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Python For Machine Learning Course
Open the power of Python for machine learning with our full course made for tech people. Go deep into regression methods like Random Forests and Decision Trees, become good at checking model performance using things like RMSE and MAE, and learn data cleaning ways including making features the same size and changing them into numbers. Make your skills better with picking the best features, writing good project notes, and using Python tools like NumPy and Pandas. Make models work better with tuning their settings and using group methods. Join now to make yourself a machine learning expert.
- Become a master of regression: Use Random Forests, Decision Trees, and Linear Regression.
- Check models: Use RMSE, MAE, and cross-validation to see how well they work.
- Clean data: Make features the same size, deal with missing data, and change categories into numbers.
- Make models better: Use hyperparameter tuning, group methods, and smart search ways.
- Look at data: Use NumPy, Pandas, Matplotlib, and Seaborn to understand data.
from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Open the power of Python for machine learning with our full course made for tech people. Go deep into regression methods like Random Forests and Decision Trees, become good at checking model performance using things like RMSE and MAE, and learn data cleaning ways including making features the same size and changing them into numbers. Make your skills better with picking the best features, writing good project notes, and using Python tools like NumPy and Pandas. Make models work better with tuning their settings and using group methods. Join now to make yourself a machine learning expert.
Elevify advantages
Develop skills
- Become a master of regression: Use Random Forests, Decision Trees, and Linear Regression.
- Check models: Use RMSE, MAE, and cross-validation to see how well they work.
- Clean data: Make features the same size, deal with missing data, and change categories into numbers.
- Make models better: Use hyperparameter tuning, group methods, and smart search ways.
- Look at data: Use NumPy, Pandas, Matplotlib, and Seaborn to understand data.
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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