Machine learning is a self-evolutionary interactive field that is designed to incorporate the functionalities of artificial intelligence (AI) and cognitive computing through a series of algorithms. Machine Learning is used to understand the relationship between big data and is aimed at obtaining a certain desired output. Machine Learning has revamped the way we interact with technology. Countless examples of everyday things like self-driving cars, self-tuned databases, automated surveillance systems, humanoids and smart assistant perfectly describe how machine learning is what makes modern machines smart the way they are today. The global machine learning as a service market was valued at $571 million in 2016 and is projected to reach at $5,537 million by 2023, according to a report published by Allied Market Research.
I. Supervised Learning
Supervised Learning is a form of machine learning that takes help of a large pool of examples. It is used in a number of modern public platforms for AI-based app development. To make the learning process work, a set of well-analyzed examples are loaded into the machine along with relevant information like geolocation data, dates, and specific characteristics of the object analysis. This is followed by generation of a number of templates on the basis of these examples and information.
II. Unsupervised Learning
In the case of unsupervised learning, the machine does not receive any specific input data and mostly fails to detect any certain pattern in the user’s request, independently. It does not receive any information from outside. The activity here is based solely on the experience of working with a particular user.
III. Reinforcement Learning
Machine learning with reinforcement includes the process of gaining the knowledge provided by the developer and through one’s own experience. These algorithms are difficult to implement.
With endless possibilities and benefits, Machine Learning has gained massive popularity among users today and rightly so. A plethora of conveniences that allied themselves with Machine learning, make it popular amidst modern app developers. More often than not it becomes difficult for individual developers to develop the machine learning features from the scratch due to various challenges like in-depth mathematical concepts, fragmentation of frameworks and lack of debugging tools etc. To rescue developers from these hassles and streamline app development processes, tech giants like Google, IBM, Microsoft, and AWS are offering open-sourced AI/ML libraries and tools. Here are a few examples of resources a developer can use to build an app with machine learning:
An open-source software library by Google, TensorFlow helps a developer in performing numerical computation using data flow graphs. Easy to use, TF empowers the app developers to execute computation to numerous CPUs/GPUs in all devices with a single API.
A popular Machine Learning framework, CoreML enables an iOS app developer to integrate wide-range of machine learning models into their applications. It offers a high level of performance excellence and efficiency to an app product.
C. Microsoft Cognitive Services
Microsoft Cognitive Services toolkit is a developer’s asset. It helps build smart apps that can see, listen, understand, interpret, and speak using natural communication means like facial expressions, the medium of speech like words and gestures.
D. TCS Ignio
An ML-based self-learning platform designed to automate and optimize IT operations, Ignio is a developer’s favourite. It grasps the information from its surroundings, lowering down the chances of knowledge gaps across operation teams and technologies. It also resolves common errors on its own.
Created by the Google development team Api.ai uses contextual dependencies to create business solutions for iOS and Android personal assistants. The creators of Api.ai strengthened their algorithms with a powerful database.
Additionally to all functions of Api.ai, Wit.ai features tools that convert speech into printed text. It is capable of analyzing context-sensitive data and, thus, generating the most accurate answers to requests.
G. Amazon Machine Learning services
With AMLS, developers can develop ML models using visualization tools and wizards. It spoils developers with a choice of APIs for obtaining predictions for mobile application without executing custom prediction generation code.
Machine learning is a multidisciplinary field and finds its implementation in technologies, science, and businesses.
Machine learning can revamp the field of e-commerce by opening up new opportunities for revenue generation and improved customer experience. Product search, promotions and product recommendations are some of the main areas in e-commerce apps that can use machine learning algorithms for an enhanced customer experience. Machine learning algorithms gather and analyze the customer behavioural data collected during the searching and purchase process, through a user’s search history, business target and semantic outcomes and work towards serving a customer better. Ebay and Amazon are two retail giants that are working with Machine learning to enhance the way they serve their customers. We at Promatics, help small e-commerce businesses apps ace machine learning as well
Machine Learning is also revolutionizing the fitness industry. Modern machine learning retrieves personal details of the users and serves according to a person’s personal needs and physical conditions. With Machine learning, coaches create a workout routine for their clients as per their goal and body capability.
When it comes to healthcare, machine learning can streamline the process of manufacturing/discovery of a new drug along with making it cost effective. We can also differentiate between different kinds of genetic markers and genes and aid patients in providing personalized medication/treatment. Disease diagnosis, robotic hair transplantation, epidemic outbreak prognosis, and smart health electronic record are other areas which can benefit from machine learning.
Machine Learning has taken the entertainment industry by stride. Recommendations of content based on the personal interest of users, helping entertainment providers create content that’s in demand, curating home pages that increase user engagement, machine learning has driven advertising and marketing drives too.
The wide applications of Machine Learning in various fields include education as well. It aids in understanding the concepts easily through real-time translation. Machine learning can help your device provide bot personal tutor services. Chatbots provide the answer to students’ queries, assess assignments and mark them with scores/grades based on their performance. Machine learning can also provide a broader and far-sighted perspective by suggesting better learning techniques and material to the students based on data collected.
The superior machine learning algorithms can predict future trends, help with portfolio optimization and send recommendations right when the market is right. Finance centric businesses take help of machine learning to analyse customers’ previous transactions, social media activities or borrowing history to reach onto their credit rating. Machine learning has also made it possible for masses to access personalized financial customer services at lower cost, along with higher revenue scope and better compliances.
An insight into the possible issues and problems that the companies face can help you avoid the same mistakes and use machine learning in better ways.
1. Too Much Focus on Algorithms and Theories
As a developer, you are fortunate enough to have open-source app libraries to save you from utilising your time in developing algorithms from scratch. Just pick out pre-developed algorithms from these libraries and use them to enhance the UX your app in wonderful ways.
2. Trying to Master All of ML
If you are a business minded app developer, you don’t have to aspire to master all aspects of machine learning that is a vast study in itself. Just identify the issues which you will be solving and find the best model resources to help you solve those issues.
3. Letting Algorithms Become Obsolete When Data Grows
ML algorithms require much data when they are being trained. A model which is “accurate” for a data set may no longer be as accurate for a set of data changes. A system that changes rapidly, has a less accurate rate for ML algorithm if past data no longer applies.
4. Making the Wrong Assumptions
ML has algorithms that run over fully automated systems. In the process, they have to deal with missing data points. The algorithm ends up using mean value as a replacement for the missing value. This application supplies reliable assumptions about data including the particular data missing. The best way to tackle with this issue is to make sure that this data is delivered on a substantial amount of assumptions.
5. Having Bad Data Convert to Bad Results
App developers need to understand that not all data is relevant and valuable. If data is not well interpreted, ML results also tend to fail a users expectations. Data scientists need to initiate tests using unforeseen variables during the development process. This acknowledges them about the chances of any possible outcome. Start and end your ML development project with high-quality data.
With Machine learning world has witnessed a new revolution in software development that offers superior and intuitive user experience. Machine Learning is a very vast concept and a fruitful investment for every modern business, as it allows you to implement completely new features into the app. If you are a business app owner wanting to inculcate machine learning elements into your existing application, we are your one stop shop solution.