Software Engineering
Java Programming
Core Java, object-oriented design and the standard library for building reliable applications.
AI and Data Science
Machine Learning Fundamentals
Supervised and unsupervised learning, model evaluation and a practical workflow in Python.
Course overview
A practical grounding in machine learning for engineers and analysts. The program covers how common algorithms work, how to prepare data, how to evaluate models honestly, and how to avoid the mistakes that make models fail outside the notebook.
What participants will learn
- Frame business problems as machine learning tasks
- Prepare and explore data with pandas
- Train regression, classification and clustering models with scikit-learn
- Evaluate models with appropriate metrics and validation
- Recognize overfitting, leakage and bias
Indicative outline
The final syllabus is agreed with each organization and can be adjusted to your requirements.
- 01
Foundations
- Types of learning
- The ML workflow
- Statistics refresher
- 02
Data preparation
- Cleaning and feature engineering
- Train/test splits
- Pipelines
- 03
Algorithms
- Linear and logistic regression
- Decision trees and ensembles
- k-means clustering
- 04
Evaluation
- Metrics
- Cross-validation
- Introduction to neural networks
Who it's for
- Developers and analysts moving into data science
- Technical managers overseeing ML initiatives
Prerequisites
- Working knowledge of Python
- Basic statistics
Related courses
- Software Engineering
Python Programming
Readable, practical Python for scripting, automation and application development.
- Cloud Computing
AWS Cloud Fundamentals
Core AWS services, the shared responsibility model and how to design simple, secure workloads.
Planning Machine Learning Fundamentals for your team?
Share your team's size, experience and goals. We'll discuss how to adapt this program to your requirements.