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Machine Learning For Big Data

This web-based training course on Machine Learning For Big Data functionality, administration and development, is available online to all individuals, institutions, corporates and enterprises in India (New Delhi NCR, Bangalore, Chennai, Kolkatta), US, UK, Canada, Australia, Singapore, United Arab Emirates (UAE), China and South Africa. No matter where you are located, you can enroll for any training with us - because all our training sessions are delivered online by live instructors using interactive, intensive learning methods.

The growth and spread of Machine Learning and data science have been extremely widespread and rapid in the recent ages with a large number of domains in the industry and research being driven by it across science, engineering, commercial applications and much more. Technologies such as search engines, recommender systems, advertisers, financial institutions, and other such technologies employ algorithms of machine learning at all the levels. These are leveraged to predict human entity behavior, compliance, risk management and more. The data available today is astronomical in quantity and complexity as well. It requires immense processing and manipulation abilities in order to effectively process data and provide quantifiable and quality results. Big data has been the fuelling the digital race for quite some time now and machine learning has added the requisite amount of charge for further improving the data processing and decision making methodologies. With machine learning as employed with big data, data models and processing techniques have improved manifold.


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Course Details

In this machine learning with big data online training course, we explore the various statistical modeling methods which relate to machine learning and use big data manipulation techniques. The course is structured to provide a practical understanding about the machine learning methodologies and a number of real life examples, situations and implementations are provided and explained in detail. The various popular algorithms of classification, regression, clustering and dimensional reduction are explained in detail along with popular models of tran/test split, Root mean squared error and random forests. The course explores machine learning from the perspective of big data and inculcates the fundamental understanding of the subjects in the trainees so that they may effectively work with the various data modeling platforms and languages such as python and Hadoop. This course has no pre-requisites and only interest in the domain of data sciences and basic understanding of mathematics and statistics will suffice.


  • Overview of machine learning
  • The various Machine Learning languages, types, and their examples
  • Comparing Machine learning and statistical modeling
  • Features, feature vectors, linear classifiers in machine learning
  • On-line learning algorithms used in machine learning
  • Non-linear classification and regression techniques
  • K-nearest neighbor technique
  • The methods of Overfitting, regularization, generalization
  • Collaborative filtering and recommender problems in machine learning
  • Supervised and Unsupervised learning techniques
  • Implementing Neural networks and deep learning
  • Dense vector representations in machine learning
  • The concept of Bias variance trade-offs
  • Different evaluation methods to implement
  • Recurrent neural networks and their implementation
  • Unsupervised learning and mixtures for machine learning implementations
  • Decision trees and random forests to be used
  • K-means clustering, hierarchical clustering and density based clustering and their implementation
  • Reinforcement learning techniques
  • Practical guide to machine learning and applicative understanding
  • Dimensionality reduction techniques
  • Collaborative filtering and its challenges faced

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