DeepLearning.AI - Sequences, Time Series and Prediction
- Offered byCoursera
Sequences, Time Series and Prediction at Coursera Overview
Duration | 13 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Sequences, Time Series and Prediction at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 4 in the DeepLearning.AI TensorFlow Developer
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level You should take the first 3 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.
- Approx. 13 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Sequences, Time Series and Prediction at Coursera Course details
- If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
- In this fourth course, you will learn how to build time series models in TensorFlow. You?ll first implement best practices to prepare time series data. You?ll also explore how RNNs and 1D ConvNets can be used for prediction. Finally, you?ll apply everything you?ve learned throughout the Specialization to build a sunspot prediction model using real-world data!
- The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
Sequences, Time Series and Prediction at Coursera Curriculum
Sequences and Prediction
Introduction, A conversation with Andrew Ng
Time series examples
Machine learning applied to time series
Common patterns in time series
Introduction to time series
Train, validation and test sets
Metrics for evaluating performance
Moving average and differencing
Trailing versus centered windows
Forecasting
Introduction to time series notebook
Forecasting notebook
Week 1 Wrap up
Week 1 Quiz
Deep Neural Networks for Time Series
A conversation with Andrew Ng
Preparing features and labels
Preparing features and labels
Feeding windowed dataset into neural network
Single layer neural network
Machine learning on time windows
Prediction
More on single layer neural network
Deep neural network training, tuning and prediction
Deep neural network
Preparing features and labels notebook
Sequence bias
Single layer neural network notebook
Deep neural network notebook
Week 2 Wrap up
Week 2 Quiz
Recurrent Neural Networks for Time Series
Week 3 - A conversation with Andrew Ng
Conceptual overview
Shape of the inputs to the RNN
Outputting a sequence
Lambda layers
Adjusting the learning rate dynamically
RNN
LSTM
Coding LSTMs
More on LSTM
More info on Huber loss
RNN notebook
Link to the LSTM lesson
LSTM notebook
Week 3 Wrap up
Week 3 Quiz
Real-world time series data
Week 4 - A conversation with Andrew Ng
Convolutions
Bi-directional LSTMs
LSTM
Real data - sunspots
Train and tune the model
Prediction
Sunspots
Combining our tools for analysis
Congratulations!
Specialization wrap up - A conversation with Andrew Ng
Convolutional neural networks course
More on batch sizing
LSTM notebook
Sunspots notebook
Wrap up
What next?
Week 4 Quiz
Sequences, Time Series and Prediction at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
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