Machine learning with Python: The practice-oriented introduction
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Machine Learning Engineer
1. basics of machine learning with Python
- Overview of the technologies and sub-areas of machine learning
- Different types of machine learning and their differences
- Linear regression and logistic regression in detail
- Mathematical basics of linear and logistic regression
2. advanced models and techniques
- Decision trees and their application in classification and regression problems
- Practical implementation of decision trees with Python
- Ensemble models: bagging and boosting
- Practical implementation of ensemble models with Random Forest and XGBoost
3. data preparation with scikit-learn and pandas
- Data preparation and pre-processing
- Exploratory data visualization and statistical analysis
- Data analysis with descriptive and inferential statistics
- Data preparation with scikit-learn
- Effective data manipulation with pandas
4. techniques of data summarization and classification
- Clustering and dimension reduction
- Introduction to the algorithms k-Means, SVD and PCA
- Linear and non-linear support vector machines
5. deep learning and industrial applications
- Basics and differences to traditional machine learning
- Implementation of simple neural networks in Python
- Case study: Image recognition and natural language processing
6. a complete pipeline explained using an example
- Complete end-to-end machine learning pipeline at a glance
- Case study: Practical implementation of a machine learning pipeline
- Understanding training, evaluation and optimization
- Use of the XGBoost ensemble model
Practical exercises for co-programming
Throughout the training , you will solve practical tasks with Python yourself, which will help you to immediately apply and consolidate what you have learned. The tasks are provided in Jupyter notebooks that you can run locally on your computer - so you don't need any complex programming environments.
Basic programming knowledge as a prerequisite
This training uses the Python programming language. It is therefore an advantage if you have basic programming skills, for example for working with variables, lists, arrays and loops, or if you acquire some initial knowledge of these before the training .
You will learn all about the technical and mathematical basics of machine learning.
You will get to know the complete process of machine learning projects - from data preparation to the creation and training of models to evaluation and deployment.
You will get an overview of many important Python libraries and learn how to use them in your own projects.
You will be able to prepare, create, train and evaluate your own machine learning models.
The technical entry hurdles are minimized by the use of Jupyter notebooks, which allow you to start directly with the programming tasks without installing programming environments.
The content of this training supports the obligation to provide evidence of the promotion of AI competence within the meaning of Art. 4 EU AI Regulation.
This training training is conducted in a group of a maximum of 12 participants using the Zoom video conferencing software.
Individual support from the trainers is guaranteed - in the virtual classroom or individually in break-out sessions.
The practical exercises are provided in the form of Jupyter notebooks, which you can easily install locally on your own computer. You do not need any previous technical knowledge. The trainers will assist you in carrying out the practical exercises.
Once you have registered, you will find all the information, downloads and extra services for this training course in your online learning environment.
This training is aimed at anyone who wants to understand machine learning in detail and use it in their own projects.
Basic knowledge of any programming language is required. Advanced technical, mathematical and statistical knowledge is helpful, but not required.
This course is a valuable building block in the qualification as a Machine Learning Engineer, Data Engineer and Data Scientist.
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