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Course Outline
Module I: Regression Models
1. Basics of regression using the linear model example
2. Optimization using the least squares method
3. Practical implementation using scikit-learn
4. Measures of regression model quality
5. Overview of other regression methods
Module II: Data Preparation for Modeling
1. Feature engineering
2. Scaling and standardizing variables
3. Identifying and eliminating outliers
4. Strategies for imputing missing values
5. Dimensionality reduction and feature selection methods
6. Encoding categorical variables (one-hot encoding, label encoding)
Module III: The Problem of Overfitting
1. The phenomenon of overfitting and its consequences
2. Techniques to prevent overfitting
3. Cross-validation as a tool for model evaluation
4. Regularization in machine learning models
Module IV: Optimization of the Learning Process
1. Hyperparameter tuning using grid search
2. Building data processing pipelines
Module V: Classification Algorithms
1. Introduction to classification using logistic regression
2. Comparison of linear and non-linear models
3. Evaluation metrics for classifiers
4. Decision tree algorithm
5. Naive Bayes classifier
6. Support Vector Machine (SVM)
7. k-Nearest Neighbors (KNN) method
8. Multiclass classification issues
9. Ensemble methods—Random Forest and Gradient Boosting
21 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.
