Durée de la formation : 1,94h
If you’re looking to keep up with the rapid advancements and applications of deep learning techniques, this course provides a comprehensive guide that can help you stay relevant and competitive in the evolving landscape of AI and data-driven technologies. Instructor Gwendolyn Stripling shows you how to transform raw data into valuable insights and build the foundation for cutting-edge AI applications. The course focuses on the concepts, with minimal coding required, so even if you’re not an experienced coder, Gwendolyn shows you how to use simple Python code to work with data. Test your learning with a series of challenges, and cap off the course with building and evaluating a predictive and generative model.
Topics include:
- Identify common applications of deep learning and generative AI in various fields such as computer vision, natural language processing, and healthcare.
- Evaluate the quality of a dataset and make informed decisions about data preprocessing strategies based on factors such as data distribution, imbalance, and outliers.
- Understand data preprocessing, cleaning, transformation, exploratory data analysis, feature engineering, and data augmentation in training effective generative AI models.
- Distinguish between the goals of predictive AI and generative AI, understand the methodologies employed in each paradigm, and identify the unique outputs generated by predictive models versus generative models.
- Create data visualizations using Python libraries like Matplotlib and Seaborn, depicting data distributions, trends, and relationships.
- Explore data analysis techniques, such as statistical analysis and visualizations, to structured and unstructured data to understand data distributions, identify outliers, and detect correlations.
Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.