Mathematics For Machine Learning Course
What will I learn?
Sharpen your Business Intelligence skills with our Mathematics for Machine Learning Course. Take a deep dive into data exploration, becoming proper experts at spotting outliers and dealing with missing values. Learn data preprocessing, including normalization and how to handle outliers, so your models dey perform proper. Explore machine learning algorithms for time series, like decision trees and ARIMA. You go gain proper expertise in feature engineering, optimization techniques, and how to evaluate models. This course go give you practical, top-quality skills wey you fit use for real work situations.
Apoia's Unique Features
Develop skills
Enhance the development of the practical skills listed below
Master data structures: Analyze and understand complex data sets well-well.
Detect outliers: Find anomalies so your data be accurate and reliable.
Apply time series models: Use ARIMA and LSTM for correct forecasting.
Optimize algorithms: Use gradient descent make model training quick and effective.
Engineer features: Create polynomial features so your model perform better.
Suggested summary
Workload: between 4 and 360 hours
Before starting, you can modify the chapters and workload.
- Select which chapter to begin with
- Add or remove chapters
- Increase or decrease the course workload
Examples of chapters you can include
You can generate additional chapters like 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 offers practical and relevant knowledge for your professional journey.