Machine Learning Examples In Real World

Machine Learning can be defined as the application of Artificial intelligence to enable systems to learn without being programmed explicitly. The whole process of improving the model happens over the experience that these Machine Learning models gain with the data that is made available to them. These models or computer programs access the data that is available to learn for themselves. In this Machine Learning Examples article, we will see how this study helps in various industries that are planning in adapting these techniques to their business processes. With this understanding, we shall look into the following topics outlaid as below:

Table of Contents - Machine learning Examples In Real World

➤Evolution of Machine Learning ➤Why is Machine Learning so popular? ➤Machine Learning examples by industry Machine Learning in Finance Industry Machine Learning in Marketing and Sales Industry Machine Learning in Healthcare Industry Machine Learning in Government Industry Machine Learning in Transportation Industry Machine Learning in Oil & Gas Industry Machine Learning in Technology Industry ➤Companies that use Machine Learning ➤Real World Machine Learning Examples

History Of Machine Learning

Consider a time frame just about 50-60 years back, Machine Learning was nothing but a puzzle piece from a science fiction movie. But now, it is more than an integral part of our lives helping us in most of the fields that a common man is interested in, ranging from movies, education, inventions, to space research. There has been enormous growth in this field, all thanks to countless philosophers, mathematicians, and scientists who’ve worked towards achieving this. Following is a brief history depicting how the Machine Learning that we know has evolved:

• The “Turing Test” was created by Alan Turing to prove Computers had intelligence in the year 1950.
• Arthur Samuel wrote the very first algorithm in the Machine Learning space (in the year 1952).
• The first neural network for a computer was designed by Frank Rosenblatt in the year 1957.
• The basic pattern recognition algorithm “Nearest Neighbor” was written in the year 1967.
• “Stanford Cart” that can navigate through the obstacles in the room by itself was invented by the students at Stanford University in the year 1979.
• The concept of Explanation-Based Learning (EBL) was coined by Gerald Dejong in the year 1981.
• NetTalk, a tool that pronounces words just like a baby was invented in the year 1985 by Terry Sejnowski.
• Machine learning shifted from the traditional knowledge-driven approach to a data-driven approach in the 1990s.
• IBM Deep Blue beat the then world champion in the game of Chess (in the year of 1997).
• “Deep Learning” is coined by Geoffrey Hinton which explains the algorithm to identify and distinguish objects in pictures and videos (in the year 2006).
• Microsoft Kinect, a technology using movements and gestures which was able to interact with a computer was introduced in the year 2010.
• Human counterparts were defeated using IBM Watson at Jeopardy. Google Brian is developed with its deeper neural network capabilities to identify cat movements. Both of these were introduced in the year 2011.
• Google X Lab introduces an algorithm that can anonymously browse through YouTube videos containing cats in the year 2012.
• Amazon launches its own Machine Learning Platform. A distributed Machine Learning toolkit was introduced by Microsoft. Both of these were introduced in the year of 2015.

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Why is Machine Learning so popular?

There are various reasons for Machine Learning to see its rise over the past couple of decades. The most important reason that we see is that the whole concept is driven by data and the larger the dataset, the more accurate are the results achieved by these algorithms. Analyzing the available vast data, there are patterns that can be outlined from these - these can help Organizations take better decisions or choices. Machine Learning is finding its usage in almost all the available industries, making it a defacto standard for the following:

• Fraud & illegal activity detection
• Pattern recognition
• Email filtering & identifying spam
• Credit score and best offers based on the same
• Network intrusion detection
• Real-time ads on websites
• Equipment/infrastructure failure prediction
• Aerospace study (space exploration, guided robots)
• Advancements in Medicine

Machine Learning Examples by Industry

Machine Learning is a generic study of feedback and putting this feedback through a constant learning model. This is the process that is applicable to almost every other industry, but then, each industry has its own scenarios and situations to handle which might be unique for those industries only. Hence, there can be variations on how Machine Learning can be put to use in each of these industries as such. The following section helps in explaining this in detail about the usage of Machine Learning in various industries:

• Financial Industry
• Marketing and Sales
• Healthcare
• Government
• Transportation
• Oil & Gas
• Technology

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Listed above are the various industries that are seeing the benefits of Machine Learning in recent days. Machine Learning has definitely taken most of these industries by storm, in the way these industries used to function earlier to now. There are numerous benefits that it has brought to the majority of these industries and also improved the way these businesses are being conducted. Let us now understand each of these industries in detail and see what advantages are reaped using Machine Learning:

#1) Machine Learning in Finance Industry

Introduction to Finance Industry

Machine Learning has become the defacto standard for putting all the security-related efforts. The traditional choices which were made based on customer history are now being handled with the help of Machine Learning instead of individuals taking decisions. In the subsequent sections let us see how this is achieved.

How does it help?

Decision making is improved by leaps and bounds based on the models developed and the data that is fed into these models. The customer history details are fed as inputs and based on the history and the other factors, loans or waivers are checked for feasibility. Without Machine Learning, it would’ve been a man-made decision where there is always a possibility to make a decision based on the customer, which may not align with the financial institution’s best interests.

