Practical course Machine Learning for Python programmers
You are a Python programmer and want to learn the most important topics of the new Machine Learning field and find out if you can integrate them into your current projects, software architectures and designs? Then this is the course for you. It covers all the tools, techniques and know-how you need to successfully implement simple Machine Learning projects. In addition, the course also introduces advanced Machine Learning topics. Learn more about this exciting practical training course on Machine Learning with Python...
- Fast and hands-on introduction to Machine Learning with Python
- Decide if Machine Learning is suited for your projects
- Includes introduction to Python libraries for ML: Panda, NumbPy, Scikit, etc.
- Safe introduction to a broad spectrum of artificial intelligence
- Foundations for further AI topics like Machine Learning and Deep Learning
Learn Machine Learning from our experts and you will be amazed what is possible!
The course covers a selection of the following topics:
- Introduction to Machine Learning
- Data Analyse
- Python Libraries for ML
- ML Algorithms
- ML Pipelines
- Traditional Machine Learning
- Linear regression
- Logicistic regression
- Decision trees
- Deep Learning (PyTorch)
- Moving ML Project into Production
For more details, please see the agenda below.
This Machine Learning course is suitable for people with very good Python knowledge, e.g. to the extent of our Python for Programmers course.
If you want to learn this course with the programming language R, then check out the Machine Learning course with R. If you are a team leader, manager, executive or non-programmer, then check out our Artificial Intelligence for Decision Makers course.
Individual: we specifically address your needs and take into account your previous knowledge, desired topics and focal points
Structured and easy to understand
Take your career, studies or training to the next level: with certificate
Safely and independently develop programmes (whether private, professional or for your start-up)
Lots and lots of practice: immediately applicable results
Small groups: max. 8 participants in the 3-day course max. 12 participants in online coaching
Developed by experts according to the Raed Method® & geared to the requirements of tech companies in 2020
E-mail support even after the end of the course
This course provides a hands-on introduction to modern machine learning for students with Python skills but no prior machine learning experience. It covers all the tools, techniques, and processes necessary to successfully run simple machine learning projects and provides a solid foundation for moving into more advanced topics.
While the course provides an overview of different approaches, we will focus on two techniques for solving ML problems that have not only proven themselves by winning numerous competitions on websites such as Kaggle, but have also been successfully used in countless industry projects: ensembles of decision trees and deep lear
Before any ML technique can be applied to a problem, it is necessary to analyze, clean, visualize, and transform the existing data. The first part of the course will deal with these less glamorous but absolutely important aspects of ML projects. In the second half, we will use decision trees and pre-trained deep networks to develop solutions with state-of-the-art performance for various ML problems.
What do you learn on the first day?
We begin with an ML introduction. In this short section we cover questions such as: What is ML? Which problems can be solved with ML techniques, which are less suitable? What does an ML development project look like? What best practices should you apply?
In many cases, the success of practical machine learning projects depends more on the quality of the data used to drive the algorithms than on the details of the ML algorithm. In this section, we cover the most important tools for collecting, cleaning, analyzing, and preparing data in Python.
What do you learn on the second day?
On the second day, you will learn Traditional Machine Learning. While Deep Learning is a major focus in academic research and provides superior results for most perceptual problems, traditional ML techniques are still viable solutions to many problems and are often used as baselines against which more elaborate solutions are evaluated.
What do you learn on the third day?In this part of the course, we will look at Deep Learning and in particular how pre-trained DL models can provide us with state-of-the-art solutions, even in cases where we have limited data or computational budget.
There are many opportunities for error when ML models are transitioned from training to production. While the topic is too broad to cover in detail in this course, we will at least provide an overview of some of the key concerns when transitioning smaller projects to production.
The Machine Learning course for Python programmers is taught by one of the following trainers:
Dr. Stefan Behnel
Expert: Python, Pytest, Unit Test und TDD, Clean Code, Clean Software Architektur, Fast Python, Cython
- Doctorate at the TU Darmstadt as Dr. Ing. in Software Architecture
References: 15 years of experience as a consultant, software developer and software architect in the financial services, automotive, publishing and tourism industries in the field of high-performance Python and open source, main developer of Cython, the data science library PANDA is based on Cython. Python training for Draeger, Apple, Sky Deutschland, IT companies, ...
