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Language models such as ChatGPT, Gemini, Claude or Qwen have proven to be very powerful in many areas such as process automation. However, the quality of the results is largely dependent on the type of connection and the adaptation of the model.
This AI training course is dedicated to the questions of how AI models can be integrated into processes and connected to company-specific interfaces. And how an existing language model can be adapted and improved for your own processes.
In this AI training course, you will use numerous examples and hands-on exercises to learn how to use AI processes via an API or how to transform a language model into an agent with new capabilities using Tool Usage. You will also understand how to specialize a language model to the specific task areas in your company through fine-tuning.
-
Course Contents
-
- Hands-on experience with AI and ML models
- Insight into AI training
- Model fine-tuning
- Creation of AI agents
- Connection of a language model via API
- Basics of local AI models
- Overview of the capabilities of different AIs
The detailed digital documentation package, consisting of an e-book and PDF, is included in the price of the course.
Premium Course Documents
In addition to the digital documentation package, the exclusive Premium Print Package is also available to you.
- High-quality color prints of the ExperTeach documentation
- Exclusive folder in an elegant design
- Document pouch in backpack shape
- Elegant LAMY ballpoint pen
- Practical notepad
The Premium Print Package can be added during the ordering process for € 150,- plus VAT (only for classroom participation).
-
Target Group
-
This course is aimed at programmers and software developers, i.e. "implementers" who want to use AI in their projects. As well as anyone who wants to understand and use Agentic AI.
-
Knowledge Prerequisites
-
The knowledge imparted in the courses Using AI tools and LLMs successfully – ChatGPT, Gemini, Claude & Co. and Machine Learning – Data Science and Artificial Intelligence creates a good basis for attending the course.
Programming skills in Python are necessary to be able to follow the exercises and examples. These can be acquired in our Python courses such as Python for Newcomers – Introduction to Programming or Python Basics for Programmers – Fast Entry .
-
Course Objective
-
In this AI course, you will learn how to properly integrate AI into your processes via API. You will gain an understanding of the process of fine-tuning by specializing a language model for different tasks. You will learn the basics of Agentic AI and will then be able to create an AI agent with your own tools.
-
Complementary and Continuative Courses
-
The AI Programming for Experts – Fine-Tuning, RAG Pipelines & Multi-Agent Systems course offers an advanced, in-depth look at the possibilities of fine-tuning and document integration with Retrieval Augmented Generation (RAG), as well as the ability to create multi-agent systems.
| 1 | Introduction to Artificial Intelligence |
| 1.1 | Intelligence |
| 1.1.1 | Definition |
| 1.1.2 | Features of Artificial Intelligence |
| 1.1.3 | Machine Learning |
| 1.1.4 | Explainability of Artificial Intelligence |
| 1.2 | AI—Boon or Bane? |
| 2 | Machine Learning |
| 2.1 | Generative AIs in Other Areas |
| 2.1.1 | Text to Image |
| 2.1.2 | Text to Video |
| 2.2 | Neural Networks |
| 2.2.1 | Biological vs. Artificial Neuron |
| 2.2.2 | Multilayer Neural Networks |
| 2.2.3 | Training and Back-propagation |
| 2.2.4 | Development Steps towards ChatGPT |
| 2.2.5 | Recurrent Neural Networks (RNN) |
| 2.2.6 | LSTM & GRU |
| 2.2.7 | Transformer: Attention Mechanism |
| 2.2.8 | Training and Fine-tuning |
| 2.2.9 | Parameters and Hyperparameters |
| 2.3 | Data Processing for Machine Learning |
| 2.3.1 | Data Science |
| 2.3.2 | Python and Machine Learning |
| 2.3.3 | NumPy |
