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Python With Machine Learning Course
Unlock the full potential of Python and machine learning with our thorough course specifically designed for tech professionals like you. Learn feature engineering, mastering how to scale data, normalise it, and handle categorical variables properly. Improve your skills with model optimisation methods such as random search and hyperparameter tuning. Use data from sources like UCI and Kaggle, and become proficient in algorithms such as random forests and decision trees. Enhance your expertise with practical, high-quality training made for solving real-world problems.
- Master feature engineering: Learn how to scale data, normalise it, and create features based on time.
- Optimise models: Learn hyperparameter tuning and grid search methods.
- Find good data: Use UCI and Kaggle to get the data you need.
- Use algorithms well: Explore Random Forests, Decision Trees, and Linear Regression.
- Evaluate models: Use MAE, RMSE, and data splitting to analyse performance.
from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Unlock the full potential of Python and machine learning with our thorough course specifically designed for tech professionals like you. Learn feature engineering, mastering how to scale data, normalise it, and handle categorical variables properly. Improve your skills with model optimisation methods such as random search and hyperparameter tuning. Use data from sources like UCI and Kaggle, and become proficient in algorithms such as random forests and decision trees. Enhance your expertise with practical, high-quality training made for solving real-world problems.
Elevify advantages
Develop skills
- Master feature engineering: Learn how to scale data, normalise it, and create features based on time.
- Optimise models: Learn hyperparameter tuning and grid search methods.
- Find good data: Use UCI and Kaggle to get the data you need.
- Use algorithms well: Explore Random Forests, Decision Trees, and Linear Regression.
- Evaluate models: Use MAE, RMSE, and data splitting to analyse performance.
Suggested summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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