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Machine Learning - Anomaly Detection via PyCaret

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.

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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

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

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning - Anomaly Detection via PyCaret with these activities:

Review of Anomaly Detection Concepts

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Refresh your foundational understanding of anomaly detection concepts before starting the course.

Browse courses on Anomaly Detection

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Review lecture notes, textbooks, or online resources on anomaly detection.
Complete practice exercises or quizzes to test your understanding.

Course Materials Compilation

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Organize and consolidate your course materials for easy reference and review.

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Gather all course notes, assignments, and other materials.
Organize them into a logical structure.

Anomaly Detection Study Group

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Collaborate with peers to discuss concepts and reinforce learning through mutual support.

Browse courses on Anomaly Detection

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Form a study group with other participants.
Meet regularly to discuss course materials.
Work together on assignments and projects.

Four other activities

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Machine Learning Anomaly Detection via Pycaret Tutorial

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Improve your understanding of anomaly detection and its implementation with PyCaret.

Browse courses on Anomaly Detection

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Follow a guided tutorial on anomaly detection using PyCaret.

Anomaly Detection Resource Collection

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Expand your knowledge beyond the course by gathering and compiling resources on anomaly detection.

Browse courses on Anomaly Detection

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Identify relevant books, articles, websites, and tools on anomaly detection.
Organize and annotate the resources for easy reference.

Anomaly Detection Blog Post

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Solidify your knowledge by explaining anomaly detection concepts in your own words.

Browse courses on Anomaly Detection

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Choose a specific aspect of anomaly detection to focus on.
Research and gather information from reliable sources.
Write a blog post explaining the topic in a clear and engaging way.

Anomaly Detection Application

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Develop a working application using PyCaret anomaly detection in any domain of your choice.

Browse courses on Anomaly Detection

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Choose a dataset for building your anomaly detection application.
Prepare and explore your chosen dataset.
Apply PyCaret anomaly detection techniques and compare algorithms.
Deploy your anomaly detection application.

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

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.

See salaries and explore the career path for Security Analyst

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Fraud 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.

See salaries and explore the career path for Risk 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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Operations Research 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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Data Engineer

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.

Neural Networks and Deep Learning

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Covers a wide range of anomaly detection algorithms, including both supervised and unsupervised methods. It also discusses different types of anomaly detection scenarios.

Neural Networks and Deep Learning: A Textbook

Hardcover

Neural Networks and Deep Learning: A Textbook

Kindle Edition

(中文) Neural network and deep learning(Chinese Edition)

Paperback

Chemometric Methods in Analytical Spectroscopy...

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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.

Chemometric Methods in Analytical Spectroscopy...

Hardcover

$$$$

Outlier Detection for Temporal Data

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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.

Outlier Detection for Temporal Data (Synthesis...

Paperback

Data Streams

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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.

Data Streams: Algorithms and Applications...

Paperback

Data Mining With Decision Trees: Theory And...

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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.

Data Mining With Decision Trees: Theory And...

Kindle Edition

Pattern Recognition and Machine Learning

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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.

Pattern Recognition and Machine Learning...

Hardcover

$$$

Pattern Recognition and Machine Learning...

Paperback

$$$

Pattern Recognition and Machine Learning (text...

Unknown Binding

$$$$

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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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