| 2.3.4 | Pandas |
| 2.3.5 | Matplotlib and Seaborn |
| 2.4 | Example: MNIST |
| 2.4.1 | Features & Feature Matrix |
| 3 | Natural Language Processing |
| 3.1 | Overview |
| 3.2 | Language Models |
| 3.2.1 | Tokenization |
| 3.2.2 | Normalization & Pre-tokenization |
| 3.2.3 | Subword Encoding |
| 3.2.4 | Vectorization and Embeddings |
| 3.2.5 | Detailed View |
| 3.3 | Transformer |
| 3.4 | Training by GPT |
| 3.4.1 | Level 1: Pre-training |
| 3.4.2 | Level 2: Supervised Fine-tuning |
| 3.4.3 | Level 3 & 4 Reinforcement Learning |
| 4 | ChatGPT and OpenAI |
| 4.1 | ChatGPT API in an Overview |
| 4.2 | OpenAIs Playground |
| 4.3 | API Usage with Python |
| 4.3.1 | Chat via API |
| 4.3.2 | Reproducibility |
| 4.3.3 | Moderation |
| 4.3.4 | Creation of Assistants via the API |
| 4.3.5 | Use of Assistants |
| 4.3.6 | Output of Structured Data |
| 5 | AI On-premise Solutions |
| 5.1 | Platforms for AI |
| 5.1.1 | Hugging Face |
| 5.1.2 | Kaggle |
| 5.1.3 | Models & Data Sets |
| 5.2 | Hardware Requirements |
| 5.2.1 | Processor (CPU) |
| 5.2.2 | Graphics Board (GPUs) |
| 5.2.3 | Tensor Processors (TPUs) |
| 5.2.4 | Random Access Memory (RAM) |
| 5.2.5 | Main Memory |
| 5.3 | Important File Formats for Self-hosted AI Models |
| 5.4 | Security Mechanisms and Guards |
| 5.4.1 | Attacks |
| 5.5 | Retrieval-Augmented Generation (RAG) |
| 5.6 | Text Generation WebUI |
| 5.6.1 | Model Loader |
| 5.6.2 | Model Tweak Parameters |
| 5.7 | Monitoring |
| 5.8 | Quantization |
| 5.8.1 | Advantages and Disadvantages |
| A | List of Abbreviations |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.-
Language models such as ChatGPT, Gemini, Claude or Qwen have proven to be very powerful in many areas such as process automation. However, the quality of the results is largely dependent on the type of connection and the adaptation of the model.
This AI training course is dedicated to the questions of how AI models can be integrated into processes and connected to company-specific interfaces. And how an existing language model can be adapted and improved for your own processes.
In this AI training course, you will use numerous examples and hands-on exercises to learn how to use AI processes via an API or how to transform a language model into an agent with new capabilities using Tool Usage. You will also understand how to specialize a language model to the specific task areas in your company through fine-tuning.
-
Course Contents
-
- Hands-on experience with AI and ML models
- Insight into AI training
- Model fine-tuning
- Creation of AI agents
- Connection of a language model via API
- Basics of local AI models
- Overview of the capabilities of different AIs
The detailed digital documentation package, consisting of an e-book and PDF, is included in the price of the course.
Premium Course Documents
In addition to the digital documentation package, the exclusive Premium Print Package is also available to you.
- High-quality color prints of the ExperTeach documentation
- Exclusive folder in an elegant design
- Document pouch in backpack shape
- Elegant LAMY ballpoint pen
- Practical notepad
The Premium Print Package can be added during the ordering process for € 150,- plus VAT (only for classroom participation).
-
Target Group
-
This course is aimed at programmers and software developers, i.e. "implementers" who want to use AI in their projects. As well as anyone who wants to understand and use Agentic AI.
-
Knowledge Prerequisites
-
The knowledge imparted in the courses Using AI tools and LLMs successfully – ChatGPT, Gemini, Claude & Co. and Machine Learning – Data Science and Artificial Intelligence creates a good basis for attending the course.
Programming skills in Python are necessary to be able to follow the exercises and examples. These can be acquired in our Python courses such as Python for Newcomers – Introduction to Programming or Python Basics for Programmers – Fast Entry .