Advantages:

Following are the advantages that Machine Learning has brought to the Financial Industry, as such:

• There is better process automation.
• Compliance to all rules and regulations are ensured with Machine Learning (as the models are developed based on these conditions for better learning).
• Offers recommendations based on user history by evaluating possible requirements.

Applications:

• The following are the applications of Machine Learning in the Financial Industry. Let us take a closer look at these:
• Fraud Prevention
• Enhanced Customer Service with precise options
• Better Trade Information based on prior history

#2) Machine Learning in Marketing and Sales

Introduction to Marketing and Sales

Marketing and Sales Industry is no longer at the end of the recent technologies that are rising in popularity, but also want to stay abreast with the advantages that these technologies bring to the table. As per a survey conducted, over 97% of the industry experts or veterans believe that the Marketing industry is going to be overwhelmed with the Machine Learning techniques.

How does it

help?

Provides immense help in identifying the probable business leads and also ensures the conversion of these leads to permanent business relationships. If it is not Machine Learning that aids in identifying these, methods like trial and error or manual follow-ups would’ve employed. With the evolution of chatbots for customer service and marketing being personalized for each and every customer, Machine Learning is going to be the key to shaping the Marketing & Sales industry.

Advantages 

Following are the advantages that Machine Learning has brought to the Sales and Marketing Industry as such:

• Provides improved and better marketing qualified leads
• Enhanced sales qualified leads
• Reduction in marketing costs
• Enhanced Customer Satisfaction rates and User Experience

Applications:

• Risk prediction and intervention
• PPC (Pay Per Click) campaigns based on Machine Learning
• Content & Email campaigns based on the insight provided by Machine Learning
• Predictive Data modeling

#3) Machine Learning in Healthcare Industry

Introduction to Healthcare Industry

Healthcare is one of the best use cases where Machine Learning can find its usage. The reason for us to go with this point is the fact that there are too many processes that are implemented manually where Machine Learning could automate these processes for us. Most of the diagnoses are done manually even to date which could’ve been automated.

How does it help?

Automates most of the monotonous activities, reduces the probability of any manual errors, and also cuts the cost of manpower. Automated tests also help the Hospital for better bookkeeping of these reports and the needed documentation.

Advantages 

Following are the advantages that Machine Learning has brought to the Healthcare Industry as such:

• Helps in predicting Chronic diseases.
• Ensures shorter LOS (Length of Stay) for the patients.
• Helps in preventing Hospital-acquired Infections (HAI).

Applications:

• Medical Imaging Diagnosis
• Identification of disease and DIagnosis of the same
• A clinical trial, research for incurable diseases

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#4) Machine Learning in the Government Industry

Introduction:

There is always a need that the Government should also lean towards Machine Learning for various scenarios: To identify the possibilities of war, to maintain the status quo with foes, and also to take calculated decisions in the most unforeseen situations. Many Governments have undertaken this exercise for varied needs but indeed, this is a step that has to be taken.

How does it help?

It helps Governments take decisions based on the situations - financially, globally, economically, or on security grounds. There are various ways through which these could be studied and the enormous amounts of data that is being tapped from various regions through various agencies.

Advantages:

Following are the advantages that Machine Learning has brought to the Government sector as such:

• Help predict when roads would have potholes based on the quality with which roads are laid.
• Help predict component or vehicular failures for our militaries.
• Help predict political deviations based on the data being collected.

Applications:

• Citizen Engagement program
• Emma at US Citizenship and Immigration Services

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#5) Machine Learning in the Transportation Industry

Introduction:

The transportation industry is no longer a dumb transport for people from source to destination. For economical usage of the resources, there is always a run to look for the cheapest way to get your resources at work and yield the maximum out of it. Machine Learning has evolved leaps and bounds in this industry too, from shortest paths, alternate routes, fuel, and cost-effective mechanisms employed by these providers. It has grown from a saturated orthodox business to a highly efficient and intelligent service providing organizations.

How does it help?

As a transportation giant, if you are in control of predicting the demand and cater to the demand without spending extra, you are in a very good situation. Complete resource utilization is achieved and based on the supply-demand, there can be plans to hire or fire resources. Helps transportation agencies, organizations to make quicker decisions.

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Advantages:

Following are the advantages that Machine Learning has brought to the Transportation industry as such:

• Helps in optimizing engine designs and processes.
• Optimized and shorter routes.
• Decision making on the fuel economy based on usage.
• Predicting transportation requests, demand.

Applications:

• Develop models in helping and predicting demand.
• Innovations in each of the Transportation industries based on learning.

#6) Machine Learning in Oil & Gas Industry

Introduction:

There are Organizations who are looking for alternate fuels for their own engines looking at the possibility that the resources like Oil and Gas can perish anytime in the near future. That being said, there is also a Supply Demand game that goes with the existing available resources and fuels, that need to be catered as well.

How does it help?

Machine Learning helps in identifying the best fuel efficiency with which a vehicle can go, then using it without considering any factors. This cuts down the additional wastage and thereby saves the aforesaid amounts of fuel for future generations.

