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Course Outline
- Introduction to ML
- Machine learning as part of Artificial intelligence
- Types of ML
- ML algorithms
- Challenges and potential use of ML
- Overfitting and bias-variance trade-off in ML
- Techniques of Machine learning
- The Machine Learning Workflow
- Supervised learning – Classification, Regression
- Unsupervised learning – Clustering, Anomaly detection
- Semi-supervised learning and Reinforcement Learning
- Consideration in Machine Learning
- Data Preprocessing
- Data preparation and transformation
- Feature engineering
- Feature Scaling
- Dimensionality reduction and variable selection
- Data visualization
- Exploratory analysis
- Case studies
- Advanced feature engineering and impact on results in linear regression for prediction
- Time series analysis and Forecasting monthly volume of sales - basic methods, seasonal adjustment, regression, exponential smoothing, ARIMA, neural networks
- Market basket analysis and association rules mining
- Segmentation analysis using clustering and self-organising maps
- Classification which customer is likely to default using logistic regression, decision trees, xgboost, svm
Requirements
Knowledge and awareness of Machine Learning fundmentals
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.
