Predictive analytics: methods - procedures - applications
Statistics meets machine learning
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Machine Learning Engineer
Methodological basics of predictive analytics, machine learning and AI
- Segmentation, regression and classification.
- Development and optimization of machine learning models.
- Training, testing and evaluation.
- Avoiding common sources of error in machine learning.
- In-depth study of AI methods: neural networks, deep learning and reinforcement learning.
- Practice-oriented identification of fields of application (controlling, marketing, sales, production, etc.).
Data procurement and preparation as the basis for success
- Data integration and cleansing of heterogeneous raw data.
- Preparation and feature engineering for effective AI applications.
- Explorative data analysis and visualization to gain knowledge.
- Cross validation for model validation and quality improvement.
Practical projects and case studies
- Introduction to common machine learning and predictive analytics methods based on practical case studies.
- Specific application examples for time series analysis and forecasting methods.
- Hands-on exercises with machine learning tools such as Knime.
- Creation of own AI models to address specific business issues.
- Practical exercise on customer segmentation and forecasting models.
Current AI trends and development tendencies
- Outlook on new trends and future development prospects in the field of AI, data science and machine learning.
In your online learning environment, you will find useful information, downloads and extra services for this training course once you have registered.
During training , you will benefit from comprehensive expert knowledge and learn practical ...
- how to successfully use predictive analytics and AI in your business environment
- how you can improve your planning, management and decision-making processes with valid forecasts,
- which prerequisites and skills are required for the successful use of machine learning and predictive analytics,
- which specific methods and procedures are particularly effective in your working environment,
- how to evaluate AI-based analysis results in a targeted manner and use them profitably,
- how to gain practical experience with Knime and develop your own AI models.
You can optionally take an e-exam and receive a certificate in addition to confirmation of participation based on the exam result.
Practice-oriented lecture, discussions, concrete case studies, intensive exercises on the PC with Knime and optional e-exam.
After successfully completing the training , you can take an optional e-exam to obtain an additional certificate in addition to your confirmation of participation. The e-exam is an online-based exam on your PC and lasts 60 minutes. You can take the exam in your own familiar environment at a time of your choosing. The exam is based on single or multiple choice questions. Once you have completed the exam, you will immediately be shown whether you have passed or failed. Once you have successfully passed the e-exam, you will receive a certificate based on your exam result.
Specialists and managers, controllers and those responsible for planning, reporting and budgeting who want to expand their methodological skills in predictive analytics, machine learning and artificial intelligence in a practical way. Experience with Knime and Excel is helpful, but not a prerequisite.
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