Machine Learning Engineer

Course overview

The course covers the entire lifecycle required to bring a machine learning product into production. It is divided into four sections: Software Engineering for Data Scientists, Data Engineering (for the pre-production phase), Machine Learning and Monitoring, and Notification (for the post-production phase). Each week ends with a small project. The course covers the entire lifecycle required to bring a machine learning product into production. It is divided into four sections: Software Development for Data Scientists, Data Processing (for the pre-production phase), Machine Learning and Monitoring, and Notification (for the post-production phase). Each week ends with a small project. Data Science for Machine Learning Master the fundamentals of data science, focusing on data analysis and data cleaning to prepare datasets for machine learning workflows. Learn programming in Python for Data Science and utilize libraries such as NumPy, Pandas, and Matplotlib for efficient data processing. Conduct exploratory data analysis (EDA) to uncover trends, patterns, and anomalies in datasets. Create meaningful data visualizations using tools like Matplotlib and Seaborn to effectively communicate insights. Work on practical projects to clean, preprocess, and analyze real datasets that form the basis for advanced machine learning models. Data Processing and AI Integration Understand the role of data processing in managing data pipelines for scalable machine learning workflows. Use big data tools like Apache Spark and Kafka to process and manage large datasets. Design ETL workflows (Extract, Transform, Load) to clean and convert data into usable formats. Integrate cloud development solutions to provide and manage data pipelines for AI models. Realize practical projects to build data pipelines that seamlessly integrate into machine learning applications. Python and AI for Data Science Acquire expertise in Python programming to implement machine learning models using libraries such as scikit-learn, TensorFlow, and PyTorch. Gain hands-on experience in AI model training, evaluation, and feature development to optimize model performance. Explore algorithms for supervised and unsupervised learning, including classification, regression, clustering, and dimensionality reduction. Dive into neural networks and deep learning architectures for advanced problem-solving. Solve real-world challenges in image recognition, predictive analytics, and text classification using Python and AI tools. Model Monitoring for Machine Learning Learn the fundamentals of monitoring and maintaining machine learning models in production environments. Track critical performance metrics such as accuracy, precision, and recall to assess reliability. Identify and manage data and concept drift to maintain model accuracy over time. Implement automated alerts and monitoring systems using tools like Prometheus and Grafana. Complete projects that involve setting up monitoring frameworks and retraining wor…

Upcoming dates

• 2026-10-18T22:00:00.000Z · 2027-01-15T23:00:00.000Z · Berlin · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-15T23:00:00.000Z · Düsseldorf · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-15T23:00:00.000Z · Dortmund · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-15T23:00:00.000Z · Hamburg · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-15T23:00:00.000Z · Bremen · Combined Learning