Mathematics For Machine Learning Course
What will I learn?
Boost your Business Intelligence skills with our Mathematics for Machine Learning Course. Get stuck into data exploration, mastering techniques to spot outliers and deal with missing values. Learn data preprocessing, including normalisation and outlier treatment, to enhance model accuracy. Explore machine learning algorithms for time series data, such as decision trees and ARIMA. Gain expertise in feature engineering, optimisation techniques, and model evaluation. This course gives you practical, high-quality skills for real-world applications.
Apoia's Differentials
Develop skills
Strengthen the development of the practical skills listed below
Master data structures: Analyse and interpret complex data sets effectively.
Detect outliers: Identify anomalies to enhance data accuracy and reliability.
Apply time series models: Use ARIMA and LSTM for precise forecasting.
Optimise algorithms: Implement gradient descent for efficient model training.
Engineer features: Create polynomial features to improve model performance.
Suggested summary
Workload: between 4 and 360 hours
Before starting, you can change the chapters and the 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 like the examples below
This is a free course, focused on 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.