Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Advanced Machine Learning Concepts
Capstone Project
Introduction to Machine Learning and Google Colab
Machine Learning Project Workflow
Special Topics in Machine Learning
Summary and Next Steps
Supervised Learning with Scikit-learn
Unsupervised Learning Techniques
- Clustering algorithms
- Dimensionality reduction
- Association rule learning
- Data preprocessing
- Model selection
- Model deployment
- Defining the problem statement
- Data collection and cleaning
- Model training and evaluation
- Feature engineering
- Hyperparameter tuning
- Model interpretability
- Neural networks and deep learning
- Support vector machines
- Ensemble methods
- Overview of machine learning
- Setting up Google Colab
- Python refresher
- Regression models
- Classification models
- Model evaluation and optimization
Requirements
Audience
- An understanding of basic programming concepts
- Experience with Python programming
- Familiarity with basic statistical concepts
- Data scientists
- Software developers
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.
