of ML is such that, around 97% of the industry experts admitted they believe the digital marketing future will involve human marketers working with machine learning-powered automation.
Machine Learning in Logistics Industry
In the time when most of the useful activities are being performed ‘online’- from shopping clothes to vegetables and likewise, logistics plays a great role in delivering and picking goods from one place to another. Thus to address the fast-evolving demands of the customers, the logistic industry is putting ample effort into adapting to technologies like ML and AI for enriching real-time decision making on issues like inventories, carriers, availability and costs , to keeping track of warehouse locations.
Forbes insights research found out 65% of senior transportation-focused executives agree that the evolution of AI, ML and, related technologies can bring significant revolution in the logistics, supply chain and transportation processes.
Machine Learning in Retail Industry
Retail businesses across the world are leveraging the potentialities of Machine Learning and it is poised to disrupt the retail industry in many ways. One primary zone where ML is contributing is in the materializing theory of smart automation in inventory management and supply chain.
For instance, a retail king like Amazon has access to a myriad of customer data and applying ML to that data, they can predict demand for particular products, provide customized recommendations and so on. Owing to that, it came to light — Amazon’s 35 percent of sales come from machine learning-driven product recommendations that are tailored to each shopper.
IBM Machine Learning
IBM or International Business Machines, an American IT MNC, is a forerunner in the world of technology, which also is widely known for its offerings in the Analytics area. Watson Studio — IBM’s enterprise platform for harnessing the potentialities of AI, enables enterprises to scale and simplify data science to accelerate their business results. Watson Studio owing to IBM Watson Machine Learning has gained a noticeable recognition worldwide as a top-notch data science and machine learning platform. IBM Watson Machine Learning allows building analytical models and neural networks by offering a range of tools and services enabling to build, train, and deploy ML models.
Given the immense success of IBM Watson Machine Learning in the IT market, there is a soaring demand for IBM trained professionals. To gain a successful career in the arena of analytics, there are a number of training programs designed exclusively for tech enthusiasts by IBM which one can obtain by enrolling in an authorized training partner.
Global Knowledge Technologies being one of the 5 Global training providers of IBM delivers a wide range of IBM training programs, letting the professionals earn essential skill sets and acclaimed Certifications that help in heightening the value of their resumes.
Machine Learning Market
By now, you might have already understood the extensive utilization of Machine Learning by varied industries which raises the value and growth of ML both globally and nationally.
- 82% of enterprises that are embracing ML and AI have gained a financial return on their investments according to McKinsey.
- AI and ML have the potential to generate an additional $2.6T in value by 2020 in Marketing and Sales and up to $2T in manufacturing and supply chain planning.
- Zion Market Research’s 2018 July report revealed that the ML global market was valued at USD 1.58 billion in 2017 and is all set to account for USD 20.83 billion in spending by 2024 with a 44.06% compound annual growth rate.
- Kris Gopalakrishnan, Co-founder of Axilor Ventures and former Executive Vice Chairman of Infosys, recently claimed that emerging technologies such as AI and ML would impact the Indian economy like the $177 IT services sector.
Machine Learning Career Highlights
Have been appearing in LinkedIn’s top emerging jobs list every year, Machine Learning is a career option that hasturned into avital topic of discussion among educators, professionals, and students everywhere. Comprehending the increasing need for ML skilled professionals, it will not be wrong to say that Machine Learning is undeniably going to shape the employment scenario globally.
- As of November 2019 Glassdoor survey on salaries, the average salary for a Machine Learning Scientist is INR 42,16,786 in India.
- Python has been found as the most popular language for data science practitioners in the Analytics India Magazine 2019 survey, where 75 percent of respondents said that Python is a demanding career path among job seekers.
- The adoption of ML by different industries has been so high that in 2018, ~0.18 Mn to ~0.2 Mn new jobs were created for the professionals who are equipped with skills and expertise in machine learning applications.
- Indeed reported that Machine Learning and Deep learning Engineers were found to be the most popular jobs posted between 2018 and 2019.
- Machine Learning Engineer, Machine Learning Developer, Machine Learning Expert, Researcher Machine Learning are some of the most-sought after job designations in the field of ML.
Technology is a field where change is the only constant. Get yourself prepared for upcoming challenges and opportunities coming your way in the professional front with adding Machine Learning Certifications, which will make sure that you are in tune with the future demand of the job market.
In today’s world, Machine Learning has become the most influential and powerful technology.
Machine learning is the process of giving training to computer systems about how to make accurate predictions when fed data.
Software engineering combined human-created rules with data to create answers to a problem which are Traditionally but machine learning uses data and answers to discover the rules behind a problem.
