-
Certificates: Certificate "PCEP™ - Certified Entry-Level Python Programmer"
Machine Learning" certificate -
Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
Certified Entry-Level Python Programmer (PCEP™) (in englischer Sprache) -
Teaching Times: Full-timeMonday to Friday from 8:30 a.m. to 3:35 p.m. (in weeks with public holidays from 8:30 a.m. to 5:10 p.m.)
-
Language of Instruction: German
-
Duration: 8 Weeks
Programming with Python
Python basics (approx. 1 day)
History, concepts
Usage and areas of application
syntax
Lexis, semantics
PEP-8 conventions
Interpreter vs. compiler
Numeral systems: binary, octal, hexadecimal
Scientific Notation
First steps with Python (approx. 5 days)
Numbers
Strings
Date and time
Standard input and output
Numeric operators
Comparison, logical and bitwise operators
Data type conversion
list, tuple, dict, set
List functions and methods
Branching and loops (if, for, while)
Member operators
String basics: escaping, multiline strings
Prioritizing and binding operators
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Functions (approx. 5 days)
Define your own functions
Variables
Parameters and arguments
Return values
Recursion
Namespaces
Functional programming
Parameter types: positional, keyword, mixed
Default values
Shadowing and global keyword
None and return without value
Troubleshooting (approx. 0.5 days)
Basics of error handling with try and except
Typical error types and exception hierarchy
Error propagation and program interruptions
Structuring the except blocks
Object-oriented programming (approx. 4.5 days)
Python classes
Methods
Immutable objects
Data class
Inheritance
Project work, certification preparation and certification exam "PCEP™ - Certified Entry-Level Python Programmer" in English (approx. 4 days)
Machine Learning
Introduction to Machine Learning and the ML Workflow (approx. 3 days)
Fundamentals, Applications, and Types of Machine Learning
Classification of Modern AI and Machine Learning Methods
Machine Learning Workflow with scikit-learn
Training, validation, and test data
Underfitting, overfitting, and model evaluation
Cross-Validation
Data Preparation and Feature Engineering (approx. 2 days)
Data Quality and Preprocessing
Scaling, Normalization, and Encoding
Feature Engineering and Feature Selection
Handling Missing Values, Outliers, and Imbalanced Datasets
Supervised Learning – Regression and Classification (approx. 5 days)
Regression and Classification Methods
Linear and Regularized Models
Distance-, probability-, and tree-based methods
Ensemble and Boosting Methods
Model Comparison and Hyperparameter Optimization
Evaluation Metrics and Probabilistic Predictions
Feature importance and explainable AI
Unsupervised Learning (approx. 2 days)
Dimension Reduction and Feature Extraction
Clustering Methods and Evaluation of Clustering Results
Anomaly Detection
Time Series Analysis and Forecasting (approx. 1 day)
Fundamentals and Analysis of Time Series Data
Forecasting Methods and Machine Learning for Time Series
Neural Networks and Modern AI (approx. 2 days)
Fundamentals of Neural Networks and Deep Learning
Machine Learning for Text Data and Natural Language Processing
Foundation Models, Large Language Models, and Generative AI
Deployment of ML models (approx. 1 day)
Storage and deployment of trained models
Development of simple ML applications
Interfaces and platforms for machine learning models
Project work (approx. 4 days)
To consolidate the content learned
Presentation of the project results
Changes are possible, the course content is updated regularly.
After the course, you will have a compact, basic knowledge of programming with Python. You will be confident in using the programming language with its classes, libraries and functions.
In addition, you can prepare data for machine learning applications, select appropriate supervised and unsupervised learning methods, and train, evaluate, and optimize models. You can interpret model results and implement basic machine learning applications. You are also familiar with current developments in the fields of deep learning and generative AI.
IT apprentices, IT specialists, programmers and specialists with relevant professional experience.
The versatility of Python makes employees with the relevant skills attractive in numerous industries and companies. People with programming skills in Python are particularly sought after in web development, machine learning and data analysis.
With the "PCEP™ - Certified Entry-Level Python Programmer" certificate, you can provide meaningful proof of your new qualification and improve your career opportunities.
Machine learning is used across various industries to analyze large amounts of data, identify patterns, optimize processes, and make predictions. Such skills are applied in fields such as IT, manufacturing, logistics, finance, and marketing. Proficiency in Python, as well as in the development, evaluation, and application of machine learning models, opens up career opportunities in data- and AI-driven fields.
Didactic concept
Your lecturers are highly qualified both professionally and didactically and will teach you from the first to the last day (no self-study system).
You will learn in effective small groups. The courses usually consist of 6 to 25 participants. The general lessons are supplemented by numerous practical exercises in all course modules. The practice phase is an important part of the course, as it is during this time that you process what you have just learned and gain confidence and routine in its application. The final section of the course involves a project, a case study or a final exam.
Virtual classroom alfaview®
Lessons take place using modern alfaview® video technology - either from the comfort of your own home or at our premises at Bildungszentrum. The entire course can see each other face-to-face via alfaview®, communicate with each other in lip-sync voice quality and work on joint projects. Of course, you can also see and talk to your connected trainers live at any time and you will be taught by your lecturers in real time for the entire duration of the course. The lessons are not e-learning, but real live face-to-face lessons via video technology.
The courses at alfatraining are funded by Agentur für Arbeit and are certified in accordance with the AZAV approval regulation. When submitting a Bildungsgutscheinor Aktivierungs- und Vermittlungsgutschein, the entire course costs are usually covered by your funding body.
Funding is also possible via Europäischen Sozialfonds (ESF), Deutsche Rentenversicherung (DRV) or regional funding programs. As a regular soldier, you have the option of attending further training courses via Berufsförderungsdienst (BFD). Companies can also have their employees qualified via funding from Agentur für Arbeit (Qualifizierungschancengesetz).