Detailed Guide on Machine Learning
The world of artificial intelligence has always been of intrigue to humans. To make things captivating, a regular effort has been made for increasing the capacity and to continually improve the systems in a bid to add on something extra to human life. After an initial slump, the pace in the progress of artificial intelligence has picked up. Several artificial intelligence technologies and processes such as deep learning, machine learning, and deep mining are now changing the landscape in healthcare, business, research, finance as well as in other similar industries to help in transforming lives globally.
We all are familiar with the fact that learning is a universal skill/trait that is acquired by any living organism on this planet. It is a phenomenon that is accompanied by the acquisition of skills or knowledge through experience. Whether it is an animal that is learning on how to peel a banana or humans learning about riding a car, the process of learning cannot be neglected.
But what if I will tell you that, “Machine too can learn and perform.”
Since we are living in the age where machines are no different therefore we guide them on how to learn and perform different operations. This process of allowing machines to think and perform is called as ‘Machine Learning’. Let gets to know in detail how exactly the process works and why it is gaining so much popularity.
What is Machine Learning?
In simple terms, machine learning is the process by which a computer system learns to perform a particular task with the help of computational statistical models and algorithms. This allows the system to develop methods to do a set of similar tasks without getting specific instructions and commands such as email filtering, developing a computer vision and making predictions. Machine learning is, therefore, a mechanism by which artificial intelligence trains itself. Machine Learning (ML) is the current application of Artificial Intelligence around the idea of instrumenting machines with data that enables them to learn by themselves. It has become more and more practical, less time consuming than ever before to implement applications that uses Machine Learning.
Thanks to the emergence of internet, number of services/frameworks available and several ways to use training algorithms to create self-learning models. At Intellectyx, we have perfected the art of balancing readily available tools, framework vs building complex ML algorithms for any use case.
In general, the approach for ML is defined by the number of outcomes – meaning if its binary (one of two possible outcomes), multiclass (one of more than two outcomes), and regression (predicting a numeric weightage towards outcomes).
Application of Machine Learning
There are several applications of machine learning in our daily lives. Computer vision is a method where a system develops a human-like understanding on the basis of pictures and videos, leading to better ways of processing data. Image search has become a new way of how people look up for information online. In the age of social media, where news is formed, received and disseminated by it, image and video search have become an important way to verify the veracity and locate the source of news for the journalists.
Scientifically, machine learning can be used for a variety of medical purposes like gene therapy, cancer treatment, predicting patient diagnostics. Several other application of machine learning may include –
- Prediction of fraudulent activity on a credit card in the banking sector.
- Prediction of customer behavior in the case of e-commerce.
- Prediction of match preferences on a dating website or other similar social media platforms.
Types of Machine Learning
1. Supervised Learning
Machines learn in several different ways, with the primary ones being supervised or semi-supervised, and unsupervised and reinforced learning. Supervised learning is based on labeled data i.e. chunks of data with specific tags and markers to give meaning or more information. The system then based on these labeled markers creates algorithms and models. For instance, based on an input of photograph, one can caption image tagging options as the desired output. The system then develops models to predict the right image tag when a similar set of photograph and caption is fed to it. Voice recordings along with transcript can help the machine learn speech recognition.
2. Unsupervised Learning
Semi-supervised systems miss some of the input or output vectors to train the system. An unsupervised machine learning system works by using the principle of cluster analysis and dimensionality reduction. In the cluster analysis method of machine learning, the system uses a set of input data and groups it by finding similarities or structures in it. Statistics makes use of this method for data analysis. Data visualization is a technique where complex data is converted into simple, crisp, images that present the required information while excluding the long and boring text.
3. Reinforcement Learning
Reinforcement learning is an area of machine learning which works on the principle of the utilitarian concept of pleasure and pain. The system uses a trial and error method where the aim is to reward the system if it performs the desired output and to penalize it if the task is done incorrectly. The remarkable achievement where AI systems have defeated the world’s best chess and Go, players would have not happened without the use of machine learning techniques. Reinforcement learning is used in computing result of models involving multiple agents and outcomes such as in game theory.
Why Machine Learning is Important?
In the modern day world, machine learning is impacting human lives in so many ways. From Facebook’s face recognition to helping you tag photos, or signing in to Google mail account and making an automatic reply to the email – it is now extensively being employed in security systems globally. Dubai, in fact, has unveiled its first Robocop, which can read license plates and can detect potential risks such as unattended bags, all this by reacting in six different languages. Uber uses machine learning to show its customers the shortest route along with travel time and the fare.
Machine learning has the potential to surpass human minds in the field of innovation and new discoveries by using data of all past inventions and research papers which is an impossible task for human scientists. Machine learning in the coming years is expected to take a central role and is set to change human lives forever.
Difference between Machine Learning and Deep Learning
People often take machine learning and deep learning as the same concept and use them interchangeably. However, while both are related, machine learning uses a task-specific algorithm whereas deep learning is based on data representation and architecture methods such as deep neural networks, deep belief network, and recurrent neural networks.
