Category: Machine Learning

What are Neural Networks? | Strong and Jovial Plain Text

The human brain is an impressive feat of cognitive engineering, giving us the upper-hand when it comes to coming up with original ideas and concepts. We’ve even managed to create the wheel – something that not even our robot friends could do! This shows just how far we’ve come in terms of evolution – proving that humans are true masters of invention.

The Fundamentals Of Machine Learning

The main purpose of ML (machine learning) is to create an automatic data model for the purpose of analysis. Thus ML is to create a system that can learn from the data according to the algorithm used. The result can be found by mapping the output to the input or finding patterns/structures or learning by rewarding/punishing.

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Machine Learning – Challenges of Supervised Machine Learning

Supervised machine learning through historic data sets is able to hunt for correct answers, and the task of the algorithm is to find them in the new data. It uses labeled data with input features and output labels. The program uses labeled samples to identify correlations between input and output data. Output labels in supervised learning are called the “supervisory signal”.

Artificial Intelligence – Some Basic Pointers

The current form of artificial intelligence is purely preliminary software. Scientists believe that this technology is still in the beginning stages, and further developments can have the capacity for changing the world. The current technology or weak AI can easily surpass human capabilities, while its more advanced forms have the power to make human workers obsolete.


Machine Learning -Basic Terminologies in Context

The Machine Learning hype, too much information on the internet, and using ML terms by almost every tech show have actually created a “misinformation epidemic” of ML.  It is revolutionizing the way we do our business and what should be done to improve upon it. ML develops its own encompassing strategy from the experience it comes across over the period. Mathematics, statistics, programming, and the common sense of human beings are now part of integral components of machine learning.

Machine Learning – Changing Payments Security Landscape

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.

Machine Learning an underlying technology for PaymentIntelligence

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.

Top Machine Learning Algorithms – Data Scientist Basic Tool Kit

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.

Supervised vs Unsupervised Machine Learning

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.

Deep Learning – Backpropagation Algorithm Basics

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.

Machine Learning – Introduction to Regression Analysis

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

Machine Learning – It all Boils Down to the Training Data

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.

Machine Learning (ML) - Everything You Need To Know

Machine Learning(ML) – Introduction to Basics

Machine learning techniques are accelerating almost daily to bring good value to the businesses of today. It is revolutionizing the way we do our business and what should be done to improve upon it. ML develops its own encompassing strategy from the experience it comes across over the period.

Reinforcement Learning

Machine Learning – Introduction to Reinforcement Learning

Reinforcement learning is closely related to dynamic programming approaches to Markov decision processes (MDP). MDP solve a partially observable problem. POMDPs received a lot of attention in the reinforcement learning community. As its a process of discrete-time stochastic control to provide a mathematical framework for decision-making modelling.

Understanding Naive Bayes -Everything You Need To Know

Naive Bayes Algorithm – The basics you need to know

The naive Bayes algorithm is a method set of probabilities. For each attribute from each class set, it uses probability to make predictions. This algorithm falls under a supervised machine-learning approach. The data model that comes out of this effort is called as “Predictive Model” with probabilistic problems at the foundation level