-
Course Objective
-
In this AI course, you will learn how to properly integrate AI into your processes via API. You will gain an understanding of the process of fine-tuning by specializing a language model for different tasks. You will learn the basics of Agentic AI and will then be able to create an AI agent with your own tools.
-
Complementary and Continuative Courses
-
The AI Programming for Experts – Fine-Tuning, RAG Pipelines & Multi-Agent Systems course offers an advanced, in-depth look at the possibilities of fine-tuning and document integration with Retrieval Augmented Generation (RAG), as well as the ability to create multi-agent systems.
| 1 | Introduction to Artificial Intelligence |
| 1.1 | Intelligence |
| 1.1.1 | Definition |
| 1.1.2 | Features of Artificial Intelligence |
| 1.1.3 | Machine Learning |
| 1.1.4 | Explainability of Artificial Intelligence |
| 1.2 | AI—Boon or Bane? |
| 2 | Machine Learning |
| 2.1 | Generative AIs in Other Areas |
| 2.1.1 | Text to Image |
| 2.1.2 | Text to Video |
| 2.2 | Neural Networks |
| 2.2.1 | Biological vs. Artificial Neuron |
| 2.2.2 | Multilayer Neural Networks |
| 2.2.3 | Training and Back-propagation |
| 2.2.4 | Development Steps towards ChatGPT |
| 2.2.5 | Recurrent Neural Networks (RNN) |
| 2.2.6 | LSTM & GRU |
| 2.2.7 | Transformer: Attention Mechanism |
| 2.2.8 | Training and Fine-tuning |
| 2.2.9 | Parameters and Hyperparameters |
| 2.3 | Data Processing for Machine Learning |
| 2.3.1 | Data Science |
| 2.3.2 | Python and Machine Learning |
| 2.3.3 | NumPy |
| 2.3.4 | Pandas |
| 2.3.5 | Matplotlib and Seaborn |
| 2.4 | Example: MNIST |
| 2.4.1 | Features & Feature Matrix |
| 3 | Natural Language Processing |
| 3.1 | Overview |
| 3.2 | Language Models |
| 3.2.1 | Tokenization |
| 3.2.2 | Normalization & Pre-tokenization |
| 3.2.3 | Subword Encoding |
| 3.2.4 | Vectorization and Embeddings |
| 3.2.5 | Detailed View |
| 3.3 | Transformer |
| 3.4 | Training by GPT |
| 3.4.1 | Level 1: Pre-training |
| 3.4.2 | Level 2: Supervised Fine-tuning |
| 3.4.3 | Level 3 & 4 Reinforcement Learning |
| 4 | ChatGPT and OpenAI |
| 4.1 | ChatGPT API in an Overview |
| 4.2 | OpenAIs Playground |
| 4.3 | API Usage with Python |
| 4.3.1 | Chat via API |
| 4.3.2 | Reproducibility |
| 4.3.3 | Moderation |
| 4.3.4 | Creation of Assistants via the API |
| 4.3.5 | Use of Assistants |
| 4.3.6 | Output of Structured Data |
| 5 | AI On-premise Solutions |
| 5.1 | Platforms for AI |
| 5.1.1 | Hugging Face |
| 5.1.2 | Kaggle |
| 5.1.3 | Models & Data Sets |
| 5.2 | Hardware Requirements |
| 5.2.1 | Processor (CPU) |
| 5.2.2 | Graphics Board (GPUs) |
| 5.2.3 | Tensor Processors (TPUs) |
| 5.2.4 | Random Access Memory (RAM) |
| 5.2.5 | Main Memory |
| 5.3 | Important File Formats for Self-hosted AI Models |
| 5.4 | Security Mechanisms and Guards |
| 5.4.1 | Attacks |
| 5.5 | Retrieval-Augmented Generation (RAG) |
| 5.6 | Text Generation WebUI |
| 5.6.1 | Model Loader |
| 5.6.2 | Model Tweak Parameters |
| 5.7 | Monitoring |
| 5.8 | Quantization |
| 5.8.1 | Advantages and Disadvantages |
| A | List of Abbreviations |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.