Description
Studying Machine Learning (ML) can boost the development of cutting-edge solutions in a variety of industries. As data-driven decisions increasingly make a difference in the modern business world, almost every organization seeks to introduce some form of machine learning in their business environment and everyday processes these days. That way they can successfully predict future outcomes by using current and historical data and take appropriate decisions and actions based on those predictions.
The Code.Learn Machine Learning program, certified by Athens Tech College, is designed to equip participants with the required knowledge to understand all core concepts of Machine Learning and gain all skills needed to fully create pipelines using Python as a programming language and popular machine learning libraries. At the end of the training, the participants will be well aware of the most important state-of-the-art tools, techniques, and technologies in the field of Machine Learning.
Key Objectives
The key learning objectives of this program can be summarized as follows:
- Machine Learning Concepts and Principles
- Python and Data Structures
- Introduction to Numpy, Pandas and Matplotlib
- Scikit-learn
- Exploratory Data analysis
- Data preprocessing
- Supervised Learning – Regression and Classification
- Metrics, Bias-Variance tradeoff, Confusion matrix, Classification report
- Hyperparameter tuning
- Grid search and pipeline
- Unsupervised Learning
Target Audience
Higher education graduates in one of the following fields:
- Computer Science,
- Ιnformatics,
- Software Engineering,
- Web and Mobile Development,
- Computer Engineering,
- or any other relevant area
Prerequisite Knowledge
Some familiarity with any programming language, especially an object-oriented one, will be beneficial, but not required.
Classroom
Sessions can be carried out:
- Live in a physical classroom
- Live online through video conferencing environments
- Using a Hybrid combination of both live physical and online approaches
The teaching method will depend on the conditions at the time the training will run and on the participants’
preferences.
