Anomaly Detection in Time Series Data with Keras from Coursera

In this hands-on introduction to anomaly detection in time series data with Keras, you and I will build an anomaly detection model using deep learning. Specifically, we will be designing and training an LSTM autoencoder using the Keras API with Tensorflow 2 as the backend to detect anomalies (sudden price changes) in the S&P 500 index. We will also create interactive charts and plots using Plotly Python and Seaborn for data visualization and display our results in Jupyter notebooks. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores anomaly detection in time series data, which is standard in finance and other industries

Taught by Snehan Kekre, who is a recognized expert in time series analysis and anomaly detection

Develops skills in using Keras for deep learning and Plotly for data visualization, which are core skills for data scientists

Reviews summary

Anomaly detection with deep learning

This course introduces anomaly detection in time series data with Keras. It covers building an anomaly detection model using deep learning, specifically designing and training an LSTM autoencoder using Keras API with Tensorflow 2 as the backend. The course also includes interactive charts and plots using Plotly Python and Seaborn for data visualization. Overall, learners found the course to be well-structured and informative, with a good balance of practical hands-on exercises and explanations. However, some learners expressed concerns about the limited time available for completing the project and the lack of explanation for certain coding steps.

Interactive charts and plots using Plotly Python and Seaborn

"The material did a good job of covering some useful katas for visualization using plotly and seaborn."

Introduces Keras API and Tensorflow 2 for deep learning

"It covers building an anomaly detection model using deep learning, specifically designing and training an LSTM autoencoder using Keras API with Tensorflow 2 as the backend."

Hands-on project with step-by-step guidance

"This course is very well structured and delivered."

"The instructor from rhyme was quite good. He explained every part, every function and reason behind their use quite clearly."

Limited time available for completing the project

"I was watching at 2x speed (an annotation told to use 1.5x speed) AND WAS STILL NOT FINISHED when the allotted time was over."

"Doesn't recommend it"

Insufficient explanation for certain coding steps

"The instructor doesn't explain why he is doing something, just does it."

"The instructor didn't explain why he does everything. He just did, and you just need to follow what he typed. After the project, you still didn't learn a lot."

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 Anomaly Detection in Time Series Data with Keras with these activities:

Review Course Materials

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By reviewing the course prerequisites, you will lay a stronger foundation and put yourself in a better position to suceed once the course begins.

Browse courses on Anomaly Detection

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Review the course syllabus
Gather notes, assignments, quizzes, and exams from previous courses in related topics
Connect with classmates either online or in person to form a study group

Review Python

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Review fundamental Python skills, ensuring proficiency before starting the course.

Browse courses on Python

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Go through Python tutorials and exercises
Solve coding challenges and practice writing Python code

Build a Simple Time Series Model

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This activity will help you develop a foundational understanding of building LSTM Autoencoders using Keras with Tensorflow before the course begins.

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Follow a tutorial on building a simple time series model using Keras and Tensorflow
Download and install the necessary software and libraries
Build the time series model according to the tutorial
Test the model on a small dataset

Ten other activities

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Read 'Deep Learning with Python' by François Chollet

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Supplement course material with a comprehensive book on deep learning, providing a deeper theoretical foundation.

View Deep Learning with Python, Second Edition on Amazon

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Read and understand key concepts from the book
Apply concepts to the course material

Attend a Workshop on Advanced Anomaly Detection Techniques

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Attending workshops allows you to learn from experts in the field and gain practical experience.

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Research and identify relevant workshops on advanced anomaly detection techniques
Register for the workshop
Attend the workshop and actively participate in the sessions
Follow up with the workshop organizers or speakers to ask questions or connect with other participants

Follow Keras and TensorFlow tutorials

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Enhance understanding of Keras and TensorFlow by following guided tutorials, solidifying concepts crucial to the course.

Browse courses on Keras

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Explore official Keras and TensorFlow tutorials
Complete hands-on exercises and projects using Keras and TensorFlow

Practice Anomaly Detection Techniques

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This hands-on activity allows you to refine your anomaly detection skills and gain practical experience with the tools used in the course.

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Find a dataset of time series data for anomaly detection
Apply anomaly detection techniques to the data, experimenting with different parameters
Visualize the results of the anomaly detection using Plotly Python and Seaborn
Evaluate the performance of the anomaly detection model

Participate in discussion forums and Q&A sessions

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Engage with peers to clarify concepts, share insights, and reinforce learning through discussion and collaboration.

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Actively participate in discussion forums
Ask questions and engage in Q&A sessions

Solve anomaly detection practice problems

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Enhance problem-solving skills by practicing anomaly detection in time series data, solidifying concepts through repetitive exercises.

Browse courses on Anomaly Detection

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Solve practice problems on anomaly detection
Analyze and interpret results

Volunteer as a Mentor for Beginner Anomaly Detection Learners

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Sharing your knowledge with others helps reinforce your understanding and provides a valuable service to the community.

