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Signal Preprocessing and Feature Extraction for Data Analytics with MATLAB
Signal Processing and Communications
Signal Preprocessing and Feature Extraction
for Data Analytics with MATLAB
Learn to preprocess time-based signals and extract features in time and frequency domains for machine learning applications
Complimentary Services: Post training email support & 1-hr consultation session within 1 month after the course completion!
Course
Highlights
This one-day course shows how to use MATLAB®, Signal Processing Toolbox™, and Wavelet Toolbox™ to preprocess time-based signals and extract key features in the time and frequency domains. This course is intended for data scientists and engineers analyzing signals (time series) for data analytics applications. No prior knowledge on signal processing is needed for this course.
Topics include:
- Creating, importing, and visualizing signals
- Preprocessing to improve data quality, including filling data gaps, resampling, smoothing, aligning signals, finding and removing outliers, and handling non-uniformly sampled signals
- Extracting features in the time and frequency domains, including finding patterns in signals, finding change points, locating peaks, and identifying trends
Who Should
Attend
Mathematicians, statisticians and engineer with statistics domain expertise but little signal processing or machine learning expertise that needs to apply machine learning to time-based data.
Course
Prerequisites
MATLAB Fundamentals or equivalent experience using MATLAB
Course
Benefits
Upon the completion of the course, the participants will gain skills to:
- Import, visualize and browse signals to gain insights
- Preprocess signals to improve data sets quality
- Extract features from signals
Partners
Upcoming Program
- Please keep me posted on the next schedule
- Please contact me to arrange customized/ in-house training
Course Outline
Explore and Analyze Signals (Time Series) in MATLAB
Objective: Learn to easily import and visualize multiple signals or time series data sets to gain insights into the features and trends in the data.
- Import, visualize, and browse signals to gain insights
- Make measurements on signals
- Compare multiple signals in the time and frequency domain
- Perform interactive spectral analysis
- Extract regions of interest for focused analysis
- Recreate analysis with auto-generated MATLAB scripts
Preprocess Signals to Improve Data Set Quality
Objective: Learn techniques to clean signal sets with operations such as resampling, removing outliers, and filling gaps.
- Perform resampling to ensure a common time base across signals
- Work with non-uniformly sampled data
- Find gaps in data and remove or fill gaps
- Remove noise and unwanted frequency content
- Perform wavelet denoising
- Use the envelope spectrum to perform fault analysis
- Locate outlier values in data and replace them with acceptable data
- Locate signal changepoints and use boundaries to automatically create signal segments
Extract Features from Signals
Objective: Apply different techniques in time and frequency domains to extract features. Become familiar with the spectral analysis tools in MATLAB and explore ways to bring out features for multiple signals.
- Locate peaks
- Locate desired signals from patterns in the time and spectral domains
- Use spectral analysis to extract features from signals
- Perform classification using supervised learning
- Use the Classification Learner app to interactively train and evaluate classification algorithms