Expert: Java, Python, Clean Code, Clean SW-Architecture, Refactoring, Testing, Train The Trainer
- Doctorate at LMU in the field of programming languages (2022).
References: 17 years of teaching and industry experience, thereof 12 years lecturer at the Ludwig-Maximilians-University Munich for Java, Python, Efficient Algorithms, Multiple awardsfor outstanding teaching at the LMU, book author for Java & soon Python at Springer and Orelly Verlag, developer of the RAED-Teaching Method®, Train the Trainer instructor, team training in Java and Python for BMW, VW, BA, SIEMENS, AGFA-Healthcare, TÜV Süd, Schufa AG, ..
Prof. Dr. Peer Kröger
Expert: Artificial Intelligence, Data Science, Big Data, SQL/NoSQL Database, Python, Java
- Doctorate at LMU in the field of Database and Data Science
References: Many years of practical experience in the implementation of data science projects as well as in consulting and training in the automotive industry, financial service providers and SMEs, among others. Approx. 150 peer-reviewed publications (cited over 8000 times) on the topic of data science, data mining, machine learning and AI. Member of the AI competence centre Munich Center for Machine Learning (MCML) at LMU Munich and professor for information systems and data mining at CAU Kiel.
Dr. Matthias Hölzl
- Doctorate at LMU in the field of Software Engineering
References: 30 years of teaching and industrial experience. Of which 18 years at Ludwig-Maximilians-Universität Munich, most recently as Professor for Software and Computational Systems Engineering. Training, technical coaching for machine learning, deep learning, process automation as well as review and improvement of software architecture in large IT projects. Python and Java trainings for Deutsche Bank, BMW, BA, VKB, etc. Editor and author of several books at Springer-Verlag and author of numerous scientific publications.
Of course, as a participant in the Machine Learning course for Python programmers you will receive a certificate. Prerequisite for this is the complete participation in all course units and programming tasks and the successful programming of a small final project. However, after this intensive machine learning training you will have more fun than stress.
The agenda is written in English due to the numerous technical terms. Descriptions as well as course material will be provided in German. You can book the course either in German or English.
All seminar contents are individually adapted to the wishes of our participants. They can vary depending on the level of knowledge and will be defined together with the seminar leader on day 1. In this Machine Learning with Python seminar you have the choice of the following topics:
Machine Learning for Python Programmers Day 1
Introduction to Machine Learning
In this short section we cover questions such as
- What is ML?
- Which Problems can be solved with ML techniques?
- Which ones are less suitable?
- How does a ML development project look?
- Which best practices should you employ?
Data Analysis, Visualization and Preparation
In many cases, the success of practical machine-learning projects depends more on the quality of the data used to drive the algorithms than on the details of the ML algorithm. In this section we cover the most important tools to collect, clean, analyze, and prepare data in Python.
- Numpy: Numerical computation in Python
- Pandas: Data cleaning, analysis, and transformation
- Matplotlib, Seaborn and Plotly: Visualizing data
- Scikit-learn part 1: Preparing data for ML algorithms
- Setting up ML pipelines that simplify good development practices
Machine Learning for Python Programmers Day 2
Traditional Machine Learning (Scikit-Learn part 2)
While deep learning is a large focus of academic research and provides superior results for most perception-oriented problems, traditional ML techniques are still viable solutions for many problems, and they are commonly used as baselines against which more elaborate solutions are evaluated.
- Traditional ML techniques
- Linear regression
- Logistic regression
- Parameter-free techniques
- Decision trees
- random forests
- boosted trees
Machine Learning for Python Programmers Day 3
Deep Learning (PyTorch)
In this part of the course we will look at deep learning and in particular how pretrained DL models can provide us with state-of-the-art quality solutions even in cases where we have limited data or computational budget.
- What is deep Learning?
- Advantages and disadvantages
- Basics of deep learning
- Fully-connected networks
- multi-layer perceptrons
- Deep learning for image classification
- CNNs, pretrained models
- Introduction to natural language understanding
- using pertained transformers
Moving ML Project into Production
There are many possibilities for errors when moving ML models from training to production. While the topic is too large to treat in detail in this course, we give at least an overview of some of the most important concerns when moving small-scale projects to production.
- Saving and loading models
- Ensuring consistency between training and production
- Monitoring model performance and updating production models