Machine Learning – Introduction

There is a vast difference between machine learning, artificial intelligence (AI) and deep learning though they are often used interchangeably. To state briefly, AI is conceptualized as a machine that can perform intelligently based on data fed to it and is capable of learning for itself. The other two terms – machine learning and deep learning are simply part of artificial intelligence.

Benefits of Machine Learning

Quicker Decisions

Machine learning helps to deliver precise and accurate outcomes because it is possible to automate and prioritize the decision making processes. When machine learning is clubbed with the Internet of Things it becomes easier to troubleshoot problems in the manufacturing process.

Easier to Adapt

Machine learning outperform human mind when it comes to speed and versatility. This means data is updated on a real-time basis which is a big advantage in discovering new ideas, processes and adapting to constantly-shifting business scenarios.

Growth and Innovation

“Machines learning” is all about complex algorithms that make sophisticated processes to appear simpler and easier. It is the basic component of that drives process automation resulting in improved and accurate decisions. What you get as a result is superior business models, improvised products and innovative services.

Uniqueness beyond Human Mind

“Machine Learning” is exciting because it is able to process patterns in Big Data – a process that is beyond human abilities. This makes it possible to deliver actions at unprecedented speeds. Potentially “Machine Learning” can help to predict opportunities and even recommend ways to conclude business deals.

Doing Business Faster

When you opt for machine-assisted business processing workflow will be fast and precise. It helps businesses get best value on investments resulting superior products and service offerings. There are countless benefits to reap – sell more, cut down back-office expenses and gain new customers.

Superior Output

“Machine Learning” can help to eliminate errors that are commonly done by humans. The quality of deliverables is superior and with added cyber security so essential in the financial service businesses. It can help to protect sensitive data, due diligence and statutory regulations.

Machine learning use cases

Among the best examples that illustrate machine learning are consumer application that can predict and recommend resources and smart device. This is not however the only areas; there are bigger potential to use machine learning in business to business commercial uses.

Smarter Processing

Machine learning is radically different from traditional learning. It is all about discovering a pattern in large database and transforming it into useful information that can be utilized for prediction. Businesses can apply this ability for checking bills and expenses for precision and accuracy.

Digital Assistants and Bots

One of the major areas in which AI technology will find immediate use is in digitizing business process such as scheduling meetings, documentation and translation and other regular business tasks. Self-learning algorithms will make this possible though within set parameters.

Sales & Marketing
Machine learning will be able to guide business managers and sales professionals to get alerts about their customers and give them current insights through use of specific algorithms. It will widely help in customer and vendor management processes that human minds cannot accomplish.
Customer Service
Machine learning can immensely help businesses to bring down costs related to servicing customers in a natural setting. It has the potential to process natural language, customer history by using algorithms that train the machine itself. Customers now are able to interact with machines directly and answer queries intelligently based on past experiences. Overall business will save tons of money and be able to focus on high-priority customers and their complex requirements.
Marketing
Businesses by focusing on machine learning can deliver customers a superior experience by giving them a personalized presentation that is more focused to specific needs. This means meeting customer requirements and expectations based on their past buying behaviors.
Finance
Applications that use machine learning are highly preferred because they are able to match historical customer data and their financial information. This makes it possible to optimize profits and focus on core customers. Devices can now recognize delayed payments and match invoices to customers’ orders and payment information. Basically it provides relief to financial professionals to concentrate on strategies rather than getting involved in routine tasks.
Internet Security
Recognizing anomalies is a major focus in machine learning. Machine learning algorithms can identify patterns in transactions and convert it into information that is useful for decision making. It makes the cyber space a safer place for carrying out business transactions automatically without human intervention. Overall it helps to prevent cyber crimes and fraudulent transaction on a real-time basis. It is easier to detect fraud types not previously documented. Potentially, machine learning can be applied to prevent hacking and making IT assets safer.
Human Resources
Machine learning models are used in identifying and recruiting personnel for their businesses. HR departments are able to use predictive analysis for making important decisions that are otherwise too complex to be carried out by human mind. Systems and devices that utilize machine learning can pick and process best talents from hundreds and thousands and of resumes and recommend the best. Machine learning is preferred for many reasons and more notably because it is not biased and is not influenced by traditional thoughts that are restrictive in nature.
Image-based Procurement
Machine-learning assisted procurement can vastly improve employee performance in the procurement process of a business. It makes it easier for employees to take snapshots of images of equipment and search for matching devices. By applying machine learning it is easier to order supplies by using just a few clicks. The advantage is it cuts down cost and save money on capital equipment.
Brand Management & Analysis
Building a formidable brand image is a long process. Businesses spend a big fortune on this often without knowing what they get back in return. Machine learning can focus where to direct the branding effort and sponsor to get best returns. A machine-learning assisted device can process and analyze hours of video footage to locate your brand logo and its prominence. This will help businesses to sponsor in appropriate situations on a scientific basis rather than guess.
Contextual Assistance
Machine-learning assisted apps on smartphones is a typically example where contextual assistance will help you manage time more efficiently. You can set of delays by helping you to reschedule plans and suggest multiple options to save money and time.
Visual Inventory Management
Machine learning can be used for optimal use of shelf space for best customer response. Your employees can shoot photos of your store or warehouse and immediately identify wrongly placed merchandize and prompt managers to rectify errors on a scientific basis.
MFG & Quality Control
Machine-learning assisted devices can be extensively deployed in quality control processes that require elimination of defective products. It is faster and more efficient than human effort. Device on machine learning systems can process many hours of video to spot defects in products on the assembly line and alert quality control personnel.
Satellite & Drone Inspection
Outdoor installations maintenance becomes easier for engineers when they use machine-learning systems. Typically a machine-learning system mounted on drone can automatically spot defects such as in a oil pipeline making it easy to speed up maintenance and cut down down-time.

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