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Join an online or in-person mentoring community
Create a profile and indicate your interest in mentoring anomaly detection beginners
Connect with learners who are seeking guidance
Provide support and guidance to your mentees

Develop a LSTM autoencoder model in Keras

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Apply course concepts by creating an LSTM autoencoder model, actively reinforcing learning through hands-on implementation.

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Design and implement the LSTM autoencoder architecture using Keras
Train and evaluate the model on the S&P 500 index data
Analyze and interpret the model's performance

Contribute to an Open-Source Anomaly Detection Project

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Collaborating on an open-source project is a great way to further refine your understanding and contribute to the community.

Browse courses on GitHub

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Find an open-source anomaly detection project on GitHub
Review the project's documentation and codebase
Identify an area where you can contribute
Create a pull request with your contributions

Develop a project on anomaly detection in a real-world dataset

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Apply course concepts to a real-world problem, demonstrating mastery and practical implementation skills.

Browse courses on Anomaly Detection

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Choose a dataset and define the anomaly detection problem
Design and implement an anomaly detection model
Evaluate and interpret the model's performance
Present findings in a written report or presentation

Career center

Learners who complete Anomaly Detection in Time Series Data with Keras will develop knowledge and skills that may be useful to these careers:

Fraud Analyst

Fraud Analysts investigate and prevent fraud and financial crimes. This course in Anomaly Detection in Time Series Data with Keras can help Fraud Analysts develop their skills in detecting anomalies in financial data, a critical skill in the field.

See salaries and explore the career path for Fraud Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course in Anomaly Detection in Time Series Data with Keras can help Quantitative Analysts develop their skills in detecting anomalies in financial data, a critical skill in the field.

See salaries and explore the career path for Quantitative Analyst

Statistician

Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course in Anomaly Detection in Time Series Data with Keras can help Statisticians develop their skills in using Keras to build LSTM autoencoders for anomaly detection, a critical technique in the field.

See salaries and explore the career path for Statistician

Software Engineer

Software Engineers design, develop, and maintain software systems. This course in Anomaly Detection in Time Series Data with Keras can help Software Engineers develop their skills in using Keras to build LSTM autoencoders for anomaly detection, a critical technique in the field.

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Data Engineer

Data Engineers design and maintain data pipelines and systems. This course in Anomaly Detection in Time Series Data with Keras can help Data Engineers develop their skills in using Keras to build LSTM autoencoders for anomaly detection, a critical technique in the field.

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Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. This course in Anomaly Detection in Time Series Data with Keras can help Machine Learning Engineers develop their skills in using Keras to build LSTM autoencoders for anomaly detection, a critical technique in the field.

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Risk Manager

Risk Managers identify, assess, and mitigate risks to an organization. This course in Anomaly Detection in Time Series Data with Keras can help Risk Managers develop their skills in detecting anomalies in financial data, a critical skill in the field.

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Economist

Economists study the production, distribution, and consumption of goods and services. This course in Anomaly Detection in Time Series Data with Keras can help Economists develop their skills in using Keras to build LSTM autoencoders for anomaly detection, a critical technique in the field.

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Compliance Analyst

Compliance Analysts ensure that organizations comply with laws and regulations. This course in Anomaly Detection in Time Series Data with Keras can help Compliance Analysts develop their skills in detecting anomalies in financial data, a valuable skill in the field.

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Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty in financial and insurance matters. This course in Anomaly Detection in Time Series Data with Keras can help Actuaries develop their skills in detecting anomalies in financial data, a valuable skill in the field.

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Data Scientist

Data Scientists combine programming skills with a deep understanding of statistics and machine learning to solve complex problems and make data-driven decisions. This course in Anomaly Detection in Time Series Data with Keras can help Data Scientists build a foundation in using Keras to detect anomalies in financial data, a valuable skill in the field.

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Financial Analyst

Financial Analysts provide financial advice and guidance to individuals and businesses. This course in Anomaly Detection in Time Series Data with Keras can help Financial Analysts develop their skills in detecting anomalies in financial data, a valuable skill in the field.

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Financial Planner

Financial Planners provide financial advice and guidance to individuals and families. This course in Anomaly Detection in Time Series Data with Keras can help Financial Planners develop their skills in detecting anomalies in financial data, a valuable skill in the field.

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Data Analyst

Data Analysts use data to identify trends, patterns, and insights to support decision-making. This course in Anomaly Detection in Time Series Data with Keras can help Data Analysts develop their skills in detecting anomalies in financial data, a valuable skill in the field.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use data to improve business processes and make better decisions. This course in Anomaly Detection in Time Series Data with Keras can help Business Analysts develop their skills in detecting anomalies in financial data, a valuable skill in the field.

See salaries and explore the career path for Business Analyst