Kursplan
1. Introduction to Machine Learning
- What is Machine Learning
- How it extends data analysis
-
Common business use cases:
- Sales forecasting
- Customer segmentation
- Churn prediction
2. From Data Analysis to Machine Learning
- Recap: working with data in Pandas
- Moving from descriptive to predictive analysis
- Defining a Machine Learning problem
3. Machine Learning Workflow (Simplified)
- Preparing the dataset
- Splitting data (train vs test)
- Training a model
- Making predictions
4. Data Preparation for Machine Learning
- Handling missing values
- Encoding categorical variables
- Feature selection (basic)
- Scaling (conceptual overview)
5. Supervised Learning (Hands-on)
Regression
- Linear Regression
- Use case: predicting numerical values (e.g. sales, demand)
Classification
- Logistic Regression
- Use case: binary outcomes (e.g. churn, fraud)
6. Unsupervised Learning
Clustering
- K-means clustering
- Use case: customer segmentation
7. Model Evaluation (Simplified)
- Train vs test performance
- Accuracy (classification)
- Basic error understanding (regression)
8. Interpreting Results
- Understanding model outputs
- Identifying patterns and trends
- Translating results into business insights
9. Practical End-to-End Example
- Load dataset
- Prepare and clean data
- Train a model
- Evaluate performance
- Extract insights
Krav
Prerequisites
- Basic Python knowledge
- Familiarity with Pandas and working with datasets
- Understanding of basic data analysis concepts
Target Audience
- Data Analysts
- Business Analysts with basic Python knowledge
- Professionals who completed Python for Data Analysis or equivalent
- Beginners in Machine Learning
Referanser (2)
ML-økosystemet omfatter ikke bare MLFlow, men også Optuna, Hyperopt, Docker og Docker Compose
Guillaume GAUTIER - OLEA MEDICAL
Kurs - MLflow
Maskinoversatt
Jeg nyttet på å delta i Kubeflow-treningen, som ble holdt pa nätet. Denne treningen gjorde det mulig for meg å fest igjen kunnskapen min om AWS-tjenester, K8s og alle devOps-verktøyene rundt Kubeflow, som er de nødvendige grunnlagene for å tilnærme seg emnet på riktig måte. Jeg ønsker å takke Malawski Marcin for hans tålmodighet og profesjonale innstilling når det gjelder trening og råd om beste praksis. Malawski tilnærmer seg emnet fra ulike vinkler, med ulike distribusjonsverktøy som Ansible, EKS kubectl, Terraform. Nå er jeg helt overbevist om at jeg går inn i det riktige anvendelsesfeltet.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life TM
Kurs - Kubeflow
Maskinoversatt
