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Introduction to Machine Learning For Data Science Course
Unleash the power of data with our "Introduction to Machine Learning for Data Science" course, ah, specially designed for Business Intelligence folks. Learn the important stuff like data preprocessing, feature engineering, and training models, can? Become a master in using algorithms like Random Forests and Gradient Boosting to predict sales. Also, learn how to tune your models by adjusting hyperparameters and evaluating them using metrics like MAE and RMSE. Power up your BI skills with practical, high-quality insights that drive business success, leh!
- Master data cleaning: Make sure your data is tip-top by removing any anyhow inconsistencies and errors.
- Develop feature engineering: Create features that geng one, for better model performance.
- Optimize models: Improve accuracy by tuning hyperparameters and comparing different algorithms.
- Visualize data insights: Use visualization tools to find insights that can be put into action.
- Evaluate models: Measure how well your model performs using metrics like MAE and RMSE.

4 to 360 hours of flexible workload
certificate recognized by the Ministry of Education
What Will I Learn?
Unleash the power of data with our "Introduction to Machine Learning for Data Science" course, ah, specially designed for Business Intelligence folks. Learn the important stuff like data preprocessing, feature engineering, and training models, can? Become a master in using algorithms like Random Forests and Gradient Boosting to predict sales. Also, learn how to tune your models by adjusting hyperparameters and evaluating them using metrics like MAE and RMSE. Power up your BI skills with practical, high-quality insights that drive business success, leh!
Elevify Advantages
Develop Skills
- Master data cleaning: Make sure your data is tip-top by removing any anyhow inconsistencies and errors.
- Develop feature engineering: Create features that geng one, for better model performance.
- Optimize models: Improve accuracy by tuning hyperparameters and comparing different algorithms.
- Visualize data insights: Use visualization tools to find insights that can be put into action.
- Evaluate models: Measure how well your model performs using metrics like MAE and RMSE.
Suggested Summary
Before starting, you can change the chapters and the 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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