Sequences, Time Series and Prediction
With an emphasis on time series prediction using RNNs and ConvNets, this course educates software developers on how to create scalable AI models using TensorFlow.
Description for Sequences, Time Series and Prediction
-
TensorFlow Best Practices: Instructs on fundamental methodologies for utilizing TensorFlow, an open-source machine learning framework, to develop scalable models.
-
Time Series Modeling: Concentrates on constructing time series models, encompassing data preparation methodologies and employing RNNs and 1D ConvNets for forecasting.
-
Practical Application: Utilizes acquired skills on real-world data, exemplified by the construction of a sunspot prediction model.
-
Fundamental Knowledge: Expands upon concepts from Andrew Ng's Machine Learning course and the Deep Learning Specialization, offering an enhanced comprehension of neural networks and model execution using TensorFlow.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by DeepLearning.AI
Â
Duration:21 hours (approximately)
Schedule: Flexible
Pricing for Sequences, Time Series and Prediction
Use Cases for Sequences, Time Series and Prediction
FAQs for Sequences, Time Series and Prediction
Reviews for Sequences, Time Series and Prediction
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Sequences, Time Series and Prediction
The AI tool empowers non-programmers to construct and deploy AI, featuring data transformation, insights generation, identification of critical drivers, and prediction and forecasting functionalities to enhance business decision-making and planning processes.
This AI Forecast tool, powered by machine learning, offers accurate forecasts for business needs, featuring automated data processing, customizable models, and seamless integration with AWS, yet novices may find its ML-based approach challenging, and data transfer costs may apply.
This helps to integrates data from various restaurant systems, utilizes advanced analytics for decision-making, and provides real-time insights to optimize operations and profitability effortlessly.
Explore the use of generative AI tools to enhance data preparation, querying, and machine learning model development in data science workflows through hands-on projects.
Enhance your software development career with Gen AI. Develop hands-on, in-demand Generative AI skills to elevate your software engineering game in one month or less.
Utilize generative AI to advance in the field of data science. Develop hands-on generative AI skills that are in high demand to accelerate your data science career in under one month.
Develop generative AI capabilities for data analytics. Learn about generative AI to advance your career as a data analyst! No prior experience is required.
Comprehend generative AI basics, practical applications, ethical implications, and potential impacts on student learning in education.
Enhance your Product Manager career with Gen AI. Boost your Product Manager career in under two months by developing hands-on, in-demand generative AI skills. No prior experience is required to initiate the process.
This course will provide you with an understanding of the technical underpinnings and essential terminology associated with generative artificial intelligence (AI).
Featured Tools
Gain a foundational understanding of machine learning and its applications, collaborate with AI professionals, and complete a practical project to train and optimize a model.
This course delves deeply into AI bias, equipping students with the knowledge they need to design responsible and ethical AI systems.
Create a final presentation to evaluate peer projects, train neural networks for regression and classification, and develop Python-based recommender systems. Additionally, employ KNN, PCA, and collaborative filtering.
In brief, this course uses scikit-learn and actual athletic data to investigate classification and regression techniques in sports analytics.
Insights into the AI employment market, ethical considerations, and productivity enhancements are among the essential AI knowledge that the course delivers to learners.