❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [August 31st, 2023]

Skills and Knowledge:
1. Best practices for preparing time series data.
2. Understanding of Recurrent Neural Networks (RNNs) and 1D Convolutional Neural Networks (1D ConvNets).
3. Ability to build and apply scalable models to real-world problems using TensorFlow.
4. Knowledge of sunspot prediction models and how to use real-world data to build them.
5. Understanding of the most important and foundational principles of Machine Learning and Deep Learning.

Professional Growth:
This course on Sequences, Time Series, and Prediction contributes to professional growth in several ways:
1. Enhanced understanding of time series data: The course teaches best practices for preparing time series data, which is a common type of data in many industries. Understanding how to handle and analyze time series data can be valuable in various professional roles.
2. Knowledge of advanced machine learning techniques: The course covers the use of recurrent neural networks (RNNs) and 1D Convolutional Neural Networks (ConvNets) for prediction. These are advanced techniques that can be applied to a wide range of problems. Acquiring knowledge of these techniques can make a software developer more versatile and capable of solving complex problems.
3. Practical experience with TensorFlow: TensorFlow is a popular open-source framework for machine learning. By learning how to use TensorFlow effectively, software developers can leverage its capabilities to build scalable AI-powered algorithms. This practical experience with TensorFlow can be highly valuable in professional settings where machine learning is applied.
4. Real-world application: The course provides an opportunity to apply the knowledge and skills learned throughout the specialization to build a sunspot prediction model using real-world data. This hands-on experience with real-world data and problem-solving can enhance professional growth and readiness to tackle similar challenges in the industry.
Overall, this course contributes to professional growth by providing knowledge, skills, and practical experience in time series modeling, advanced machine learning techniques, and the use of TensorFlow for building scalable models.

Further Education:
This course is suitable for preparing for further education. It covers important principles of machine learning and deep learning, and teaches how to use TensorFlow, a popular framework for machine learning. It also provides hands-on experience in building time series models and applying them to real-world problems. Taking this course can help you develop a deeper understanding of neural networks and prepare you for further education in the field of AI and machine learning.