What will you learn in this Sequences, Time Series and Prediction Course
Implement best practices for preparing time series data for machine learning.
Build and train deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs) for time series forecasting.
Apply techniques like moving averages, differencing, and windowing to enhance model performance.
Develop a real-world sunspot activity prediction model using TensorFlow.
Program Overview
1. Sequences and Prediction
⏳ 5 hours
Introduction to time series data, forecasting methods, and evaluation metrics. Includes hands-on labs on time series forecasting and moving averages.
2. Deep Neural Networks for Time Series
⏳ 5 hours
Covers windowing techniques, feature-label preparation, and training DNNs for time series prediction.
3. Recurrent Neural Networks for Time Series
⏳ 5 hours
Focuses on building and training RNNs and LSTMs for sequential data modeling.
4. Real-world Time Series Data
⏳ 5 hours
Applies learned techniques to real-world data, including sunspot activity, using combined models like CNNs and RNNs
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Job Outlook
Equips learners for roles such as Machine Learning Engineer, Data Scientist, and AI Specialist.
Applicable in industries like finance, healthcare, and technology where time series forecasting is crucial.
Enhances skills in building scalable AI-powered algorithms using TensorFlow
Specification: Sequences, Time Series and Prediction
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