Machine learning with Python
Create, train and evaluate your own models - with all important Python libraries
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Machine Learning Engineer
1. Data preparation with scikit-learn and pandas
- Data preparation and pre-processing
- Exploratory data visualization and statistical analysis
- Exploratory data analysis using descriptive statistics and visualization methods
- Data preparation with scikit-learn
- Effective data manipulation with pandas
2. Fundamentals of machine learning with Python
- Overview of the different types of machine learning and their differences
- Technologies and subfields of machine learning
- Linear regression and logistic regression in detail
- Mathematical basics of linear and logistic regression
3. 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 the ensemble model Random Forest
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. A complete pipeline explained using an example
- Practical implementation of a machine learning pipeline
- Understanding training, evaluation and optimization
- Use of the XGBoost ensemble model
6. Deep Learning and Industrial Applications
- Basics and differences to traditional machine learning
- Implementation of simple neural networks in Python
- Application of well-known deep learning models from the fields of speech and image processing
Practical exercises for co-programming
Throughout the whole training Throughout the course, you'll solve practical Python tasks that will help you immediately apply and solidify what you've learned. The tasks are provided in Jupyter Notebooks, which you can complete online or locally on your computer.
Basic programming knowledge as a prerequisite
In this training The programming language Python will be used, and basic knowledge is required. You should already be familiar with variables, lists, dictionaries, and loops, or acquire this knowledge before the seminar begins. (For a similar training course including an introduction to Python, see DSidP.)
You will learn all about the technical and mathematical basics of machine learning.
You will learn the complete process of machine learning projects – from data preparation to model creation and training, and finally to evaluation.
You will get an overview of many important Python libraries and learn how to use them in your own projects.
You will implement, train, and evaluate your own machine learning models . The technical barriers to entry are minimized through the use of Jupyter Notebooks, which allow you to interact with the data and programming concepts.
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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