Though AI has been around for over 60 years, advances in computing and access to data have allowed many industries to exploit the potential of machine learning algorithms.
After a fresh round of funding of about Rs 2,200 crore, this innovation hub plans to hire Machine Learning (ML) and AI professionals and build cutting-edge technologies for its products
The service runs on AWS and delivers high-performance and scalable analytics as well as end-to-end, in-database machine learning to organizations
This paper covers five stages of the machine learning process: what to do before building the model, reliably building model, model evaluation, fairly comparing models, and reporting the results.
Sanmay has worked with the US Treasury department on machine learning approaches to credit risk analysis, and occasionally consults in the areas of technology and finance.
Is it time to bid farewell to bias-variance tradeoff when it comes to machine learning?
We will soon announce new hardware partners who have joined us on our journey toward machine learning efficiency.
A robust machine learning model is required to detect rumours on social media.
Facebook is building a machine learning chip to manage content recommendations for its users.
The key highlights of this two-day action-packed applied AI conference were the exciting Advances in AI, Customer & Product Sciences, and Data Science Platforms & Analytics tracks which touched upon every aspect in the field of artificial intelligence, machine learning, and data science.
Here, we will explore some of the fascinating yet underestimated concepts in machine learning.
Matrix multiplication is among the most fundamental and compute-intensive operations in machine learning.
As part of this series, we’ll review several fascinating yet underestimated machine learning concepts.
If 80 per cent of machine learning work is data preparation, then ensuring data quality is the most important work of a machine learning team
With the fast development in the machine learning frameworks, comparative approaches are being created within the capacity of ML engineering, which handles the special complexity of the practical application of machine learning.
As part of this series, we’ll review several fascinating yet underestimated machine learning concepts.
R-CNNs ( Region-based Convolutional Neural Networks) a family of machine learning models Specially designed for object detection, the original goal of any R-CNN is to detect objects in any input image
Students in these institutions will learn about cloud architecture, data analytics, cybersecurity, artificial intelligence, and machine learning.
Scikit-learn, a free software machine learning library for the Python programming language, has announced the latest Scikit-learn Version 1.0 with major updates and fixes.
Mayflower Autonomous Ship is equipped with three layers of technology — sensory inputs, real-time machine learning and analytics, and a decision engine.
The first-hand data is usually messy and comes from different sources and distributions. To feed them into the machine learning model they need to be standardized and cleaned up.
In the era of Data Science where knowledge of programming languages like Python and R is essential to implement the fundamental algorithms and techniques related to Machine learning and Data analytics.
Only 3 percent possess skills that are needed for jobs in emerging tech such as AI, machine learning, etc.
Kissht uses machine learning-based statistical models to predict customers’ ability to repay loans and detect frauds.
Compared to most other machine learning models, foundation models are characterised by a vast increase in training data and complexity
Images consist of a lot of pixels that help retain their clarity. Still, as the number of images to process increases its size, it can significantly slow down the system’s performance. We can use the Image Reconstruction technique to overcome this situation, which comes under Unsupervised Machine Learning.
As the world reels under acute chip shortage, using AI and machine learning techniques for chip design seems to be a possible solution.
Transparent Taxation Platform uses data analytics, artificial intelligence and machine learning (AI/ML) against tax fraud and tax evaders.
Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm. It has quite effective implementations such as XGBoost as many optimization techniques are adopted from this algorithm. However, the efficiency and scalability are still unsatisfactory when there are more features in the data.
LazyPredict is an open-source python library that helps you to semi-automate your Machine Learning Task. It can build multiple models without writing much code and helps understand which models work better for the processed dataset without requiring any parameter tuning.
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