Table of Content
Everything or none of the things…!
Today, I welcome you to dive into the world of AI and ML technologies. Talking about advancement and invention here Machine Learning and Artificial Intelligence (both) have proved their way to reach new heights in overwhelming competition.
Do you know?
Smart machines and applications are rapidly wished list in a daily event and helping forthcoming and existing mobile app development companies to take part in the world of AI through the technologies used in Machine Learning.
So, here in this blog, you will go to explore and learn about some interesting sounds of technologies used in Machine Learning and the way it's refurbished mobile app development.
Before I start, do you agree with the statement that ‘Artificial Intelligence is the future of our next mobile application development’ (from its source to the origin)?
What do you say? Check here to know: –
“Artificial Intelligence: A New Approach to Future Mobile Applications”
Let get started with basics…!
What Do You Understand by Machine Learning?
Well, the term ‘Machine Learning’ was coined in 1959 by ‘Arthur Samuel’ which reflect the field of computer science respectively that use statistical methods to help computer systems to learn about performance or specific task with the help of data (explicitly).
If you heard about it, with more than 75% of enterprises right now investing in the big role of AI and MI which will not stop here, it dramatically increases percent by a percent each year.
I can bet (you don’t know what I will go to say in the next line)…!
There is no doubt that AI and ML both have gained immense success in the field of IT, but do you know, enterprises/firm/business level of reliance is more strict on machine learning along with automation and artificial intelligence as measured in 2018.
“Why is this technology is next-must have ingredient for competitive businesses?”
The answer is very deep drowning…let’s check it!
Before the upcoming age of fruitful technologies, the biggest problem faced up by the business is that the restriction of limited operating space by the limited number of people.
But now, the time coinage and most of the work we have accomplished by automating and digitizing the business process through Machine Learning.
With the increase in AI and ML technologies, consumers all around the world give immense looks towards AI technologies which help million and hundred dollar businesses to become more customer-centric.
Machine Learning plays an important arena to increase the potential consumers, and all this happens with the hidden behind data accumulated by the business.
The automation technologies have dominated everywhere like in the case of cashier, billing, assignment, etc. all work now operating via the automation process.
As these technologies fluctuate in the market at (high level), thoroughly it increases the business growth level vastly like speed light.
The machine learning technologies are the part of the developer’s team that builds iconic products and algorithms and delivers reliable, quick, and scale functions.
Here and now the AI and MI become almost a sphere part of our lives, from the source of communication to the end of transportation, we seem our self-trap in AI (Artificial) and ML (Machine Learning) world.
Here, for better understand, you can put your eye into below [Infographic]…!
To know the Great and Open Machine Learning Technology used in 2018. Get it to know…!
Now, to know it deeply, please scroll down and read each element…!
The deep learning platform that deals with strong models over large datasets is known as ‘Apache Singa’. It is an intuitive design programming model-based abstraction that supports deep learning models with the help of popular convolution neural networks (CNN), recurrent neural networks (RNN), etc.
Using this machine learning-based technology; its built-in layer provides great experiences to the users.
The top and large e-commerce king ‘Amazon’ uses ML technology to serve developers with easy skills and levels to use machine learning technology. It highly provides visualization tools and kits that guide you to create a machine learning model without learning a complex algorithm.
It gives security with the strong data storage facility to create a model at best and a flexible way that is really easy to understand.
API is also getting a huge hit in the market for any type of activity. Microsoft Azure allows users to create and train models and further allows them to turn into APIs that will consume by the consumers easily.
The main aspect to use this machine learning is the features that it’s providing to the uses with every detailed algorithm.
To give a boost to your machine learning, get deep learning with the Caffe framework. Its superior expression, speed, and modularity allow developing meaningful models and optimizing it with a different configuration.
Users can define configuration without hard-coding and allow at the same time to switch between CPU and GPU.
It literally does not stand for water (as I learn in schooling time)…!
H2O is widely used in large as well as small enterprises to solve today’s most challenging business problems. Due to its countless counting features, it is hardly found in other machine learning platforms.
With, H2O, you can work on the existing language and can extend the platform seamlessly into your Hadoop environment.
MOA is the most popular and globally recognize data stream mining with an active (growing) community. It is also based on the algorithms that collect huge machine learning classification for end result evaluation.
Do you know that MOA is also written in JAVA?
It is a strong Apache Spark’s contribution towards the machine learning library. It has a wide aim to make practical ML as scalable and easy as it makes.
Best known for its common algorithms and utilities, for regression, clustering, and filtering along with optimization to deliver a higher level of satisfaction.
Ml pack is based on the C++ machine learning library which is greatly designed for scalability, speed, and ease of functionality. As it is market out in (2011), it implementing mlpack cache command-line more progressively for quick and dirty operations.
It provides C++ and high efficient algorithms which integrate into large-scale machine learning solutions.
It is an open-source machine learning framework that was released in 2015, with the aim to deliver easy deployment across a variety of platforms. It is one of the well-known and progressive frameworks for machine learning now a day.
Scikit learn is an open-source library initially released in 2007. While it is written in Python (only) and its amazing seamless feature help to create a successful model with classification, regression, and dimensionality reduction.
Liberate in 2007, as other related frameworks publish, is an open-source library based on Python that liberally allows you to easily explore various machine learning models.
Since it is one of the oldest frameworks regarded as the highest vote in the global scenario, it simplifies the process of defining, optimizing, and assessing mathematical expressions.
Now, after tasting my words, now look at the previous one too.
Here are some real competitive examples or you can say game-changing machine learning examples in the era of 2018 where technology lies at the semantic stone…!
To understand it in a simple way, I organize a fruitful Infographic (for a better experience).
Say Hello to My Opinion!
Read trending, live trending, and taste trending are the things customers prior to it…!
Talk about priority, here (Machine Learning) is the subway to fulfill those prior needs either in case of simplicity, flexibility, and entertainment that right now we and other (peoples) getting.
Here (both) AI and ML have been fluctuating the market with the help of hybrid app development.
Simple, its countless features help various hybrid app development companies to deal in the field of AI and ML.
As regards this, you can also be a part of Machine Learning in an easy way…!
Well, this is it; these are the things I just want to keep you updated with…!
!!By!! Meet in the next blog
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