Unlocking the Power of AI: ML Fundamentals Bootcamp

November 25 – 26, 2024 | Berlin

CONFERENCE STARTS IN:

Conquer big data challenges, and embrace MLOps strategies

Build and deploy Decision Trees, KNN, and Neural Networks.

Empower yourself with ML knowledge to drive automation and innovation

Bootcamp Program Day 1

Introduction to Machine Learning
Types of machine learning (supervised, unsupervised, reinforcement), Real-world applications of machine learning, The machine learning pipeline (data collection, preprocessing, feature engineering, model training, evaluation, deployment)
Hands-on: Classical Machine Learning Algorithm
Introduction to specific classical algorithms (e.g., KNN, Decision Trees), Overview of classical algorithms and their use-cases, Data preparation and exploration Advantages & Disadvantages of such methods
Lab session
Model training and evaluation, Interpretation of results, Algorithm optimization techniques, Practical considerations and challenges

Bootcamp Program Day 2

Recap of Day 1
Brief review of key concepts from the previous day, Q&A session
Introduction to Neural Networks
Neural network architecture (neurons, layers, activation functions), Training process (backpropagation, gradient descent), Overfitting and underfitting, Hands-on: Building a simple neural network, Using a popular deep learning framework (e.g., TensorFlow, PyTorch), Training the model on the same dataset as the previous day Evaluating the model's performance
Deep Dive into Neural Networks
Hyperparameter tuning, Regularization techniques, Model optimization, Hands-on: Experimenting with different neural network architectures
Introduction to FastAPI
Building a basic API with FastAPI, Integrating the trained model into the API, Deploying the API (locally or to a platform like Heroku), Testing the API, Close out the session and answer any last questions
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6 Reasons to attend the Bootcamp

1
Future-Proof Your Skills: Stay ahead in the fast-evolving tech landscape by understanding AI's role in software development
2
Hands-On Learning: Dive deep into Machine Learning essentials with practical examples tailored for software professionals
3
Tackle Real-World Challenges: Learn how to manage data quality and scale AI solutions in your projects
4
Discover AI Opportunities: Identify how AI can drive innovation and competitive advantage in your company
5
Expert-Led Insights: Gain valuable knowledge from industry experts and prepare for the AI-driven future.
6
Get the chance to discuss your individual applications for ML with experts

Für wen ist das Camp geeignet?

Trainer

Christian Fässler

Christian Fässler, the founder of adnexo GmbH in Zürich, is an entrepreneur in the field of engineering and lecturer for IoT and Machine Learning at the Ostschweizer Fachhochschule OST. His company specializes in providing IoT development services. With a diverse professional background, he has served numerous global clients across various industries, including cybersecurity, advertising, and automotive. His keen interest lies in exploring the potential of IoT applications and utilizing machine learning techniques to extract valuable insights from sensor data. Guided by his work philosophy: “The secret of getting ahead is getting started.”

Martin Stypinski

Martin Stypinski is the founder of Veemg GmbH in Zürich, a software engineering and consulting company, and the vice-dean of studies for the post-diploma degree in “Machine Learning for Software Engineers” at the Ostschweizer Fachhochschule in Rapperswil. Over the past fifteen years, he worked in multiple engineering positions in the software industry and applied research. He is passionate about Machine Learning and Software challenges “beyond CRUD”.

Elevate your Machine Learning journey by adding the MLcon to your schedule. While the ML Fundamental Bootcamp provides a solid foundation and hands-on experience, the conference offers a unique opportunity to expand your knowledge even further. Explore the latest advancements in ML, learn from industry leaders, and see how cutting-edge tools and strategies complement the skills you’ve developed in the bootcamp. By attending, you’ll gain a comprehensive understanding of ML’s impact across various domains, enabling you to apply these insights to real-world projects and stay ahead in the rapidly evolving tech landscape.

Take your machine learning skills to the next level with our intensive 2-day ML Fundamentals Bootcamp. Designed for software professionals, this workshop dives deep into supervised and unsupervised learning, big data complexities, and essential MLOps strategies. Unlike standard conference sessions, our hands-on approach empowers you to build and deploy your own machine learning models using industry-leading tools like TensorFlow and FastAPI. Enhance your expertise, stay competitive, and harness the power of AI for innovation in just two days.

Dates & Prices

The Bootcamp will be held in English.

Was sie mitbringen sollten?

Grundkenntnisse in Python und Jupyter Notebooks, die am optionalen ersten Tag erlernt werden können.

Für wen ist das Camp geeignet?

Das Camp ist ideal für Softwareentwickler:innen und -achitek:innen, die sich für die Erstellung und Integration von ML- Lösungen und GenAI-Services interessieren und offen für neue Technologien und Best Practices im GenAI-Design und -Entwicklung sind.
Tag 1: Einführung in Python für Machine Learning

Für Teilnehmer:innen gedacht, die ihre Grundlagen in Python für ML-Projekte stärken möchten.

  • Python 101: Wichtige Konzepte anhand von Beispielen.
  • Top 10 ML Python Frameworks & Bibliotheken: Theorie & Praxis.
  • Jupyter Notebook: Praktische Erfahrung mit der interaktiven IDE.
  • Hello ML World: Implementierung von ML-Services.

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Tag 2: Einführung in ML & GenAI

Konzentriert sich auf die Grundlagen und Anwendungen von ML und GenAI.

  • ML-Landschaft: Interaktive Einführung und Modellanwendung für verschiedene Usecases.
  • GenAI-Projekte: Architekturelle Bausteine und einfache Anwendungsfälle.
  • Prompt-Engineering: Bedeutung und Implementierung.
  • Modellintegration: Einbindung von proprietären und Open Source LLMs.
  • Semantische Validierung: Guardrails für User-Input und Modell-Output.
  • Enterprise-Integration: GenAI-Lösungen in Unternehmenssoftware einbinden.
  • Unterschiede in GenAI-Projekten: Herausforderungen und Lösungen im Betrieb.

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Tag 3: GenAI im Eigenbau

Geht um die praktische Implementierung und Optimierung von GenAI-Systemen.

  • RAG-Systeme: Einführung und einfache Implementierung.
  • Chunking: Einfluss auf den ermittelten Kontext.
  • Vektordatenbanken: Nutzung zur Kontextfindung.
  • Systemoptimierungen: Anpassung an individuelle Anforderungen.
  • Evaluation und Qualitätssicherung: Möglichkeiten für den produktiven Betrieb.

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