What will you learn in this Sequences, Time Series and Prediction Course
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Implement best practices for preparing time series data for machine learning.
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Build and train deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs) for time series forecasting.
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Apply techniques like moving averages, differencing, and windowing to enhance model performance.
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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
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Equips learners for roles such as Machine Learning Engineer, Data Scientist, and AI Specialist.
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Applicable in industries like finance, healthcare, and technology where time series forecasting is crucial.
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Enhances skills in building scalable AI-powered algorithms using TensorFlow
