Mathematics For Machine Learning Course
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.
Apoia's Unique Offerings
Develop your skills
Enhance your practical skills listed below
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
Workload: between 4 and 360 hours
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
Examples of chapters you can add
You will be able to generate more chapters similar to the examples below
This is a free course aimed at personal and professional development. It is not equivalent to a technical, undergraduate, or postgraduate course, but provides practical and relevant knowledge for your professional journey.