Course Outline
Introduction
- Building effective algorithms for pattern recognition, classification, and regression.
Setting Up the Development Environment
- Python libraries
- Online versus offline editors
Overview of Feature Engineering
- Input and output variables (features)
- Advantages and disadvantages of feature engineering
Common Issues in Raw Data
- Unclean data, missing data, and other challenges.
Pre-Processing Variables
- Addressing missing data
Handling Missing Values in the Data
Working with Categorical Variables
Converting Labels into Numbers
Managing Labels in Categorical Variables
Transforming Variables to Improve Predictive Power
- Numerical, categorical, date, and other types.
Cleaning a Dataset
Machine Learning Modelling
Handling Outliers in Data
- Numerical variables, categorical variables, and more.
Summary and Conclusion
Requirements
- Experience with Python programming.
- Familiarity with Numpy, Pandas, and scikit-learn.
- Understanding of machine learning algorithms.
Audience
- Developers
- Data scientists
- Data analysts
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.
