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:
Foundations of ML - Understand ML workflows, feature engineering, and model evaluation
Supervised Learning - Master regression, classification, and regularization techniques
Unsupervised Learning - Learn clustering, dimensionality reduction, and data preprocessing
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
Depending on your location, you'll have different opportunities to participate in on-site activities:
We offer four on-site community events for the Berlin students throughout the semester. You find more information in Slack.
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
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!
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