We have tons of data and it’s increasing every second and millions of tools but how to harness the power of modern data and related delivery tools, is still a question. It’s even more difficult now in the era of GDPR or similar regulations. Reducing the cost and simplifying data intelligence infrastructure going to be our next biggest challenge at least for the year 2020. Few questions at hand like below
Ministry of innovation adding lots of excitement and innovation thus increase in a fire about the security & privacy of transactions. Subscriber normally doesn’t ask too much in the low-value transaction but as it happens on a daily basis subscriber do get nervous and freak out more when they add their card or bank details on provided channels i.e. mobile app and web portal etc. Sadly data privacy is still in the back seat for many many providers.
PaymentIntelligence – In today’s time payments are devoid of post hoc analysis, despite the fact that our money has transformed from being tangible to mere electronic data. Our contemporary milieu, as well as our daily lifestyles, spending patterns, and myriad activities, are becoming increasingly intricate. Incorporating machine learning and data science into real-time payment systems has the potential to yield intriguing insights into the behavior and culture of the payment industry, which may be identified as “payment intelligence.
The financial technology sector persistently advances and enthusiastically adopts new technologies in order to enhance its self-development. The integration of financial technology with human transformation-capable technologies has precipitated significant progress, extensive adoption, and considerable accolades. Machine learning is the hot new thing that’s shaking things up, and it’s all thanks to artificial intelligence.
Learning Machine Learning skills is widely seen as a game-changing advantage for organizations, especially those with data-driven operations, as it has the potential to provide significant benefits. Nowadays, the most common term used to describe digital communication tools is social media platforms. The main aim of this written communication is to explicate and exemplify the prominent machine learning algorithms.
How AI and ML are now changing the work style and methodologies of marketing. Digital marketing cannot be run as just a computerised version of the traditional market or print media. The main and exciting use cases of data science and big data analytics are exciting and make the marketing job even more exciting. Some may say it’s getting more of a technology job as now it involves predicting, prescribing, planning, and forecasting.
Supernovae emerge as stars that have exhausted their nuclear fuel face an existential crossroads. The gravitational forces that have sustained these celestial giants over eons suddenly yield to their own colossal weight, triggering a cataclysmic collapse.
AI Elevated: Amidst Conversations on Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Artificial Neural Networks (ANN), the lexicon employed often intertwines these phrases, causing ambiguity. Nevertheless, each expression possesses distinct connotations, delineations, objectives, and interrelationships.
Supervised vs Unsupervised Learning – Both of them are very popular machine learning algorithms currently in use. Supervised learning is all about labelled data where its algorithm does reverse engineering work as compared to traditional programs i.e. it predicts/maps the output to the input data. Unsupervised is all about hidden patterns and structures from unlabeled data.
Backpropagation Algorithm – An important mathematical tool for making better and high accuracy predictions in machine learning. This algorithm uses supervised learning methods for training Artificial Neural Networks. The whole idea of training multi-layer perceptrons is to compute the derivatives of the error function or gradient descent concerning weights using the backpropagation algorithm. This algorithm is actually based on the linear algebraic operation with a goal of optimising error function by harnessing its intelligence and provisioning updates.
In Machine Learning regression analysis is used just to understand how to establish a relationship between independent variables and dependent variables. How independent variables going to effect dependent variables. Whether there is a strong relationship or just a casual relationship between IV and DV. Machine learning is a goal not a technique in this regard while linear regression is. ML can be achieved through many different means and techniques. In short, we can say this
Data generation sources like social media as 1st and winners are doing an excellent job. 2nd to this is payment data which is as big as social media or the Western world. Payments on mobile for e-commerce, online food orders, etc are almost 30 – 50 times more than in the U.S. as in Africa and Asia combined above. Off-course all this data is quality data for making more money as well as to improve the user experience. Data is also used as a yardstick for comparing algorithms.
“Artificial neural networks (ANNs) are biologically inspired computing code with number of simple, highly interconnected processing elements for simulating human brain workings to process information model”.
Artificial neural networks are a type of computing model that takes inspiration from the structure and function of the neural networks found in the human brain. Nevertheless, machine learning has yet to attain authentic biological accuracy, given the present level of implementation and utilization. The process entails receiving multiple inputs and generating a single output.
The use of digital marketing analytics is crucial in generating insightful data that aids informed decision-making in business. The conducted investigations hold meaningful implications that can be translated into meticulously crafted hypotheses. Marketers are utilizing various theories related to big data analytics to create and refine significant performance indicators for businesses. Businesses could reap numerous benefits from a sudden upswing in website traffic, such as the potential acquisition of fresh business leads, bolstering of brand identity, and stimulation of customer engagement.