Applications of Machine Learning

Pp: 19-44 (26)

* (Excluding Mailing and Handling)

Abstract

In this chapter, we briefly discuss various real-time applications of machine learning algorithms. Machine Learning Algorithms explain the following topics: Introduction to ML algorithms, Supervised Learning, Classification, Regression (Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, Support Vector Machine, Random Forest, AdaBoost, Gradient-Boosting Trees), and Unsupervised Learning (K-Means Clustering, Gaussian Mixture Model, Hierarchical Clustering, Recommender Systems, PCA/T-SNE). Application of Machine Learning explains various real-time applications like augmentation, automation, finance, government, healthcare, marketing, traffic alerts, image recognition, video surveillance, sentiment analysis, product recommendation, online support using chatbots, Google translate, online video streaming applications, virtual professional assistants, machine learning usage in social media, stock market signals using machine learning, auto-driven cars, and real-time dynamic pricing.

Keywords: Machine-Learning, Supervised learning, Un-Supervised learning, Naive bayes, Support vector machine, Random Forest, AdaBoost, Gradient-boosting.

Cite as