Natural language processing and large language models (LLM) with Python
.svg)
.svg)
.svg)
Machine Learning Engineer
1. python techniques for text processing
- Python basics for word processing
- Process text and PDF files
- The most important regular expressions
2. introduction to Natural Language Processing (NLP)
- Concepts of Natural Language Processing
- Use of the SpaCy library for text analysis
- Tokenization, stemming and lemmatization
- Part-of-speech and Named Entity Recognition
- Decomposition of texts with Sentence Segmentation
3. text classification and text analysis
- Introduction to scikit-learn
- Evaluation of classification models with precision, recall and F1 score
- Semantic understanding and sentiment analysis
- Vector-based text representations with Word Vectors
- Sentiment analysis with the NLTK library
4 Topic Modeling and Long Short-Term Memory
- Introduction to topic modeling
- Classification with Latent Dirichlet Allocation (LDA)
- Recognize structures with Non-negative Matrix Factorization (NMF)
- Long Short-Term Memory, GRU and Text Generation
- Implementation of an LSTM for text creation with Keras
5. transformer and attention
- The concept of self-awareness
- Multihead attention and meaning in NLP models
- Encoder and decoder for machine translation and language understanding
- Architectural concepts of common transformer models: GPT-2/3/4, BERT
- Creating a Transformer structure with Python and Keras
- Training and evaluation of a Seq2Seq transformer
6. transfer learning and fine-tuning with Hugging Face
- Introduction to Hugging Face and presentation of pre-trained models
- Selection of suitable models and tokenizers
- Transfer learning and fine-tuning of pre-trained models
- Automatic configuration and customization of models
7th practical project: Training your own chatbot
This training will provide you with in-depth knowledge of concepts and methods for using language-based artificial intelligence. You will get to know the basic technologies and acquire comprehensive knowledge of the transformer architecture, which is a key technology for modern generative AI.
You will learn the practical work with the most important Python frameworks and with pre-trained models on Hugging Face and know how to use them in your own projects.
The technical hurdles for getting started are minimal - thanks to the use of Jupyter notebooks and free cloud GPUs.
After completing this training course, you will not only have sound theoretical knowledge of language processing with artificial intelligence, but also practical experience in the application of methods and frameworks. You will be able to develop, adapt and productively use your own language systems and models based on machine learning. You will also learn how to use the technologies in your own projects. This will qualify you for advanced tasks in AI development.
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.
The practical exercises are provided in the form of Jupyter notebooks, which you can install locally on your own computer. The computationally intensive training of the data models is carried out on freely available cloud GPUs.
The trainers are on hand to help you carry out the practical exercises - in the virtual classroom or individually in break-out sessions.
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, machine language processing and generative AI in detail and use them in their own projects.
Basic knowledge of programming with Python is assumed. Advanced technical, mathematical and statistical knowledge is helpful, but not required.
This course is a valuable building block in the qualification as a data scientist, data engineer and machine learning engineer.
.svg)
