Your Course

The Machine Learning and AI course is designed to take your data analytics skills to the next level by introducing you to the powerful world of predictive modeling and artificial intelligence. Over 14 weeks, you'll learn how to build, evaluate, and deploy machine learning models to solve real-world problems. This advanced course builds on your Python and data analytics foundations, building data science, machine learning, and AI skills.

Key Course Information

• Duration: 14 weeks (March 10 - June 19, 2025)

• Schedule: Twice a week (Mondays and Wednesdays, 19:00-21:00)

• Format: Hybrid learning (online sessions with on-site activities)

• Time commitment: Approximately 15 hours per week (including sessions and independent work)

• Learning approach: Combination of taught concepts and weekly homework assignments

Course Structure

This course follows a structured learning approach where you'll develop comprehensive machine learning skills:

1. Foundations of ML - Understand ML workflows, feature engineering, and model evaluation

2. Supervised Learning - Master regression, classification, and regularization techniques

3. Unsupervised Learning - Learn clustering, dimensionality reduction, and data preprocessing

4. Final Project - Implement a complete machine learning solution to a real-world problem

The curriculum with weekly breakdowns and exercises will be shared with you in Google Classroom each week.

Weekly Structure

Each week consists of two key session types:

Monday - Session (19:00-21:00)

• Review of homework from the previous week

• Discussion of challenges and model optimization approaches

• Introduction to new ML concepts and techniques through examples

Wednesday - Session (19:00-21:00)

• Explore new machine learning algorithms through theory and practice

• Participate in guided exercises implementing ML models

• Receive homework assignments to reinforce your learning

Between sessions: You'll work independently on weekly homework, spending approximately 10-12 hours per week on implementing models and self-study.

On-site Activities

There are two on-site sessions taking place in Hamburg at JAKALA.

What You'll Achieve

By the end of this course, you will:

• Understand the machine learning workflow from data preparation to model deployment

• Build and evaluate supervised learning models for regression and classification tasks

• Implement unsupervised learning techniques for clustering and dimensionality reduction

• Apply machine learning to text data using NLP techniques

• Evaluate and optimize models using appropriate metrics

• Complete a comprehensive machine learning project for your portfolio

• Be prepared to join the Data Circle and work on team-based data science projects

Graduation Requirements

To successfully graduate from the course, you'll need to:

• Attend at least 80% of the sessions (we have a camera-on policy)

• Complete and submit at least 80% of weekly homework assignments

• Submit a final machine learning project

• Attend 2 Career Events, complete 1 eLearning career course

Are you ready to dive into the exciting world of machine learning and artificial intelligence? Let's begin building intelligent systems that can learn from data!