Advance Machine Learning:An Era Of Innovation For Beginners

 

Technology has taken over the world and our lives. Things are now very different from how they previously were. Now, we have become very dependent on technology and that too without realizing it.Technology has evolved too. It has become more immersive, user-friendly, and smart. Where once humans were a necessary element to keep machines running, Data Science and Machine Learning are making machines smarter. Just look at self-driving cars.

 

However, what is Machine Learning? Machine Learning is the in-depth study of statistical models and algorithms that are used by computer systems so they can carry out a specific task. All of this is done by using only inference and patterns instead of anyexplicit instructions.Machine Learning is considered to be a subset of AI (artificial intelligence). However, it isn’t exactly like AI.

 

Timeline of the Era

Machine Learning has been around for a long, long time. Way before many of us were born. Although when we think about AI or Machine Learning, robots automatically come to mind. Yet, MA and AI are not just about that. There is a lot more to it. AI has been integrated with other technologies to make them more adaptive, intelligent, and perceptive. Nonetheless, how did all of this come about?

 

Let’s have a look at the era in which Machine Learning was introduced, also called as the era of innovation, to learn more about the milestones of MA.

1950s

·         IBM’s Arthur Samuel developed the first computer game program in the 1950s.

·         In 1952, Arthur Samuel coined the phrase 'Machine Learning.'

·         Frank Rosenblatt combined Arthur Samuel’s Machine Learning with Donald Hebb’s model of brain cell interaction to form the Perceptron.

1960s

More layers were discovered in the Perceptron, so it would offer the higher processing power, which led to the back propagation and feed forward neural networks.

1970s

A period of reduced funding in AI called ‘AI Winter’was caused.

1980s

A resurgence was witnessed in Machine Learning after the rediscovery of backpropagation.

1990s

·         It was the golden era for Machine Learning.

·         Multiple developments were made in the technology, algorithm, and hardware.

·         Machine Learning shifted to a data-driven approach from a knowledge-driven approach.

2000s

Several contributions were made in the field of Machine Learning and Artificial Intelligence, including Deep Learning, which now saw commercial use.

2010s

Machine Learning received much publicity and is now widely used in software services.

 

All the information above showcases the timeline of the era of innovation for Machine Learning. During this era, there were various developments, innovations, and breakthroughs in the field of Machine Learning. There is more to it, but now it is time to move on towards its usage in the real world, to help understand that firstly Machine Learning is different from Artificial Intelligence; secondly how Machine Learning can be leveraged in business.

Integration In Industries

Machine Learning and Artificial Intelligence have become part of many technologies and industries. They immersed themselves deeply into various processes, and as a result, offer multiple benefits. Below is a list of all the sectors that are relevant to Computer Learning and how this concept can benefit businesses.

 

Let’s look at Artificial Intelligence on the whole to learn more about its contributions to various industries and how AI is changing lives.

Manufacture

A number of processes have become automated in the manufacturing process. Where once machines had reduced the need for workers, they will soon replace the labor forcefully.

Artificial intelligence (AI) in the manufacturing market in 2018 valued to be around USD 1.1 billion. By 2025, it is expected to reach around USD 18.5 billion. (Source: Zion Market Research)

Healthcare

Machine Learning is flourishing in the healthcare sectorthrough sensors and wearable devices that use data to evaluate patients’ health. In 2017, around 7 million Americans were using wearable tech to monitor their vital signs with the help of digital health platforms.

AI has made its way into the hospital system, and diagnostics, but very soon, robot arms might be seen performing surgeries on people.

Artificial intelligence (AI) in the healthcare industry in 2018 valued at around USD 1.4 billion. By 2025, it is expected to reach around. (Source: Zion Market Research)

Gaming

Artificial Intelligence has also immersed itself into gaming, and we are not just referring to the computer-generated opponents seen in combat games.

“The only similarity Machine Learning has to Artificial Intelligence is that both are smart technologies.” – Joe Nast (Writer, crowdwriter)

Processes Get Easy Through Machine Learning

Now let’s have a look at all the sectors that have integrated Machine Learning into their processes.

Financial Services

Banks, along with other financial institutions, utilize Machine Learning for a number of reasons including:

·         To prevent any kind of fraud

·         Identify insights into data such as investment opportunities

·         Identify vulnerabilities using cyber-surveillance

·         Pinpoint clients that have high-risk profiles

Retail Industry

Machine Learning is being used to gather, analyze, and make use of data where people can personalize their shopping experience. Machine Learning capabilities provide online shoppers with personalized product recommendations. Meanwhile, it also includes coupons, pricing, and other incentives.

More than 28% of retailers are integrating Artificial Intelligence/Machine Learning solutions into their business, which is seven times more than the number of retailers in 2016. (Source: Google Trends)

Automotive

Big Data analytics and Machine Learning are being used by the automotive industry to improve operations, customer experience, and marketing. Predictive analytics allows manufacturers to share and monitor critical information about potential parts and vehicular failures. Also, it helps identify patterns and trends from large datasets.

In 2018, a 3% marginal increase was seen in the number of companies deploying AI at scale in the industry. (Source: Accelerating Automotive’s AI Transformation, Capgemini)

Government Agencies

Government agencies use Machine Learning to deliver utilities andpublic safety because they consist of multiple sources of data, whichneeds to be mined. This analyzed data can be used to identify ways of saving money, increasing efficiency, minimizing identity theft, and detecting fraud.

Transportation

Machine Learning’s modeling aspects and data analysiscapabilities are utilized bypublic transportation, delivery companies, and other transportation businesses.Analyzing the data can help identify trends and patterns to predict problems in increasing profitability and making routes more efficient.

AI is expected to reach 3.5 billion dollars in the transportation market by 2023. (Source: P&S Intelligence)

Oil & Gas Industries

Machine Learning can be used in business operations to translate data sets into insights and to collect large volumes of information and translate data sets into actionable insights. This can, in turn, reduce costs, improve safety, save time, and boost efficiencies.

AI in the oil and gas sectoris expected togrow to $2.85 billion by 2022. (Source: Markets and Markets)

Conclusion

Machine Learning is essential for all sorts of businesses to collect and analyze data, which then can be used to identify patterns and garner insight. This can be used to cut costs, find vulnerabilities in their techniques, and find ways to maximize efficiency and more.

“These are the pros of Machine Learning now. Imagine what benefits it could offer once the technology evolves further.” – Belinda Newman(Staff Writer, Academist Help)

 

Author Bio:

Claudia Jeffrey is the Founder and CEO of WordCountJet.She is also serving as the Assistant Manager of Networking at Assignment Assistance. A staunch believer in Machine Learning, Claudia makes use of it to streamline processes in her own business.