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Course Outline
Introduction to Machine Learning and Google Colab
- Overview of machine learning
- Setting up Google Colab
- Python refresher
Supervised Learning with Scikit-learn
- Regression models
- Classification models
- Model evaluation and optimization
Unsupervised Learning Techniques
- Clustering algorithms
- Dimensionality reduction
- Association rule learning
Advanced Machine Learning Concepts
- Neural networks and deep learning
- Support vector machines
- Ensemble methods
Special Topics in Machine Learning
- Feature engineering
- Hyperparameter tuning
- Model interpretability
Machine Learning Project Workflow
- Data preprocessing
- Model selection
- Model deployment
Capstone Project
- Defining the problem statement
- Data collection and cleaning
- Model training and evaluation
Summary and Next Steps
Requirements
- An understanding of basic programming concepts
- Experience with Python programming
- Familiarity with basic statistical concepts
Audience
- Data scientists
- Software developers
14 Hours
Testimonials (1)
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.
