Identifying Behaviour Patterns using Machine Learning Techniques [Video]

More Information
Learn
  • Understand K-Means Clustering to detect network traffic.
  • Feature Normalization and Categorical Variables.
  • Analyzing Time Series data using Clustering.
  • Verifying and Validation of Model.
  • Identifying Patterns using in time-series data using GMM.
  • Explore explanation of Hidden Markov Model Explanation. 
  • Using HMM for defining transitions between states.
About

Nowadays web-sites needs to handle huge amount of traffic. We can leverage that fact and capture user interactions with the application. For further analysis.
Next, we can analyze users behavior and capture patterns on which we are able to react properly.

In applications that needs to deal with huge amount of traffic it is very hard to detect anomalies. We’ll learn how to apply clustering to find anomalies in web
traffic. Next, we can analyze users behaviour and when they tend to do on our application using time series data. We will be using GMM clustering technique to achieve
that.

On the e-commerce sites we want to predict when and what user wants to buy in the future. We can use the Hidden markov Model to find transitions between states and
find the transition with highest probability.

Style and Approach

This course will start with clustering that will help to detect network traffic and analyze users behaviour using time series data using Gaussian Mixture Model. By the
end, viewers will be able to predict users behaviour using Hidden Markov Model and understand highest probability.

Features
  • Learn to employ clever ML algorithms to find patterns in user behaviour.
  • Analyze users behaviour on e-commerce site using Machine Learning Techniques.
  • Use Gaussian Mixture Model to find a pattern in time series data.
Course Length 1 hour 12 minutes
ISBN 9781788621885
Date Of Publication 21 Sep 2017

Authors

Tomasz Lelek

Tomasz Lelek is a Software Engineer and DevOps. He has been working as a software engineer with key operational management duties for tens of microservices infrastructures for 6 years leveraging Kubernetes from the beginning (2014) and Docker for over 6 years. In addition, he has worked in the cloud ecosystem with hundreds of deployments automated using Kubernetes. Moreover, his expertise encompasses implementing the automation of rolling deployments of services that had zero downtime during their new release. He has created several courses about automation using Kubernetes (all courses are available at https://github.com/tomekl007/Packt_Publishing_courses_by_Tomasz_Lelek). He has conducted multiple conferences and presented on topics including Java and JVM-related technologies.