Learn about the Applications of AI for Anomaly Detection

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Applications of AI for Anomaly Detection Course Overview


AI for Anomaly Detection is a course that provides a comprehensive overview of the methods used to detect anomalies in data. The course covers topics such as machine learning, unsupervised learning, deep learning, neural networks, time-series analysis, clustering-based algorithms, and deep learning-based approaches for anomaly detection. It also provides students with a hands-on approach to understanding and implementing these algorithms. In the course, students learn how to apply different methods of anomaly detection to complex datasets and how to combine these methods in order to improve the accuracy of detection. In addition, the course provides guidance on how to build custom anomaly detection systems based on specific datasets. After completing this course, students will have a strong understanding of how and when to apply AI algorithms for anomaly detection, as well as how to build custom and robust models.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

Applications of AI for Anomaly Detection
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The 1-on-1 Advantage

Get 1on-1 session with our expert trainers at a date & time of your convenience.
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Flexible Dates

Start your session at a date of your choice-weekend & evening slots included, and reschedule if necessary.
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4-Hour Sessions

Training never been so convenient- attend training sessions 4-hour long for easy learning.
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Destination Training

Attend trainings at some of the most loved cities such as Dubai, London, Delhi(India), Goa, Singapore, New York and Sydney.
Live Online Training (Duration : 8 Hours) Fee On Request
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

4 Hours
8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
Date On Request
Applications of AI for Anomaly Detection
Course Prerequisites

1. Basic knowledge of coding in Python.
2. Knowledge of machine learning fundamentals.
3. Knowledge of basic statistical models and concepts such as decision trees, Naive Bayes, logistic regression, etc.
4. Understanding of probability, linear algebra and multivariate calculus.
5. Knowledge of basic deep learning models such as convolutional and recurrent neural networks.
6. Familiarity with failure prediction and machine health monitoring.

Target Audience


The target audience for applications of AI for Anomaly Detection training would include data scientists, machine learning engineers, and software developers
For those with more advanced experience, they should have a good understanding of algorithms, statistics and probability theory, as well as familiarity with AI libraries and developing models
Professionals who have experience and knowledge in programming, machine learning and deep learning will be able to better understand and apply these methods in their projects
Additionally, a basic understanding of networks, architectures and data processing is also beneficial in this field
Further, a basic understanding of domain-specific knowledge and experience with anomaly detection methods would be helpful for those looking to apply this type of AI training
Overall, the target audience for AI for Anomaly Detection training is anyone with an interest or knowledgebase in AI and its related technologies and processes

Learning Objectives of Applications of AI for Anomaly Detection


1. Identify and classify anomalies in datasets.
2. Use different methods to detect anomalies such as clustering, rules-based, or statistical methods.
3. Deploy algorithms and other machine learning techniques for identifying and processing anomalies.
4. Learn how to interpret and analyze the results from different techniques applied for anomaly detection.
5. Develop strategies to reduce the amount of false positives during anomaly detection.
6. Utilize case studies to gain an understanding of how AI is being used for anomaly detection in the industry.
7. Understand the impact of data quality on anomaly detection methods.
8. Learn to interpret and compare different anomaly detection techniques' efficacy.

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FAQ's

Yes, fee excludes local taxes.
Yes, we do.
Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1 on 1 Public you can select your own schedule, other students can be merged. Choose 1-on-1 if published schedule doesn't meet your requirement. If you want a private session, opt for 1-on-1 Private.
1-on-1 Public - Select your start date. Other students can be merged. 1-on-1 Private - Select your start date. You will be the only student in the class.
Yes, course requiring practical include hands-on labs.
You can buy online from the page by clicking on "Buy Now". You can view alternate payment method on payment options page.
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We use the best standards in Internet security. Any data retained is not shared with third parties.
You can request a refund if you do not wish to enroll in the course.
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Yes, we also offer weekend classes.
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It is recommended but not mandatory. Being acquainted with the basic course material will enable you and the trainer to move at a desired pace during classes. You can access courseware for most vendors.
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You will receive the digital certificate post training completion via learning enhancement tool after registration.
Yes you can.
Yes, we do. For details go to flexi
You can pay through debit/credit card or bank wire transfer.
Dubai, London, Sydney, Singapore, New York, Delhi, Goa, Bangalore, Chennai and Gurugram.
Yes you can request your customer experience manager for the same.
Yes of course. 100% refund if training not upto your satisfaction.

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