The innovations in science makes our life more comfortable and preferable than before. In our regular day to day existence, the commitment of science is simply irrefutable. We can not ignore the effect of science in our life. If we try to analyze the effect of science in our life, then we will notice that, these are the outcomes of using Artificial Intelligence and Machine Learning applications. Machine learning is a modern innovation of science. It helped man in industrial and professional processes and advances everyday living. To know the difference between Artificial Intelligence and Machine Learning, go through our blog- Artificial Intelligence and Machine Learning: A Comparison.
What is Machine Learning?
Machine learning is a subset of artificial intelligence. It focuses on using statistical techniques to build intelligent computer systems in order to learn from databases available to it.
Machine learning is the process of teaching a computer system how to make accurate predictions when fed data. Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video.
The main difference from traditional computer software is that a human developer hasn’t written code that tells the system how to distinguish. Instead a machine-learning model has been taught how to reliably distinguish between the fruits by being trained on a large amount of data, in this instance likely a huge number of images labelled as containing a banana or an apple. You can refer machine learning software tools at- Best Machine Learning Software and Tools To Learn in 2019.
Types of Machine Learning-
Supervised learning and unsupervised learning are the types of Machine learning.
What Is Supervised Learning?
This approach basically teaches machines by using examples. Systems are presented to large amounts of labelled data during training for supervised learning. For example images of handwritten figures annotated to indicate which number they correspond to. From the sufficient examples, a supervised- learning system would learn to recognize the clusters of pixels and shapes associated with each number and in the end have the option to perceive manually written numbers, ready to dependably recognize the numbers 9 and 4 or 6 and 8. For training of these systems, a large amount of labelled data requires. Systems with these data need to be exposed to millions of examples to master a task.
The data-sets used to train these systems can be huge with Google’s Open images about nine million images. The size of training data-sets continuously growing with Facebook and Instagram. Using these images to train image recognition system yielded record levels of accuracy – of 85.4 percent – on ImageNet’s benchmark. The process of labeling the datasets used in training is carried out using crowd working services such as Amazon Mechanical Turk. These services provides access to a large pool of low-cost labor distributed across the world. Facebook’s approach of using publicly available data to train systems could provide an alternative way of training systems using billion-strong datasets without the overhead of manual labeling.
What Is Unsupervised Learning?
In contrast, unsupervised learning tasks algorithms with identifying patterns in data, attempting to spot similarities that split that data into categories. An example might be Airbnb clustering together houses available to rent by neighborhood, or Google News grouping together stories on similar topics each day. The algorithm isn’t designed to single out specific types of data. It simply searches for data that can be grouped by its similarities, or for anomalies that stand out.
Why Is Machine Learning So Successful?
While machine learning is not a new technique. But the interest in this field has reached a sky in recent years. This resurgence returns on the back of a series of breakthroughs, with deep learning setting new records for accuracy in areas such as speech and language recognition, and computer vision. What’s made these successes conceivable? There are primarily two factors. One being the vast quantities of images, speech, video and text that is open to analysts hoping to prepare machine-learning systems. Most important is the availability of vast amount of parallel-processing power, cordiality of modern graphics processing units, which can be connected together into clusters to form machine learning powerhouses.
Anyone with an internet connection can use these clusters to train machine-learning models, via cloud services provided by firms like Amazon. As the use of machine-learning has taken off, so companies are now creating specialized hardware tailored to running and training machine-learning models. For eg., Google’s Tensor Processing Unit which speed up the rate at which machine-learning models built using using Google’s TensorFlow software library can construe information from data, as well as the rate at which they can be trained. These chips are not only to train models for Google DeepMind and Google Brain, but also the models. These models support Google Translate and the image recognition in Google Photo as well as services that allow the public to develop machine learning models with the use of Google’s TensorFlow Research Cloud.
The second era of these chips was revealed at Google’s I/O meeting in May a year ago, with a variety of these new TPUs ready to prepare a Google AI model utilized for interpretation in a fraction of the time it would take a variety of the top- end GPUs, and as of late declared third- age TPUs ready to quicken training and induction considerably further. It is becoming very common for ML tasks to be carried out on consumer-grade phones and computers, instead in cloud datacenters because hardware becomes more specialized and machine- learning software frameworks are refined. In the summer of 2018, Google took a step towards offering the same quality of automated translation on phones that are offline as is available online, by rolling out local neural machine translation for 59 languages to the Google Translate app for iOS and Android.
Real Life Machine Learning Applications-
1. Image recognition-
It is an approach for identifying and detecting a feature or an object in the digital image. Moreover, this technique can be used for further analysis, such as pattern recognition, face detection, face recognition, optical character recognition, and many more.
2. News Classification-
Interesting category of news to the target readers will surely increase the acceptability of news sites. Moreover, readers or users can search for specific news effectively and efficiently. There are several methods of machine learning for this purpose, i.e., support vector machine, naive Bayes, k-nearest neighbor, etc. Moreover, there are several “news classification software” is available.
3. Email Classification and Spam Filtering-
To classify email and filter the spam in an automatic way machine learning algorithm is employed. There are many techniques, i.e., multilayer perception, C4.5 decision tree induction, are used to filter the spam.
4. Speech Recognition-
Speech recognition is the process of transforming spoken words into text. This field is benefited from the advancement of machine learning approach and big data. In a machine learning approach, the system is trained before it goes for the validation.
5. Online Fraud Detection-
It is an advanced application of machine learning algorithms. This approach is practical to provide cybersecurity to the users efficiently.
All sort of forecasts can be done using a machine learning approach. There are several methods like Hidden Markov model can be used for prediction.
7. Services of Social Media-
Social media uses machine learning approach to create attractive and splendid features. For eg.,people you may know, suggestion, react options for their users. These features are the result of the machine learning technique.
If you’re interested in adopting Machine Learning technology for your business, then you might need some help getting started. Solace team is there for you to start. Dedicated developers of solace will be more happy to help you for development of ML system and set you on your way to business innovation. Contact us to get effective ML system that will help you to stand out in a growing market.