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Snehan Kekre
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...
Read more
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...
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/ax5jib/anomaly
Good to know
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
Save this course
Save Anomaly Detection in Time Series Data with Keras to your list so you can find it easily later:
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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
Show steps
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
Show steps
-
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
Show steps
Review fundamental Python skills, ensuring proficiency before starting the course.
Browse courses on
Python
Show steps
-
Go through Python tutorials and exercises
-
Solve coding challenges and practice writing Python code
Build a Simple Time Series Model
Show steps
This activity will help you develop a foundational understanding of building LSTM Autoencoders using Keras with Tensorflow before the course begins.
Show steps
-
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
Expand to see all activities and additional details
Show all 13 activities
Read 'Deep Learning with Python' by François Chollet
Show steps
Supplement course material with a comprehensive book on deep learning, providing a deeper theoretical foundation.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read and understand key concepts from the book
-
Apply concepts to the course material
Attend a Workshop on Advanced Anomaly Detection Techniques
Show steps
Attending workshops allows you to learn from experts in the field and gain practical experience.
Show steps
-
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
Show steps
Enhance understanding of Keras and TensorFlow by following guided tutorials, solidifying concepts crucial to the course.
Browse courses on
Keras
Show steps
-
Explore official Keras and TensorFlow tutorials
-
Complete hands-on exercises and projects using Keras and TensorFlow
Practice Anomaly Detection Techniques
Show steps
This hands-on activity allows you to refine your anomaly detection skills and gain practical experience with the tools used in the course.
Show steps
-
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
Show steps
Engage with peers to clarify concepts, share insights, and reinforce learning through discussion and collaboration.
Show steps
-
Actively participate in discussion forums
-
Ask questions and engage in Q&A sessions
Solve anomaly detection practice problems
Show steps
Enhance problem-solving skills by practicing anomaly detection in time series data, solidifying concepts through repetitive exercises.
Browse courses on
Anomaly Detection
Show steps
-
Solve practice problems on anomaly detection
-
Analyze and interpret results
Volunteer as a Mentor for Beginner Anomaly Detection Learners
Show steps
Sharing your knowledge with others helps reinforce your understanding and provides a valuable service to the community.
Browse courses on
Mentoring
Show steps
-
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
Show steps
Apply course concepts by creating an LSTM autoencoder model, actively reinforcing learning through hands-on implementation.
Show steps
-
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
Show steps
Collaborating on an open-source project is a great way to further refine your understanding and contribute to the community.
Browse courses on
GitHub
Show steps
-
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
Show steps
Apply course concepts to a real-world problem, demonstrating mastery and practical implementation skills.
Browse courses on
Anomaly Detection
Show steps
-
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
Review Course Materials
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
Review fundamental Python skills, ensuring proficiency before starting the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Go through Python tutorials and exercises
- Solve coding challenges and practice writing Python code
Build a Simple Time Series Model
Show steps
This activity will help you develop a foundational understanding of building LSTM Autoencoders using Keras with Tensorflow before the course begins.
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all 13 activities
Read 'Deep Learning with Python' by François Chollet
Show steps
Supplement course material with a comprehensive book on deep learning, providing a deeper theoretical foundation.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read and understand key concepts from the book
- Apply concepts to the course material
Attend a Workshop on Advanced Anomaly Detection Techniques
Show steps
Attending workshops allows you to learn from experts in the field and gain practical experience.
Show steps
Show steps
Show steps
- 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
Show steps
Enhance understanding of Keras and TensorFlow by following guided tutorials, solidifying concepts crucial to the course.
Browse courses on
Keras
Show steps
Show steps
Show steps
- Explore official Keras and TensorFlow tutorials
- Complete hands-on exercises and projects using Keras and TensorFlow
Practice Anomaly Detection Techniques
Show steps
This hands-on activity allows you to refine your anomaly detection skills and gain practical experience with the tools used in the course.
Show steps
Show steps
Show steps
- 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
Show steps
Engage with peers to clarify concepts, share insights, and reinforce learning through discussion and collaboration.
Show steps
Show steps
Show steps
- Actively participate in discussion forums
- Ask questions and engage in Q&A sessions
Solve anomaly detection practice problems
Show steps
Enhance problem-solving skills by practicing anomaly detection in time series data, solidifying concepts through repetitive exercises.
Browse courses on
Anomaly Detection
Show steps
Show steps
Show steps
- Solve practice problems on anomaly detection
- Analyze and interpret results
Volunteer as a Mentor for Beginner Anomaly Detection Learners
Show steps
Sharing your knowledge with others helps reinforce your understanding and provides a valuable service to the community.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- 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
Show steps
Apply course concepts by creating an LSTM autoencoder model, actively reinforcing learning through hands-on implementation.
Show steps
Show steps
Show steps
- 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
Show steps
Collaborating on an open-source project is a great way to further refine your understanding and contribute to the community.
Browse courses on
GitHub
Show steps
Show steps
Show steps
- 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
Show steps
Apply course concepts to a real-world problem, demonstrating mastery and practical implementation skills.
Browse courses on
Anomaly Detection
Show steps
Show steps
Show steps
- 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 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.
Quantitative 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.
Statistician
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.
Software Engineer
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.
Data Engineer
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.
Machine Learning Engineer
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.
Risk Manager
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.
Economist
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.
Compliance Analyst
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.
Actuary
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.
Data Scientist
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.
Financial Analyst
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.
Financial Planner
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.
Data Analyst
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.
Business 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.
For more career information including salaries, visit:
OpenCourser.com/course/ax5jib/anomaly
Reading list
We've selected nine 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
Anomaly Detection in Time Series Data with Keras.
A comprehensive and advanced textbook on time series analysis, covering advanced topics such as state-space models, Bayesian analysis, and nonlinear time series. Used as a textbook in graduate-level courses.
A comprehensive and advanced textbook on time series analysis using state-space methods. Covers theory, modeling, and forecasting techniques. Used as a textbook in graduate-level courses.
Covers the fundamentals and provides a detailed exposition of modeling time series. Discusses classical and modern methods, including ARMA, ARIMA, GARCH, and time-varying models. Serves as a reference and advanced textbook for researchers and practitioners in econometrics, statistics, finance, and other disciplines. Demonstrates methods using R and Python.
A classic textbook on time series analysis, covering both theoretical and practical aspects. Includes chapters on ARMA, ARIMA, seasonal models, and forecasting. Used as a textbook in many university courses.
Focuses on deep learning techniques for time series analysis, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and graph neural networks (GNNs). Covers model architectures, training algorithms, and evaluation metrics. A good choice for practitioners looking to apply deep learning to time series data.
Focuses on time series analysis and forecasting using the statistical programming language R. Covers time series analysis, forecasting techniques, and econometric modeling. Suitable for practitioners and researchers in economics, finance, and data science.
Save
A practical guide to time series analysis using R. Covers data exploration, model building, and forecasting. Includes examples and case studies from various disciplines.
Save
Provides a comprehensive overview of time series forecasting techniques. Covers both classical and advanced methods, including exponential smoothing, ARMA, ARIMA, and machine learning algorithms. Includes case studies and examples to illustrate practical applications.
Provides a practical introduction to time series analysis using Python. Covers data exploration, feature engineering, and model building. Includes hands-on examples and case studies to illustrate best practices.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ax5jib/anomaly
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Time
1 hours
Via
Coursera
Institutions
Rhyme
Coursera Project Network
Instructor
Snehan Kekre
Good to know
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
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