by Tomasz Lelek | Released September 2017 | ISBN: 9781788621885

Learn to identify behaviour patterns based on the user actions on the web site using ML techniques About This VideoLearn 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.In DetailNowadays 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. Show and hide more

1. 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
2. Chapter 2 : Finding Patterns in Time Series Data
   • Analyzing Time Series Data Using Clustering 00:03:56
   • Implementing GMM in Apache Spark 00:03:08
   • Validation and Testing 00:06:00
3. Chapter 3 : Finding Intent of Users Using HMM
   • Hidden Markov Model Explanation 00:05:39
   • Implementing HMM for Weather Forecast 00:06:55
   • Using HMM to Find State of a User 00:06:20
4. Chapter 4 : Finding Anomalies in Data Using Graphs
   • Graph Data for Anomaly Detection 00:06:01
   • Finding Anomalies in Social Network 00:06:46
   • Finding Suspicious Users in Social Network 00:07:04
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