Machine Learning: Fundamentals

Course overview

Data preparation and exploratory analysis as a basis for machine learning - Collect, clean, and prepare data from various sources for analysis - Identify anomalies, patterns, and correlations using exploratory analysis techniques SQL and relational databases for efficient data processing in machine learning - Use database queries to analyze and manage large datasets effectively - Apply joins, aggregations, and subqueries to represent complex data structures Understanding the fundamentals of machine learning and supervised learning - Utilize regression techniques to create predictive models based on historical data - Employ classification methods to categorize and analyze data meaningfully Applying statistical methods and probability theory for machine learning - Use descriptive statistics to interpret data distributions and central metrics - Conduct model evaluation and performance measurement to improve prediction accuracy

Upcoming dates

• 2026-10-11T22:00:00.000Z · 2027-01-09T23:00:00.000Z · Berlin · Hybrid Learning
• 2026-10-11T22:00:00.000Z · 2027-01-09T23:00:00.000Z · München · Hybrid Learning
• 2026-10-11T22:00:00.000Z · 2027-01-09T23:00:00.000Z · Berlin · Hybrid Learning
• 2026-10-11T22:00:00.000Z · 2027-01-09T23:00:00.000Z · Frankfurt am Main · Hybrid Learning
• 2026-10-11T22:00:00.000Z · 2027-01-09T23:00:00.000Z · Stuttgart · Hybrid Learning