Machine Intelligence | Artificial Intelligence & Machine Learning

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Machine Intelligence is an advanced intelligence computing that enable any machine to interact with the outside world. Machine Intelligence uses Machine Learning (ML) and Deep Learning (DL) to run systems for automation & network evolution. Machine intelligence allows organizations to operate more efficiently, using data to predict the future and manage the present.

Following tasks can be performed by Machine Intelligence

Extraction and
classification of data

Optimization and
automation of processes

Interaction enhancement
with humans & environment

Detection, analysis &
prediction of trends or patterns

Types of Machine Intelligence

Machine Learning

Machine Learning is based on the idea to give these machines the power & access to data & let them learn themselves. Due to emerging technical concepts and technologies this idea of machine learning has transformed to a much higher level. The new wave of technology focuses on iterative learning where machines learn to repeat decisions made in the past, in similar situations based on the patterns and computations that were previously created. So, to see this concept simply, the machine learns from the previous computations, known as pattern recognition to produce robust results.

Why is it important?

To understand why machine learning is important for your business consider some of these examples: the self-driving Google Car, friends’ suggestions of Facebook and your favorite items being displayed on Amazon- are enough to enlighten with the fact that this technology has taken over the world. Industries handle a lot of data and to filter the relevant one, machine learning is important. Machines can speed up this process to bring out only the integral piece of information that is relevant in industrial development making businesses work more efficiently gaining a competitive edge. Implementing various automatic sets, ML has replaced many traditional statistical techniques of data extraction and interpretation providing a well gateway to industries to excel in their fields. Affordable and easy computation processing & cost-effective data storage options made it feasible to develop models that quickly and accurately analyze huge chunks of complex data. Organizations already using ML are successful in delivering personalized services and differentiated products that precisely cater to varying needs of the customers. To know what it takes to develop an efficient and proper machine learning program for your business here’s what you need to excel in:

Superior data
preparation capabilities

Knowledge of basic &
advanced algorithms

Better
Scalability

Automation & iterative
processes

Knowledge of
ensemble modeling

Machine Learning Methods We Use

Machine Learning methods are not just limited to the two most widely accepted methods: Supervised and unsupervised learning, there are various other machine learning techniques.

Overview of some of the most accepted ML methods are as follows -

Supervised Learning

In Supervised Learning the algorithms are provided with a proper input and a correct output to detect the errors. The algorithms are trained using labeled examples like for instance an equipment can have data points as ‘F’ (failed) and ‘R’ (run). Using these labels, the algorithms are able to modify the model further. This is known as pattern recognition where the algorithm uses different methods such as, regression, classification, gradient boosting, and prediction. Supervised Learning is generally used in the areas where historical data predicts the future events, such as in fraudulent credit card transactions.

Unsupervised Learning

Here the data has no historical labels. The algorithm has to identify the structure from the given data after analyzing it properly. This type of learning works best for Transactional data like identifying customer segments and attributes and then lets the business to treat them similarly in marketing campaigns. Unsupervised Learning is about identifying a similar structure in the available data sets and outliers in the available data sets.

the available data sets and outliers in the available data sets. A few popular techniques of unsupervised learning are -

k-means clustering self-organizing maps value decomposition mapping of nearest neighbor

Semi-supervised Learning

Semi-supervised Learning is yet another kind of learning which is based on the notions of both supervised and unsupervised learning. The algorithm uses small set of labeled data and a large set of unlabeled data for training. This type of machine learning is often used with methods, such as regression, classification and prediction and is ideally used in face and voice recognition techniques.

Reinforcement Learning

As the name suggests this type of learning is based on the traditional data analysis where the algorithm analyses the highest rewarding result through trial and error. The three main components in reinforcement learning are: the agent or the decision maker, the actions that the agent does and the environment that interacts with the agent. As a whole, the ultimate idea of reinforcement learning is to identify the best policy or the method that helps businesses in achieving the goals faster. Reinforcement Learning is commonly used in navigation, robotics and gaming.

