Study Learn Grow
Identifying Behaviour Patterns Using Machine Learning Techniques

Identifying Behaviour Patterns Using Machine Learning Techniques

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.

Overview

Description
Learn to identify behaviour patterns based on the user actions on the web site using ML techniques

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.

About the Author

Tomasz Lelek is a Software Engineer, programming mostly in Java, Scala. Fan of microservices architecture, and functional programming. He dedicates considerable time and effort to be better every day. Recently diving into Big Data technologies such as Apache Spark and Hadoop.
He is passionate about nearly everything associated with software development. Thinking that we should always try to consider different solutions and approaches before solving a problem. Recently he was a speaker at conferences in Poland - Confitura and JDD (Java Developers Day) and also at Krakow Scala User Group.

Course Information

Software Engineers with professional Experience in Scala and Java

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

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.

• Lifetime Access to Each Course
• Certificate on Completion of Course
• No Extra Charges Or Admin Fees
• Easy Access to Courses
• High Priority Support After Sales.
• Big Discounts on Individual Courses

Course Specifications

IT and Computing courses are available to study on our learning platform. 

See All Courses

Adult education is the non-credential activity of gaining skills and improved education. 

See All Courses

Online education is electronically supported learning that relies on the Internet for teacher/student interaction. 

See All Courses

A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

See All Courses

Course duration is 24 hours.

See All Courses

Study Learn Grow

Related Jobs