When we google something or listen to a song, watch video on youtube, take a photo.
Eliminating Manual Tasks : Duplication of data is the biggest dispute faced by the business today.
If any organization is using machine learning for their business than its simply end entry tasks and its incomparable accuracy saves both money & time.
Artificial intelligence matches human performance by learning, deriving conclusions on its own, and executing a clearly defined task.
Machine Learning is an artificial intelligence technology that uses data to automatically discover patterns and trends, resulting in deriving more accurate predictions of future events.Many organizations are leveraging ML and AI to advance their product development, sales, and overall customer experience.
Industries like manufacturing, construction, energy etc.
Most companies understand the value of a safety culture and have ideologies about what a hyper-vigilant safety culture looks like — where employees are on high alert of their surroundings, perpetually evaluating risks for the task at hand and taking precautions accordingly.
Algorithms can identify objects, edges, and velocity because of the advancements of Machine Vision.Workplace Safety Solutions through AI Computer Vision:Process and Production Safety - Majority of the workplace facilities have a CCTV system installed on the premises.
Vision-based object detection and tracking use feature such as shape, color, motion etc.
Machine learning (ML) and artificial intelligence (AI) have started to gain traction over the past years, and today, nearly every emerging startup is trying to leverage these technologies to attract funding and disrupt traditional markets.
And it’s true that companies using “AI” and “ML” as buzzwords in their pitch are more likely to attract external investments than their counterparts working with traditional and mainstream tech.But still, apart from all this hype around machine learning, how applicable is it for solving real-life, everyday problems and when does it make sense to use it instead of/together with traditional software programming?
Let’s start exploring the issue by describing the various types of machine learning and its basic principles.Machine Learning vs Traditional ProgrammingTo better understand how machine learning works, let’s look at how it differs from traditional programming.First of all, machine learning does not replace traditional programming, and a software developer will never use machine learning algorithms to create a website.
For example, ML can be used to build predictive algorithms for an online trading platform, while the platform’s UI, data visualization and other components will be implemented in a mainstream programming language such as Ruby, Python, or Java.The rule of thumb: only use machine learning when traditional programming methods are not effective/feasible for solving a particular problem.To better exemplify it, let’s consider a classical machine learning problem of exchange rate forecasting and see how it can be solved with the help of both techniques.In this article, we looked at three types of machine learning: supervised, unsupervised, and reinforcement.
Each of them has areas of practical application in real-world conditions and its own distinctive features.Supervised ML is by far the most developed and applicable form of machine learning to date.
Now there are dozens of ready-made classical algorithms for machine learning, as well as various Deep Learning algorithms for solving more complex problems, such as image, text, and voice processing.On the other hand, unsupervised machine learning is much less applicable in real life.
You might think machine learning is far-fetched from real life and something used by scientists with exceptional qualifications.
Believe me; you are mistaken here.
You’d be surprised to learn that you deal with this phenomenon in your daily activities.Netflix and Spotify suggestions, your favorite eCommerce chatbots, Grammarly and other spellcheckers, and many more.
All of those, as mentioned above, are shining examples of machine learning technology in our lives.This trend has become an essential part of software development.
And to build a machine learning app is no longer a crazy idea.
Your business may benefit from ML applications and bring additional value to the market.
Looking back at the 1990s, technology was limited to computers, the internet, emails, wired telephony, but advancements in technology have led to a sweeping change in its role as an indispensable necessity in our lives and society.
In the subsequent sections, we will see the expanse of AI and ML in various use cases as well as understand their role in one of the most advanced forms of biometric security — facial recognition.What Is Artificial Intelligence (AI)?AI is a technology that simulates human intelligence processes using machines to make cognitive decisions.
Secondly, it adds intelligence to products in the areas of automation, conversational platforms, smart machines, and bots.
Lastly, Artificial Intelligence helps in monetizing data for businesses to stay ahead of the curve.What Is Machine Learning (Ml)?Machine Learning is a subset of AI that mainly focuses on using data and algorithms to mimic human learning.
It converts unstructured data to manageable groups for processing through a process known as dimensionality reduction.On the other hand, neural networks also known as artificial neural networks comprise node layers — an input layer, multiple hidden layers, and an output layer.
In the basic neural network, two or three layers are present whereas a deep neural network consists of more than three layers.How Does ML Work?An ML algorithm has three components:Decision processError FunctionModel OptimizationIn the decision process, an initial input is analysed to make a prediction or estimation of the pattern in the data.
Unstructured data can be filtered with the help of AI algorithms.
How do you get more information from non-sequential information?
These all happened with the help of data mining used by Artificial Intelligence.
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