Learn to identify behaviour patterns based on the user actions on the web site using ML techniques
About This Video
- 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.
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.
Table of contents
Chapter 1 : Anomaly Detection with K-means Clustering
- The Course Overview 00:01:56
- Anomaly Detection 00:05:05
- Analyzing and Loading Input Data in Spark 00:03:59
- Implementing Clustering - Choosing Number of Clusters 00:05:58
- Detecting Anomalies in Network Traffic 00:03:31
- Chapter 2 : Finding Patterns in Time Series Data
- Chapter 3 : Finding Intent of Users Using HMM
- Chapter 4 : Finding Anomalies in Data Using Graphs
- Title: Identifying Behaviour Patterns using Machine Learning Techniques
- Release date: September 2017
- Publisher(s): Packt Publishing
- ISBN: 9781788621885
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