Deep learning is inspired by information processes and communication patterns in the animal brain. Similar to the functioning of neurons and synapses in the human brain. A unit of artificial neurons called ‘Artificial Neural Networks’ or ANN uses binary numbers 0 and 1 to communicate the signal between two artificial neurons via a connection popularly known as a Synapse. When organized in several layers these form a network which then resembles that of the neurological system of a human brain. Such a system may learn to differentiate between a cow and a bird by viewing different pictures of both labeled as “cow” and a bird tagged as “no cow”.
‘Deep Neural Networks’ or DNN is a multilayer ANN system which contains multiple levels of input and output. Each layer of neurons turns input into a given output which is then further processed by the next unit in the network as an input. After mathematical computations by each subsequent layer, a final outcome or decision is achieved. A deep neural network is trained to breakdown an image into different features, traits; identify trends to discern every image. For instance, a DNN system may be asked to identify a specific type of cat. And to do so each layer may discern specific features like size, followed by color to discern the breed.
Recurrent neural networks are special networks in which data can flow in either direction. This allows for back looping process to take place, thus making them efficient at performing more complex tasks. Therefore, while machine learning process can be termed as more logic oriented, a deep learning system tries to incorporate behavioral and neurological aspects of the brain trying to create the right environment and context to solve the required problems.
Future of Machine learning
Even as there are great potential and scope for Artificial intelligence and its processes such as machine learning, deep learning and data mining in shaping our lives, there are certain concerns with these computational processes and their access to various aspects of our lives as well. The whole debate on privacy and interference in voting during Brexit and US presidential elections by the firm Cambridge Analytica raises certain serious ethical questions. Biased algorithms such as one which racially profiles the prospective candidates for a job vacancy can further and deepen discrimination.
While a Matrix film like the doomsday scenario of Artificial intelligence ruling the human race is unlikely and yet there is an ongoing debate about how much control we would like to give it. However, the positives of Machine learning cannot be overlooked as well. The ongoing democratization of the cloud computing services by various platforms has further strengthened the positives of this amazing technology.
Every single minute more and more machine learning systems are being employed globally. The corporate world is now using cloud services to not only keep their data safe but also use machine learning tools for processing language, speech, images on Google cloud. Similar services are being launched by Amazon Web Service Cloud and Azure to make IT operations smoother.
Many companies are also using available data to make more algorithms for different tasks. In the coming days, we will see the use of machine learning to further quicken the app process to save battery. Health gadgets and fitness trackers are more likely to see an improvement as more models come to measure the biological process, changing the present healthcare scenario.
Skype’s real-time speech translator provides conversion to seven different languages. As the language translation and system models improve further and the process speeds up, we will see the end of language barriers as more and more video calling services will start adopting similar applications. The whole of the world’s giants is investing in self-driving cars powered by artificial intelligence based technology.
More online retails platforms such as Amazon and Jabong use machine learning techniques such as the use of the past history of purchased items and wish list to predict customers needs and show results accordingly to optimize the experience and save time. Various researches have used machine learning and algorithm method to predict climate change and disasters induced by it in order to save lives.
How Intellectyx can help with this?
We at intellectyx believe that in this age of Artificial intelligence, quantum technologies, and robotic process automation, there is massive potential for a company to harness the power of MI technology for any product development. The key to this is in using the core fundamental technologies to abstract the level and push ourselves to the maximum to add value to the process.
Comprised of the right set of people, our experienced team of professionals promises to develop smart solutions and deliver the right service at the right price thereby lowering the costs for our clients. When a customer came with a problem concerning the unstructured data (email and file) so as to makes decisions based on fact and set policies in order to keep them secure. The proposed Artificial intelligence and machine learning system had to ensure that every file and an email containing sensitive information related to businesses, had to be classified based on user-provided classification rules, regulatory requirements, data, policies, and environmental attributes.
We hereby accept all the challenges and develop an automated General Data Protection Regulation or GDPR compliance model for email and file archiving processes. The system uses Natural language processing to understand the context in emails and data within the files. Allowing all our customers to analyze the content, attributes, user context, compliance rules and classifies documents, email attachments or prompts to manually classify with the Semi-supervised machine learning model further allowing them to mark documents and emails with a confidential label.
It also allows, for central configuration and deployment of the model with classification rules, attributes, tags, and policies. The Application programming interface or API model developed by our team has allowed seamless integration of the services with their existing products. Intellectyx is committed to our customer’s requirements and continuously developing new ways to develop new processes to provide unique solutions in the world of Artificial Intelligence and Machine Learning technology.
Some of the use cases currently involved are
- GDPR compliance Deep Learning Platform
- Conversational Platform for Behavioral and Relevance Marketing solutions
- Device based Machine Learning Model for Usage/User Analytics
- Forecast/Projections in Real Estate market
- Life Time Value (LTV), Risk %, Claim Loss Analysis in Financial and Insurance solutions
- ML based fraud detection, Anti-Money Laundering (AML) detection across large data
- Machine Learning based algorithm for predicting future trends in hedge fund or investment portfolio management
- ML based improved customer experience by measuring and analyzing behavior data
- ML powered marketing platforms using omni-channel data, behavioral data and usage data across web, mobile and campaigns
- Machine Logic based Data Quality algorithm to manage and monitor Data Governance and Quality initiatives