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Machine Learning - Anomaly Detection via PyCaret
Muhammad Saad uddin
Muhammad Saad uddin
In this 2 hour long project-based course you will learn how to perform anomaly detection, its importance in machine learning, set up PyCaret anomaly detection, create, visualize & compare anomaly detection algorithms all this with just a few lines of code.
Register for this course and see more details by visiting:
OpenCourser.com/course/3fc984/machine
What's inside
Syllabus
Machine Learning - Anomaly Detection via PyCaret
Here you will describe what the project is about. It should give an overview of what the learner will achieve by completing this project.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides an overview of anomaly detection and its benefits
Utilizes the PyCaret library, which streamlines the anomaly detection process
Offers hands-on experience through project-based learning
Suitable for beginners seeking an introduction to anomaly detection
Designed for individuals interested in exploring machine learning and data analysis
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Reviews summary
Basic anomaly detection
According to students, this course provides a basic introduction to anomaly detection in Python, using the PyCaret library. The course is affordable, but students say it could use more advanced content, real-world examples, and in-depth explanations. Overall, reviews are largely negative, especially among more experienced students.
Priced well
"This was a decent intro into anomaly detection for the price and time spent"
Final quiz has errors
"Also I felt like the final quiz had some errors"
Not for experienced learners
"Expected a more challenging use case"
"This topic demands a much more thorough introduction"
"I felt it would be too hard for beginners to understand and too easy for intermediates to be challenged with"
Activities
Coming soon
We're preparing activities for
Machine Learning - Anomaly Detection via PyCaret.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Machine Learning - Anomaly Detection via PyCaret will develop knowledge and skills
that may be useful to these careers:
Security Analyst
Security Analyst
A Security Analyst is a professional who protects an organization's computer systems and networks from threats. A course in Machine Learning - Anomaly Detection via PyCaret would be particularly useful to a Security Analyst, as it would provide them with the skills needed to identify and interpret anomalies in data. This would be particularly valuable in roles that involve detecting and mitigating threats, such as intrusion detection or forensics.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is a software engineer who specializes in developing and deploying machine learning models. A course in Machine Learning - Anomaly Detection via PyCaret would be particularly useful in this role, as it would provide you with the skills needed to develop and deploy models that can detect anomalies in data. This would be particularly valuable in roles that involve developing models for fraud detection or predictive maintenance.
Fraud Analyst
Fraud Analyst
A Fraud Analyst is a professional who investigates and identifies fraudulent activities. A course in Machine Learning - Anomaly Detection via PyCaret would be particularly useful to a Fraud Analyst, as it would provide them with the skills needed to identify and interpret anomalies in data. This would be particularly valuable in roles that involve investigating and mitigating fraud, such as financial analysts or fraud investigators.
Risk Analyst
Risk Analyst
A Risk Analyst is a professional who identifies and assesses risks. A course in Machine Learning - Anomaly Detection via PyCaret would be particularly useful to a Risk Analyst, as it would provide them with the skills needed to identify and interpret anomalies in data. This would be particularly valuable in roles that involve identifying and mitigating risks, such as financial analysts or fraud investigators.
Data Analyst
Data Analyst
A Data Analyst is a professional who uses their knowledge of data analysis and statistics to identify trends and patterns in data. A course in Machine Learning - Anomaly Detection via PyCaret would be particularly useful to a Data Analyst, as it would provide them with the skills needed to identify and interpret anomalies in data. This would be particularly valuable in roles that involve identifying and mitigating risks, such as financial analysts or fraud investigators.
Data Scientist
Data Scientist
A Data Scientist is a professional who uses their knowledge of data mining, machine learning, and statistics to extract meaningful insights from data. A course in Machine Learning - Anomaly Detection via PyCaret would be particularly relevant to this role, as it would provide you with the skills needed to identify and interpret anomalies in data. This would be particularly useful in roles that involve identifying and mitigating risks, such as financial analysts or fraud investigators.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is a professional who uses mathematical and statistical models to analyze and predict financial markets. A course in Machine Learning - Anomaly Detection via PyCaret would be particularly useful to a Quantitative Analyst, as it would provide them with the skills needed to identify and interpret anomalies in data. This would be particularly valuable in roles that involve developing models for predicting stock prices or identifying trading opportunities.
Actuary
Actuary
An Actuary is a professional who uses mathematical and statistical models to assess and manage risks. A course in Machine Learning - Anomaly Detection via PyCaret would be particularly useful to an Actuary, as it would provide them with the skills needed to identify and interpret anomalies in data. This would be particularly valuable in roles that involve developing models for pricing insurance policies or assessing financial risks.
Software Engineer
Software Engineer
A Software Engineer is a professional who designs, develops, and maintains software systems. A course in Machine Learning - Anomaly Detection via PyCaret would be particularly useful to a Software Engineer, as it would provide them with the skills needed to develop and deploy models that can detect anomalies in data. This would be particularly valuable in roles that involve developing software for fraud detection or predictive maintenance.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is a professional who uses mathematical and statistical models to solve business problems. A course in Machine Learning - Anomaly Detection via PyCaret would be particularly useful to an Operations Research Analyst, as it would provide them with the skills needed to identify and interpret anomalies in data. This would be particularly valuable in roles that involve developing models for optimizing supply chains or improving customer service.
Business Analyst
Business Analyst
A Business Analyst is a professional who uses data and analysis to help businesses make better decisions. A course in Machine Learning - Anomaly Detection via PyCaret would be particularly useful to a Business Analyst, as it would provide them with the skills needed to identify and interpret anomalies in data. This would be particularly valuable in roles that involve identifying and mitigating risks, such as fraud or compliance.
Data Engineer
Data Engineer
A Data Engineer is a professional who designs and builds data pipelines. A course in Machine Learning - Anomaly Detection via PyCaret would be particularly useful to a Data Engineer, as it would provide them with the skills needed to identify and interpret anomalies in data. This would be particularly valuable in roles that involve building pipelines for fraud detection or predictive maintenance.
For more career information including salaries, visit:
OpenCourser.com/course/3fc984/machine
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning - Anomaly Detection via PyCaret.
Covers a wide range of anomaly detection algorithms, including both supervised and unsupervised methods. It also discusses different types of anomaly detection scenarios.
Covers a wide range of machine learning algorithms for anomaly detection. It provides a theoretical overview and practical guidance on how to use these algorithms.
Covers data mining techniques for anomaly detection. It discusses different types of anomaly detection problems and provides practical guidance on how to use data mining algorithms to solve them.
Save
Provides a comprehensive overview of data streams and their applications. It covers topics such as data stream models, algorithms, and systems. The book is suitable for researchers and practitioners working with data streams.
This comprehensive handbook covers a wide range of data mining and knowledge discovery topics. It includes a section on anomaly detection, providing an overview of different techniques and applications. The book valuable resource for researchers and practitioners in the field.
This classic textbook provides a comprehensive introduction to pattern recognition and machine learning. It covers various anomaly detection techniques as part of its broader discussion of machine learning algorithms. The book is suitable for advanced undergraduate and graduate students, as well as researchers and practitioners.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3fc984/machine
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Muhammad Saad uddin
Language
English
Good to know
Provides an overview of anomaly detection and its benefits
Utilizes the PyCaret library, which streamlines the anomaly detection process
Offers hands-on experience through project-based learning
Suitable for beginners seeking an introduction to anomaly detection
Designed for individuals interested in exploring machine learning and data analysis
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