Advanced Anomaly Detection & Recommendation Systems

Build anomaly detection and recommendation systems for e-commerce, finance, and social-media platforms. Anomaly detection covers sources of anomalous data, statistical and ML approaches,...

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$1,665USD
Duration 2 days
Course Code DS-3203
Available Formats Classroom

Overview

Course Description

Build anomaly detection and recommendation systems for e-commerce, finance, and social-media platforms. Anomaly detection covers sources of anomalous data, statistical and ML approaches, deep-learning methods, classical techniques across statistical, distance-based, clustering-based, and proximity-based methods, time-series anomalies, fairness considerations, and evaluation metrics. Recommendation systems cover content-based, collaborative filtering, matrix factorization, deep-learning, and hybrid approaches, with modern techniques including sequential, session-based, graph-based, and reinforcement-learning methods. The two domains share similarity and outliers as their core primitive, so the techniques cross-pollinate through shared evaluation and deployment considerations including interpretability and scalability. Hands-on labs produce trained anomaly detectors on financial data, content-based and collaborative recommendation systems on real datasets, and a deployed API for each. The course is designed for data scientists, analysts, and developers with Python and DS-2204 experience.

Skills Gained

By the end of this course, participants will be able to:

  • Detect anomalies to minimize financial losses and improve security
  • Build recommendation systems that increase user engagement and sales
  • Apply deep learning for accuracy in detection and recommendations
  • Apply ethical considerations to maintain user trust and compliance
  • Deploy and monitor models for operational efficiency

Who Can Benefit

This course is designed for:

  • Data Scientists & Analysts
  • Software Developers

Prerequisites

Participants should enter this course with:

  • Practical Python experience
  • DS-2204 or equivalent

Organizational Objectives

This course assists organizations to

  • Reduce fraud and security losses through deliberate anomaly-detection patterns
  • Increase user engagement through personalized hybrid recommendation systems
  • Lower model-drift risk through codified evaluation and monitoring patterns
  • Build a working anomaly-detection and recommendation discipline across data-science teams

Software

All attendees must have a modern web browser and an Internet connection.

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Course Details

Course Details

Module 1 - Understanding Anomalous Data

By the end of this module, you will be able to recognize sources of anomalous data, apply statistical and machine-learning methods to detection, work with deep-learning approaches, handle anomalies in time-series, and integrate anomaly detection into pipelines.

  • Sources of anomalous data
  • Statistical methods for anomaly detection
  • Machine learning approaches for anomaly detection
  • Deep learning-based approaches
  • Anomalies in time-series data
  • Handling anomalies in data science pipelines and applications
  • Hands-on Lab: Identify outliers in financial transactions and run anomaly detection on time-series data.

Module 2 - Classical Anomaly Detection Techniques

By the end of this module, you will be able to apply statistical, distance-based, clustering-based, and proximity-based anomaly detection; apply time-series anomaly detection; choose evaluation metrics; and reason about fairness, bias, and ethical considerations.

  • Statistical and distance-based techniques
  • Clustering-based and proximity-based techniques
  • Time-series anomaly detection techniques
  • Evaluation metrics for anomaly detection
  • Fairness, bias, and ethical considerations
  • Comparing classical techniques across domains
  • Hands-on Lab: Detect outliers in financial data and apply clustering-based anomaly detection.

Module 3 - Deep Learning for Anomaly Detection

By the end of this module, you will be able to apply deep anomaly detection, choose between deep anomaly model types, design training strategies, evaluate model performance, and apply lessons from real-world case studies.

  • Deep Anomaly Detection
  • Types of Deep Anomaly Detection Models
  • Training Strategies for Deep Anomaly Detection
  • Evaluation Metrics for Anomaly Detection Models
  • Case Studies & Real-World Applications
  • Hands-on Lab: Implement an autoencoder for anomaly detection and run image-based anomaly detection with CNNs.

Module 4 - Building Recommendation Systems

By the end of this module, you will be able to apply content-based filtering, collaborative filtering, matrix factorization, and hybrid recommendation approaches; build deep-learning-based recommenders; and choose evaluation metrics.

  • Content-based filtering
  • Collaborative filtering
  • Matrix factorization techniques
  • Deep learning-based recommendation systems
  • Hybrid recommendation systems
  • Evaluation metrics for recommendation systems
  • Hands-on Lab: Implement content-based filtering with TF-IDF and cosine similarity, then build deep-learning personalized recommendations.

Module 5 - Modern Recommendation Techniques

By the end of this module, you will be able to apply sequential and session-based recommendations, use graph-based recommenders, apply reinforcement learning for recommendations, and choose modern evaluation metrics.

  • Deep learning-based recommendation systems
  • Sequential and session-based recommendations
  • Graph-based recommendation systems
  • Reinforcement learning for recommendations
  • Hybrid recommendation approaches
  • Evaluation metrics for modern recommender systems
  • Hands-on Lab: Apply graph neural networks for social-network recommendations and reinforcement learning for dynamic recommendations.

Module 6 - Evaluating and Deploying Anomaly Detection and Recommendation Systems

By the end of this module, you will be able to evaluate anomaly-detection and recommendation systems, apply model interpretability and explainability, scale and deploy detection and recommendation services, and operate them at scale.

  • Evaluating Anomaly Detection Models
  • Evaluating Recommendation Systems
  • Model Interpretability & Explainability
  • Scaling & Deploying Anomaly Detection Systems
  • Scaling & Deploying Recommendation Systems
  • Case Studies & Real-World Deployments
  • Hands-on Lab: Deploy an anomaly-detection model and a recommendation API into a production-style environment.

Schedule

Upcoming Course Dates

There are currently no scheduled dates for this course. If you are interested in this course, contact us with the form below.

Contact Information

FAQ

Does the course schedule include a Lunchbreak?

Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does Ascendient Learning deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

What does vendor-authorized training mean?

As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.

Is the training too basic, or will you go deep into technology?

It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.

How up-to-date are your courses and support materials?

We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.

Are your instructors seasoned trainers who have deep knowledge of the training topic?

Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.

Do you provide hands-on training and exercises in an actual lab environment?

Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.

Will you customize the training for our company’s specific needs and goals?

We will work with you to identify training needs and areas of growth.  We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.

How do I get started with certification?

Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.

Will I get access to content after I complete a course?

You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.

How do I request a W9 for Ascendient Learning?

View our filing status and how to request a W9.

Reviews

Although there seemed to be too many links for the course, everything worked smoothly.

the class/lecture was amazing and very easy to understand and was in detail.

Topics, material and specially instructor (Graham Godfrey) was beyond my expectations.

Course was thorough and gave a very solid grounding in the tools advertised.

Great instructor, clear and concise course. Labs were easy to follow and worked perfectly.