Curated learning path for AI for Fraud & AML. Build practical skills through expert-selected courses.
Start now · step 1 of 5
Basic algebra and statistics helpful but not required
Any programming experience; Python preferred
Follow these courses in order to complete the learning path. Click on any course to enroll.
A course aimed at providing the necessary tools and techniques to succeed in the changing field of financial fraud prevention. It features insights from an instructor with over 25 years of experience in Fraud Detection and AI/ML.
Developed by AI experts and risk practitioners, this certificate provides a comprehensive understanding of AI and machine learning methodologies in the context of risk management. It covers AI tools, techniques, risks, ethical considerations, and governance frameworks.
A live, instructor-led training available online or onsite for professionals with intermediate knowledge. The course is designed for those looking to use machine learning and AI tools to automate and improve financial crime detection, compliance monitoring, and operational governance.
Dive into Computer Vision with PyTorch: Master Deep Learning, CNNs, and GPU Computing for Real-World Applications - 2024 Edition"Unlock the potential of Deep Learning in Computer Vision, where groundbreaking advancements shape the future of technology. Explore applications ranging from Facebook's image tagging and Google Photo's People Recognition to fraud detection and facial recognition. Delve into the core operations of Deep Learning Computer Vision, including convolution operations on images, as you master the art of extracting valuable information from digital images.In this comprehensive course, we focus on one of the most widely used Deep Learning frameworks – PyTorch. Recognized as the go-to tool for Deep Learning in both product prototypes and academia, PyTorch stands out for its Pythonic nature, ease of learning, higher developer productivity, dynamic approach for graph computation through Auto Grad, and GPU support for efficient computation.Why PyTorch?Pythonic: PyTorch aligns seamlessly with the Python programming language, offering a natural and intuitive experience for learners.Easy to Learn: The simplicity of PyTorch makes it accessible for beginners, allowing a smooth learning curve.Higher Developer Productivity: PyTorch's design prioritizes developer productivity, promoting efficiency in building and experimenting with models.Dynamic Approach for Graph Computation - Auto Grad: PyTorch's dynamic computational graph through Auto Grad enables flexible and efficient model development.GPU Support: PyTorch provides GPU support for accelerated computation, enhancing performance in handling large datasets and complex models.Course Highlights:Gain a foundational understanding of PyTorch, essential for delving into the world of Deep Learning.Learn GPU programming and explore how to access free GPU resources for efficient learning.Master the Auto
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
Explore related content to expand your skills beyond this learning path.
Curated learning path for AI for Fraud & AML. Build practical skills through expert-selected courses.
You'll work through Understand core concepts and foundations; Build your first projects with guided tutorials; Gain confidence with hands-on exercises; Build and train machine learning models from scratch; Understand supervised, unsupervised, and reinforcement learning paradigms; Evaluate model performance using industry-standard metrics.
It's pitched at beginner level — a solid starting point if you're new to the topic.
5 curated courses, sequenced from foundational to advanced.
Around $160 total if you buy every course — but many include free audit options.
Enroll in this path to track your progress and stay motivated.