Machine Learning: Algorithms, Models, and Applications

Abstract

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.

Published by IntechOpen, London Uk in Dec 2021. the book contains 6 chapters spanning over 154 pages

References in corpus (10)

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• Twitter mood predicts the stock market
• DeepPose: Human Pose Estimation via Deep Neural Networks
• Human pose estimation via Convolutional Part Heatmap Regression
• Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models
• Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries
• Predicting stock market movements using network science: An information theoretic approach
• Analysis of Research in Healthcare Data Analytics
• Evaluation of Predictive Data Mining Algorithms in Erythemato-Squamous Disease Diagnosis
• A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector

Cited by in corpus (1)

• A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms