Anomaly Detection: Machine Learning, Deep Learning, Automl

Covers Time Based, Non Time Based and Image Anomalies | Understand what happens inside a library | With Explainer AI

What you'll learn

What is an anomaly?

What are the areas where anomaly detection can be applied?

What are the three types of anomaly detection techniques?

How to analyze time based data for anomalies?

How to use supervised learning to identify anomalies?

How to apply unsupervised learning algorithms like DBSCAN and Isolation Forest to detect anomalies?

How to analyze images and identify anomalies among them?

Requirements

None.

Description

Recent UpdatesFeb 2023: Explaining the outcome of an algorithm is always challenging, more so in an unsupervised area. We have added a video lecture on this interesting area.Jan 2023: Added anomaly detection algorithms (Auto Encoders, Boltzmann Machines, Adversarial Networks) using deep learningNov 2022: We all want to know what goes on inside a library. We have explained isolation forest algorithm by taking few data points and identifying anomaly point through manual calculation. A unique approach to explain an algorithm!July 2022: AutoML is the new evolution in IT and ML industry. AutoML is about deploying ML without writing any code. Anomaly Detection Using PowerBI has been added.  June 2022: A new video lecture on Predicting High Impact Low Volume Events: Predictive Maintenance has been added.May 2022: A new video lecture on PyOD: A comparison of 10 algorithms has been added–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––Course DescriptionAn anomaly is a data point that doesn’t fit or gel with other data points. Detecting this anomaly point or a set of anomaly points in a process area can be highly beneficial as it can point to potential issues affecting the organization. In fact, anomaly detection has been the most widely adopted area with in the artificial intelligence - machine learning space in the world of business. As a practitioner of AI, I always ask my clients to start off with anomaly detection in their AI journey because anomaly detection can be applied even when data availability is limited.Anomaly detection can be applied in the following areas:· Predictive maintenance in the manufacturing industry· Fraud detection across industries· Surveillance activities across industries· Customer Service and retail industries. SalesThe following will be covered in this program:· The three types of anomaly detection – time based, non time based and image. Of these, image anomaly is a new frontier for AI. Just like we analyze the numbers, we can now analyze images and identify anomalies.· Machine learning and deep learning concepts· Supervised and unsupervised algorithms (DBSCAN, Isolation Forest). Image anomaly detection using deep learning techniques· Scenarios where anomaly detection can be applied· Python is covered in great detail to assist those who are new to python or want a refresher on any of the python topics.Anomaly detection is one area that can be applied in any type of business and hence organizations embarking on AI journey normally first explore anomaly detection area. So, as professionals and students, you can also explore this wonderful field!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Structure

Section 2: The Three Types of Anomalies

Lecture 3 Understanding Anomalies

Lecture 4 Application Scenarios

Lecture 5 Anomaly Vs Outlier: Is Every Anomaly An Outlier?

Section 3: Anomaly Detection - Time Series

Lecture 6 Understanding Anomaly Detection in Time Series Data

Lecture 7 Programming for Anomaly Detection - Time Series

Section 4: Anomaly Detection - Unsupervised DBSCAN

Lecture 8 Anomaly Detection - Unsupervised DBSCAN

Section 5: Anomaly Detection - Unsupervised Isolation Forest

Lecture 9 Uncovering Isolation Forest Algorithm

Lecture 10 Isolation Forest Programming

Lecture 11 Explainer AI: Understand the Output of Algorithm

Section 6: Anomaly Detection - Supervised

Lecture 12 Anomaly Detection - Supervised

Section 7: Anomaly Detection - Images

Lecture 13 Intro and Background

Lecture 14 Anomaly Detection - Images

Section 8: Anomaly Detection Using Deep Learning

Lecture 15 GAN

Lecture 16 RBM, DBN

Lecture 17 Auto Encoder

Lecture 18 Building the anomaly detection models

Section 9: PyOD: A comparison of 10 algorithms

Lecture 19 PyOD: A comparison of 10 algorithms

Section 10: Predicting High Impact Low Volume Events: Predictive Maintenance

Lecture 20 Predicting High Impact Low Volume Events: Predictive Maintenance

Section 11: No Code (AutoML) approach to anomaly detection using PowerBI

Lecture 21 What is AutoML

Lecture 22 Anomaly Detection Using PowerBI

Section 12: Machine Learning

Lecture 23 Machine Learning Concepts

Lecture 24 Logistic Regression and Introduction to Deep Learning

Lecture 25 Unsupervised Learning

Section 13: Bonus Lecture

Lecture 26 Bonus Lecture

Machine and Deep Learning enthusiasts,Data Science/Analytics Managers & Heads,Beginners in Data Science