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Course Outline
Introduction
What is AI?
- Computational Psychology
- Computational Philosophy
Machine Learning
- Computational learning theory
- Computer algorithms for computational experience
Deep Learning
- Artificial neural networks
- Deep learning vs. machine learning
Preparing the Development Environment
- Installing and configuring Mathematica
Machine Learning
- Importing and separating data
- Normalizing and interpolating data
- Grouping and sorting elements
Predictors and Classifiers
- Working with a linear model
- Representing a data set
- Generating a sequence of values
Supervised Machine Learning
- Implementing supervised tasks
- Using the training data
- Measuring performance
- Identifying clusters
Summary and Conclusion
Requirements
- An understanding of Mathematica
Audience
- Data Scientists
14 Hours
Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
