Course Outline
Introduction
- Building effective algorithms for pattern recognition, classification, and regression.
Setting Up the Development Environment
- Python libraries
- Online vs. offline editors
Feature Engineering Overview
- Input and output variables (features)
- Advantages and disadvantages of feature engineering
Types of Problems in Raw Data
- Unclean data, missing data, etc.
Pre-Processing Variables
- Addressing missing data
Handling Missing Values in Data
Working with Categorical Variables
Converting Labels to Numbers
Managing Labels in Categorical Variables
Transforming Variables to Enhance Predictive Power
- Numerical, categorical, date, etc.
Cleaning a Dataset
Machine Learning Modeling
Handling Outliers in Data
- Numerical variables, categorical variables, etc.
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.