Type of Machine learning algorithms we develop

To extract value out of Big data, businesses must know exactly how to pair the right algorithm with a particular tool or process and build machine learning models based on iterative learning processes. A few main machine learning algorithms are -

Random forests Analysis of principal components Discovery of sequence and associations SEO Boosting and bagging gradient Decision trees Neural networks Mapping of nearest neighbor Supporting vector machines Multivariate adaptive regression

Harnessing ML applications is not just knowing the algorithms, but in pairing them accurately with the right tools and
processes, which include:

1

Data exploration and visualization of results

2

Data quality and management

3

Deployment models for quick, reliable and repeatable results

4

Creating process flows and models through development of graphic UI

5

Comparison and Identification of various ML models

6

Performance comparison through automated ensemble model evaluation

7

Data-to-decision Process Automation

Deep Learning

Artificial Intelligence holds a broader concept of machines carrying out tasks and processes on their own according to our assistance wherein Deep Language is an application of AI to achieve this target. Deep learning is a machine learning technique of understanding through example, it will teach computers to do that comes to humans naturally. For example, on self-driving car will recognize the STOP sign and understand to stop car on that sign. In deep learning, a computer model learns to perform tasks directly from audio, video or text. It encompasses ML, where machines can learn by experience and acquire skills without human involvement. Deep learning is removal of the manual identification of the features in data on whatever training process it discovers the useful information in the input examples.

Deep Learning Applications

Speech Recognition

Image Recognition

Natural Language Processing

Why Is Deep Learning Important?

We are living in time in which we are facing many challenges. Deep learning technology can help us to achieve them.

It makes
life easy
Distributed
Representations
Depth
knowledge
information is spread
over several neurons

Benefits

Cost and time benefits

Quality and accurate results

Growth in Job

identifies defects

Type of Industries Vertex Plus services

Machine Intelligence has gradually attained a valuable mark in the corporate world. There is not a single industry left that does not deal with huge volumes of data and to manage this data, they are the solution. By leveraging insights obtained from the filtered data, companies are able to work in an efficient manner to control costs and outshine their competitors. Here are some of the industries which implement machine learning:

Financial Sector

Companies in the financial sector are more concerned about managing the financial data. Machine Intelligence is used extensively to identify key insights in financial data as well as to prevent any occurrences of financial fraud, identifying institutions prone to financial risk as well as identifying investment opportunities. oblems in the first place.

Marketing and Sales

Personalized marketing is the key to gain more and more customers hence the companies are using Machine Intelligence technologies to analyze the purchase history of their customers and make personalized product recommendations for their next purchase.

Government

Government agencies also handle a large amount of data that needs to be handled efficiently. For any government public safety is the major concern as they have multiple data sources. Machine Intelligence is used to identify useful patterns and insights and also to minimize identity thefts and detect fraud.

Healthcare

Healthcare is also gradually inclining towards the Machine Intelligence technologies. Wearable sensors and devices have become the new trend that uses data to access health of a patient in real time. These sensors provide real-time patient information, such as overall health condition, heartbeat, blood pressure and other vital parameters. This technology also empowers medical experts to analyze data to identify trends that facilitate better diagnoses and treatment.

Transportation

As machine intelligence works on pattern recognition hence based on the travel history and pattern of traveling across various routes, this technology can help transportation companies predict potential problems that could arise on certain routes. This is helping Transportation firms and delivery organizations to make informed decisions and help their customers make smart while travelling.

Oil and Gas

Oil and Gas industry uses Machine to analyze underground minerals and find new energy sources to continue oil distribution. This application is yet to be explored a lot for this industry.

Why Choose VertexPlus ?

COMPETENCE

Our highly experienced team with vast technological expertise is fully equipped to deliver perfect solutions for all your requirements.

COMMITMENT

Our dedicated and professionally skilled team works relentlessly to ensure error-free and timely delivery of solutions to our clients.

CREDIBILITY

With the best talent on-board and following best practices, we are fully committed to provide technically superior and the most efficient solutions to our clients.

COLLABORATION

We keep our engagement clear and transparent; our teams work hand-in-hand with clients ensuring exceptional servicing & long-term relationships.

CLIENT ORIENTATION

With the Client-First approach; all our efforts are aimed towards creating winning solutions for our clients, even if that calls for going the extra mile.

CLIENT SATISFACTION

For us Client satisfaction is supreme; we take full ownership and responsibility to offer you more value for your money and keep you ahead of your competition.

Why Choose VertexPlus ?