Advantages:

Following are the advantages that Machine Learning has brought to the Oil & Gas industry as such:

• Help predict efficient fuel consumption for vehicles
• Help predict locations for further availability
• Help in optimizing the available fuels to make better ones

Applications:

• Enhanced seismic interpretation
• Stratigraphic frameworks to facilitate evaluations.

#7) Machine Learning in Technology Industry

Introduction:

Machine Learning is a boon obtained from one of the wings of Science along with Mathematics and Philosophy. Hence, there is no specific introduction on what Machine Learning is here for in this industry.

How does it help?

Machine Learning is not just about helping almost all the industries available, but also helping the Technology industry to keep track of a lot of things - starting with cyber security Machine Learning can help identify a similar cybersecurity flaw identified in an organization falling in the same sector plus additionals factors that determine these organizations.

Advantages:

Following are the advantages that Machine Learning has brought to the Technology industry as such:

• Massive data consumption from various sources, resources.
• Facilitates accurate predictions without skipping the precision
• Quicker and easier spam detection
• Improved efficiency in maintaining infrastructure
• Product recommendations

Applications:

• Google Tensorflow

Companies that use Machine Learning

When we discuss this topic, there is one thing that we need to bear in mind that each of these Organizations would be putting Machine Learning to use for their unique scenarios. Nonetheless, Machine Learning is put to general use to draw conclusions on major or critical factors. We will see some of the real-time scenarios in the next section where these can be studied in detail, but for now, let us just see through the list of Companies or Organizations that put Machine Learning to their benefit.

• Yelp: Image curation
• HubSpot: Smarter and Intelligent sales
• EdgeCase: Improved Ecommerce conversion
• Pinterest: Improved content discovery
• Uber: Route-based pricing system
• Facebook: Chatbot Army
• Google: Neural Networks with the capabilities to dream
• Baidu: Futuristic Voice Search

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Real-World Machine Learning Examples

In this section, we will see some machine learning examples that are available for us to use. Most of us would have already come across these examples in some way or the other but probably you would have not realized that it is a Machine Learning use case that is being looked at. This section will cover some of these examples and explains how Machine Learning has been put to use in a certain industry space to reap benefits:

Facebook:

Facebook has it's a homegrown algorithm to target the social media space, targeting for specific customers and specific businesses. The relevance that Facebook gives to the ads presented to end-users is pretty spot-on, as this is done considering a lot of factors like user preferences, search history, and usage. To put this in a simpler way, Facebook is building its business by learning about its users and using this data for their advertisers. Deep Learning is what exactly Facebook puts to use, to classify the available data all by itself thereby enabling better learning for the models. Facebook puts Machine Learning to perfect use by employing techniques like textual analysis, facial recognition, targeted advertising, and designing newer artificial intelligence applications that feed on the previous data.

Amazon:

Amazon, a retail giant helps its customers purchase related products and/or products that go along with the purchased products.   The simplest example one can think of is the suggestions that Amazon provides to buy a phone back case and a screen guard when you purchase a smartphone: if you have viewed a product earlier, showing it as your viewed product to purchase it later. These techniques that are employed by these giants are nothing but simpler and targeted examples of Machine Learning. These provide the customers to review the related products based on their purchases, review their previous searches for later purchases, etc.

Netflix:

Netflix, a giant in the data/content space provides its services to a varied number of customers from different geographical locations as well. Providing the services to all these customers in a uniform manner has issues with emerging countries like Japan, India, and South Africa where there are data issues, bandwidth issues, etc. Using a Machine Learning algorithm as, like Dynamic Optimizer, Netflix provides its HD services to all the customers in a uniform manner. Dynamic Optimizer compresses the HD content to smaller sizes without compromising on the pixels but adjusting between frames. This is a unique way to provide their services to all the customers without having issues with the limited bandwidths of such customers.

Uber:

Uber, the transportation giant, employs Machine Learning algorithms to provide the best of the services at the least possible fares to the end-users. Based on the frequency of cabs being booked from an area and individual, prices are fixed over and above the distance traveled through the cabs. This way, it provides better fares to the usual commuters from prominent places than ordinary commuters to remote destinations. Putting these algorithms to use, the tech giant is able to provide quality services at better and cheaper prices to the end-users. This has been coined as the Route based pricing system by Uber to cater to the needs and demands of customers.

Google Search:

One of the best sorted out search engines that is available is Google Search. Ever wondered what happens behind the scenes when a search query is presented to Google?. There are a couple of algorithms that we can assure they work behind the scenes whenever you present a search query to Google. One is the Knowledge Graph algorithm and the other is the PageRank algorithm. The PageRank algorithm counts the number of references that you make from one of your websites to the external world and based on these values, provides an estimated page rank to your webpage. More the links to the external world mean lesser is your page rank and lesser are the possibilities to show up in the Google search results. 

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Conclusion:

Machine Learning, as we understood from the prior sections, is becoming the de facto standard in ascertaining customer satisfaction. There are various other scenarios that this technique is put to use, in order to make the perfect use of the feedback that these models are gaining on constant learning. Also, going through real-world examples of Machine Learning, it is evident that there are going to be more and more scenarios where machine learning can be put to use.