Anomaly Detection & Fraud Learning Path

Offered by Johns Hopkins University, this course equips learners with skills to combat advanced cybersecurity threats using artificial intelligence, focusing on malware detection and network anomaly identification.

A course focused on acquiring practical expertise in using generative AI for fraud prevention and detection analytics.

This course delves into the advanced uses of generative AI for detecting fraud and ensuring compliance. Participants will learn how generative AI is transforming risk management and how to apply AI-based strategies.

A 2-hour project-based course where you will learn to perform anomaly detection using Py Caret, a low-code machine learning library in Python.

This course is designed for anyone interested in pursuing a career in artificial intelligence and computer vision or looking to implement computer vision applications in their projects. In "Computer Vision Smart Systems: Python, YOLO, and OpenCV -1," we start with the fundamentals of computer vision and cover image processing techniques using the Python programming language and OpenCV library. Then, we advance to object detection and deep learning modeling using the YOLO (You Only Look Once) algorithm. Students will learn to build custom deep learning models from scratch, work with datasets, perform object detection, and apply these models in various projects.Throughout the course, practical exercises are provided step-by-step along with theoretical knowledge, giving students the chance to apply what they've learned. Additionally, we address common challenges you may face and provide detailed solutions. Aimed at building skills from basic to intermediate levels, this course serves as a comprehensive guide for anyone interested in the field of computer vision. It empowers you to develop smart systems for your projects and enhances your expertise in this exciting domain."You are never too old to set another goal or to dream a new dream." - C.S.Lewis"Do the difficult things while they are easy and do the great things while they are small. A journey of a thousand miles begins with a single step" - Lao Tzu You get the best information that I have compiled over years of trial and error and experience!Best wishes,Yılmaz ALACA

If you want to learn the process to detect whether a person is wearing a face mask using AI and Machine Learning algorithms then this course is for you.In this course I will cover, how to build a Face Mask Detection model to detect and predict whether a person is wearing a face mask or not in both static images and live video streams with very high accuracy using Deep Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model using CNNs and OpenCV.This course will walk you through the initial data exploration and understanding, Data Augumentation, Data Generators, customizing pretrained Models like Mobile NetV2, model building and evaluation. Then using the trained model to detect the presence of face mask in images and video streams.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1  :  Project Overview.Task 2  :  Introduction to Google Colab.Task 3  :  Understanding the project folder structure.Task 4  :  Understanding the dataset and the folder structure.Task 5  :  Loading the data from Google Drive.Task 6  :  Importing the Libraries.Task 7  :  About Config and Resize File.Task 8  :  Some common Methods and Utilities Task 9  :  About Data Augmentation.Task 10 :  Implementing Data Augmentation techniques.Task 11 :  About Data Generators.Task 12 :  Implementing Data Generators.Task 13 :  About Convolutional Neural Network (CNNs).Task 14 :  About OpenCV.Task 15 :  Understanding pre-trained models.Task 1

Recent Updates July 2024: Added a video lecture on hybrid approach (combining clustering and non clustering algorithms to identify anomalies)Feb 2023: Added a video lecture on "Explainable AI". This is an emerging and a fascinating area to understand the drivers of outcomes. Jan 2023: Added anomaly detection algorithms (Auto Encoders, Boltzmann Machines, Adversarial Networks) using deep learning Nov 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 balancing the imbalanced dataset has been added.May 2022: A new video lecture on PyOD: A comparison of 10 algorithms has been added Course Description An 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 ind

Latest update: I will show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab.This course is a complete guide for setting up TensorFlow object detection api, Transfer learning and a lot moreI think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.Here is the details about the project.Here we will star from colab understating because that will help to use free GPU provided by google to train up our model.We’re going to bridge the gap between the basic CNNs architecture you already know and love, to modern, novel architectures such as Res Net, and Inception.We will understand object detection modules in detail using both TensorFlow object detection api as well as YOLO algorithms.We’ll be looking at a state-of-the-art algorithm called RESNET and Mobile NetV2 which is both faster and more accurate than its predecessors.One best thing is you will understand the core basics of CNNs and how it converts to object detection slowly.I hope you’re excited to learn about these advanced applications of CNNs Yolo and TensorFlow, I’ll see you in class!AMAGING FACTS:· This course give’s you full hand’s on experience of training models in colab GPU.· Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on high-level building blocks. The result? Almost zero math.· Another result? No complicated low-level code such as that written in TensorFlow, Theano,YOLO, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you.

Master Real-Time Object Detection with Deep Learning Dive into the world of computer vision and learn to build intelligent video analytics systems. This comprehensive course covers everything from foundational concepts to advanced techniques, including:Video Analytics Basics: Understand the 3-step process of capturing, processing, and saving video data.Object Detection Powerhouse: Explore state-of-the-art object detection models like Haar Cascade, HOG, Faster RCNN, R-FCN, SSD, and YOLO.Real-World Applications: Implement practical projects like people footfall tracking, automatic parking management, and real-time license plate recognition.Deep Learning Mastery: Learn to train and deploy deep learning models for image classification and object detection using frameworks like TensorFlow and Keras.Hands-On Experience: Benefit from line-by-line code walkthroughs and dedicated support to ensure a smooth learning journey.Exciting News!We've just added two new, hands-on projects to help you master real-world computer vision applications:Real-Time License Plate Recognition System Using YOL Ov3: Dive deep into real-time object detection and recognition.Training a YOL Ov3 Model for Real-Time License Plate Recognition: Learn to customize and train your own YOL Ov3 model. Don't miss this opportunity to level up your skills!Why Enroll?Industry-Relevant Skills: Gain in-demand skills to advance your career in AI and machine learning.Practical Projects: Build a strong portfolio with real-world applications.Expert Guidance: Learn from experienced instructors and get personalized support.