Anomaly Detection
Why Real-Time, AI-Based Anomaly Detection Is a No-Brainer ?
Duration : 1 year Classes : 72 Days : Weekdays / Weekends
Among the multiple classes of applications of AI: Anomaly Detection, detects data points in data that does not fit well with the rest of the data. It has a wide range of applications such as fraud detection, surveillance, diagnosis, data cleanup, and predictive maintenance.
This Professional course explores what is anomaly system, different anomaly detection techniques, discusses the key idea behind those techniques, and wraps up with a project on how to make use of those results.
This course provides a comprehensive and practical foundation in the theory and application of Anomaly Detection (aka Outlier Analysis or Novelty Detection). Participants will learn to use statistical, machine learning and AI, techniques to identify rare events, suspicious activities, system faults, and data errors that deviate significantly from expected patterns. The training is hands-on, focusing on real-world applications across various domains, including cybersecurity, finance (fraud detection), predictive maintenance, and quality control.
Target Audience:-
- Data Analysts and Data Scientists
- AI / ML / DS - Engineers
- Computer Science students
- IT Professionals looking to transition into Anamoly Detection
- Research Scholars and AI Enthusiasts
- IT roles focused on Fraud, Anamoly and Outlier detection
- Professional in domains where detecting rare events is critical to Business
Learning Outcomes:-
- Define and classify different types of anomalies (point, contextual, collective) and choose the appropriate detection strategy.
- Apply a variety of statistical and distance-based methods for outlier identification.
- Implement classic machine learning algorithms, such as Isolation Forest and One-Class SVM, for both supervised and unsupervised anomaly detection.
- Utilize deep learning architectures, specifically Autoencoders, for complex and high-dimensional data problems.
- Develop effective anomaly detection systems for time-series and streaming data.
- Evaluate and compare the performance of different models using appropriate metrics (e.g., Precision, Recall, ROC-AUC) in imbalanced datasets.
- Pre-process data and engineer relevant features to maximize detection accuracy.
Course Format:-
✔ The course shall be delivered through a combination of lectures, interactive discussions & case studies
✔ Participants are exposed to practical exercises and new-age projects, where they learn by doing
✔ Participants shall have access to online resources, including reading materials, videos & business simulations
✔ Students shall receive all the study material
✔ Guest speakers from the industry may be invited to share insights and experiences
✔ Regular assessments and quizzes will be conducted to reinforce learning
✔ This is a Classroom only training
✔ Corporates: We understand your specific needs and goals. Contact us for customizations to this training
Trainers:-
✔ Equipped with multidisciplinary backgrounds
✔ Experts from the field of Maths, Financial Markets, AIML, Data Science & Management
✔ Each with over 25+ years of International experience working in EU / US / Australia
✔ All our trainers are Highly Qualified and Certified, in their respective subject areas
- You need to have intermediate to advanced Python experience. You are familiar with object-oriented Programming. You can write nested for loops and can read and understand code written by others.
-Intermediate Statistics background. You are familiar with Probability.
-Intermediate knowledge of machine learning techniques. You can describe backpropagation, and have seen a few examples of Neural Network architecture (preferrably a recurrent Neural Network or a long short-term memory network).
-You have seen or worked with a Deep Learning framework like TensorFlow, Keras, or PyTorch before.
....
NB: All our trainings are always tailored to adopt to the Individual's Pace and Learning Depth.
NB: As a stepping stone, providing foundational knowledge, Bridge Courses are conducted periodically, to help students transition between different levels by closing knowledge gaps. These classes can be attended ad hoc, and are 'complimentary' for our bonafide students.
Kindly fill the DownloadPDF Form for the Brouchre with latest curriculum and full Training details.
Or you may Book an Appointment to collect your Brouchre and complete your registration.
-Intermediate Statistics background. You are familiar with Probability.
-Intermediate knowledge of machine learning techniques. You can describe backpropagation, and have seen a few examples of Neural Network architecture (preferrably a recurrent Neural Network or a long short-term memory network).
-You have seen or worked with a Deep Learning framework like TensorFlow, Keras, or PyTorch before.
....
NB: All our trainings are always tailored to adopt to the Individual's Pace and Learning Depth.
NB: As a stepping stone, providing foundational knowledge, Bridge Courses are conducted periodically, to help students transition between different levels by closing knowledge gaps. These classes can be attended ad hoc, and are 'complimentary' for our bonafide students.
Kindly fill the DownloadPDF Form for the Brouchre with latest curriculum and full Training details.
Or you may Book an Appointment to collect your Brouchre and complete your registration.
This syllabus provides a structured, module-by-module breakdown of this comprehensive training program focused on participants overall performance, retention, and engagement, covering foundational theory, implementation, best industry practices and advanced techniques in the subject.
Module 1: Fundamentals of Anomaly Detection
✔ Why Anomaly Detection is critical
✔ Types of Anomalies
✔ Detection Paradigms
✔ Data Preparation
Module 2: Statistical and Proximity-Based Techniques
✔ Statistical Methods
✔ Distance-Based Methods
✔ Density-Based Methods
✔ Clustering Methods
Module 3: Classic Machine Learning Algorithms
✔ Tree-Based Methods
✔ Support Vector Machines (SVM)
✔ Ensemble Methods
✔ Case Study
Module 4: Deep Learning for Complex Anomaly Detection
✔ Autoencoders
✔ Variational Autoencoders (VAEs)
✔ Anomaly Detection in Images
✔ Generative Adversarial Networks (GANs) in anomaly detection
Module 5: Anomaly Detection in Specialized Data
✔ Time-Series Anomaly Detection
✔ Recurrent Neural Networks (RNNs) and LSTMs
✔ High-Dimensional Data: Challenges and solution
Module 6: Evaluation, Deployment, and Advanced Topics
✔ Model Evaluation
✔ Threshold Selection
✔ Real-Time Systems
✔ Ethical Considerations and Model Drift
Module 7: Capstone Project
NB:The curriculum is regularly subjected to updates, reflecting the latest industry trends & current technological advancements.
At Vyom Data Sciences, we aspire to provide the latest curriculum and most recent technology, as a standard component of all our trainings. Experts, with 25+ years of experience from USA, Europe and Australia, bring the best industry practices while designing and executing these trainings. All our trainers are Highly Qualified and Certified in their respective subject areas.
Kindly fill the DownloadPDF Form for the Brouchre with latest curriculum and full Training details.
Or you may Book an Appointment to collect your Brouchre.
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Bhawana
Fabulous NLP + ML course
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Your instructor was excellent, the best I've experienced on a software subject, and I couldn't imagine him doing a better job of seamlessly walking students through a breadth of information for such complex subject like AI and ML. he did a fabulous job pacing everything and addressing student questions. I am very impressed.
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Excellent ML course!
The course was well structured and easy to understand. Good pace of learning.
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