Mathematics For Machine Learning Course
What will I learn?
Improve your Business Intelligence skills with our Mathematics for Machine Learning Course. You go learn how to explore data well, and how to handle outliers and missing data. You go learn data preprocessing, like normalization and how to treat outliers, to make your models more accurate. We go look at machine learning algorithms for time series data, like decision trees and ARIMA. You go get good at feature engineering, optimization techniques, and how to evaluate your models. This course go give you the practical skills wey you need for real-world work.
Apoia's Unique Features
Develop skills
Enhance the development of the practical skills listed below
Understand data structures well: Analyze and interpret big data sets properly.
Find outliers: See the things wey dey different so you go make sure your data correct.
Use time series models: Use ARIMA and LSTM to predict things well for future.
Make algorithms work better: Use gradient descent to train your models fast-fast.
Build features: Create polynomial features to make your models perform well.
Suggested overview
Workload: between 4 and 360 hours
Before getting started, you can adjust the chapters and the workload.
- Choose which chapter to begin with
- Add or remove chapters
- Increase or decrease the course workload
Examples of chapters you can include
You'll be able to create more chapters like the examples below
This is a free course, focused on personal and professional growth. It is not equivalent to a technical, undergraduate, or postgraduate course, but offers practical and relevant knowledge for your professional journey.