Machine Learning in Python and R Programming


Machine learning is a category of an Artificial Intelligence (AI). The primary goal of machine learning is to enable the computers to learn from past and current examples without human intervention automatically and to predict the future based on its experience. When a new data is fed into the machine learning algorithms, they learn and predict the future by developing ‘intelligence’ over time. There are four types of popular machine learning algorithms available, such as supervised, semi-supervised, unsupervised, and reinforcement learning. Recently, the machine learning algorithms are more popular than ever, due to the adaptation of learning algorithms on the new and dynamically changing environment. For huge volume and variety of available data, the machine learning provides accurate analytics and prediction algorithms with affordable data storage. The machine learning algorithms support an organization to build a precise model for future prediction and future profitable opportunities.

The machine learning is applied in a wide range of applications today.

  • Machine learning plays a vital role in the automotive, for instance, self-driving cars. The machine learning algorithms are primarily applied to the identification of objects and automatic emergency response systems that can make driving decisions without human intervention.
  • News Feed is one of the best examples of machine learning. To personalize each member’s feed, the News Feed applies the statistical analysis and predictive analytics on friends’ activity earlier in the feed. The identified patterns are utilized in populating the News Feed.
  • The machine learning is also applied in marketing new products in banking sectors, fraud detection, government pattern recognition in images and videos for security and threat detection.
  • Retail Micro-segmentation and consumer behavior analysis using machine learning algorithms tend to instantaneous customized offers.

Research Fields

  • Automation and Control Systems
  • Big Data Analytics
  • Communications
  • Cyber Security
  • Fraud Detection
  • Emotion Classification
  • Image Quality Assessment
  • Internet of Things
  • Opinion Mining
  • Recommendation System
  • Search Engine
  • Security
  • Sentiment Analysis
  • Signal Processing
  • Sequence Mining
  • Social network Security
  • Storage Security
  • Telecommunication
  • Time Series Forecasting
  • User Behavior Analysis
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