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Mathematics For Machine Learning Course
Improve your Business Intelligence skills with our Mathematics for Machine Learning Course. You'll learn to explore data properly, getting good at finding odd things (outliers) and dealing with missing information. You'll also learn how to prepare data, including making sure everything is on the same scale (normalization) and dealing with outliers, so your models work better. We'll look at machine learning ways for dealing with data that changes over time (time series), like decision trees and ARIMA. You'll become skilled at creating new features from your data, making your models better, and checking how well your models are working. This course will give you real, useful skills that you can use in the real world.
- Become good at data structures: Understand and explain complicated data sets well.
- Find outliers: Spot unusual things in the data to make sure it's correct and reliable.
- Use time series models: Use ARIMA and LSTM to make good guesses about the future.
- Make algorithms better: Use gradient descent to train your models quickly.
- Create features: Make new polynomial features to make your models work better.

from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Improve your Business Intelligence skills with our Mathematics for Machine Learning Course. You'll learn to explore data properly, getting good at finding odd things (outliers) and dealing with missing information. You'll also learn how to prepare data, including making sure everything is on the same scale (normalization) and dealing with outliers, so your models work better. We'll look at machine learning ways for dealing with data that changes over time (time series), like decision trees and ARIMA. You'll become skilled at creating new features from your data, making your models better, and checking how well your models are working. This course will give you real, useful skills that you can use in the real world.
Elevify advantages
Develop skills
- Become good at data structures: Understand and explain complicated data sets well.
- Find outliers: Spot unusual things in the data to make sure it's correct and reliable.
- Use time series models: Use ARIMA and LSTM to make good guesses about the future.
- Make algorithms better: Use gradient descent to train your models quickly.
- Create features: Make new polynomial features to make your models work better.
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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