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Introduction
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Decision Trees
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Decision Boundaries
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Artificial Neural Networks
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Hyperparameters Optimization
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Word Embeddings
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Variational Autoencoders
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Explainable AI
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Gradio: Graphical Interfaces For Machine Learning Models
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Conclusion
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End of Course Survey
Machine Learning Visualization
Create ML visualizations using different techniques.
As part of any data science project, data visualization plays an important part in order to learn more about the available data and to identify any main pattern. Wouldn't it be great to also make as visually intuitive as possible the machine learning part of the analysis?
In this course, we are going to explore some techniques that could help us to face this challenge, such as summarizing Hyperparameter Tuning searches, drawing ANNs graphs, creating visual interfaces for ML models and many more.
What you'll learn—and how you can apply it
By the end of this hands-on course, you'll understand:
- How to present your ML analysis using visualizations
- How to use visualizations to interpret your ML model
- How to make visualizations interactive and sharable online
And you'll be able to:
- Create ML visualizations using different techniques
- Better communicate with non-technical stakeholders
- Become familiar with popular open source data visualization libraries
This training is for you because...
- You're a data scientist.
- You work with Python and ML/DL.
- You want to become an ML specialist working in a customer facing environment.
Prerequisites
- Experience working with Python
- Basic understanding of Machine Learning, Data Literacy, and Data Visualization
Setup
To open Anaconda Notebooks:
- Go to https://anaconda.cloud
- Click on 'Notebooks' from the top navigation menu
- Create an account or login if you already have one
Facilitator bio
Pier Paolo Ippolito is founder of PPI Consulting and MSc in Artificial Intelligence holder with an interest in research areas such as Data Science, Machine Learning, and Cloud Development. Pier is also a book co-author and technical writer for Towards Data Science (1M+ views).