Become an AI Professional in 6 Months
Build. Ship. Get hired.
Learn from AI leaders who build in production.
€2,800€4,00030% Off
Trusted by Leading Companies
Our students and instructors come from organizations where AI drives real business outcomes
Join professionals who are already applying AI at scale
Course Overview
Start Date
May 9, 2026
Duration
6 months
Language
English
Investment
4,000€ 2,800€ Early Bird
Why Choose TUTAI?
Learn From PhDs & CTOs
Every instructor is a practitioner. PhDs, CTOs, and senior leaders who build AI systems in production today.
Build a Portfolio That Gets You Hired
Publish real work on Medium, GitHub, and YouTube as you learn. Graduate with proof of your skills, not just a certificate.
Projects Tailored to Your Industry
Work on datasets and problems relevant to your career, whether you're in finance, healthcare, retail, or tech.
A Network That Lasts Beyond the Course
Join a community of AI professionals through peer reviews, discussions, and collaborative projects that continue after graduation.
From Foundations to AI Agents
Cover the full spectrum: from advanced analytics and ML to LLMs, Generative AI, and autonomous AI Agents.
Feedback Loops Like Real Teams
Our evaluation mirrors real development cycles: submit, get feedback, iterate, and improve. Just like in production teams.
Meet Your Instructors
Learn from industry leaders and PhD holders with extensive real-world experience
Luís Pinto
Chief Intelligence Officer @ Genesis Digital Solutions, BSc Computer Engineering @ IST
What You'll Be Able to Do
By the end of the course, you will be able to
Think Like a Data Scientist
Understand the principles behind data science and AI, and apply them confidently to real business problems.
Analyse Data With Confidence
Master statistical and data analysis techniques used daily by top analytics teams.
Build & Deploy ML Models
Go from raw data to a deployed machine learning model, covering the full end-to-end workflow.
Work With LLMs in Production
Understand how Large Language Models work under the hood and apply them to automation, analytics, and real products.
Create With Generative AI
Get hands-on experience building with the latest generative AI tools and frameworks shaping the industry right now.
Design Autonomous AI Agents
Architect and deploy AI agents that automate complex workflows. The most in-demand skill in AI today.
Course Structure
A comprehensive 6-month journey through AI and machine learning
1. ADVANCED DATA ANALYTICS TECHNIQUES
Statistical Analysis & Inference
- Deep dive into inferential statistics and confidence intervals
- Design and execute A/B testing scenarios with real/simulated data
- Interpret p-values and statistical significance in business context
Advanced Data Visualization and Reporting
- Master data analysis at scale using BigQuery
- Create interactive dashboards with Looker Studio
- Best practices for visualizing complex relationships
Data-Driven Insights & Recommendations
- Translate raw analysis into actionable business strategies
- Develop and present data-driven recommendations on a real-world case study
2. AI FOUNDATIONS FOR PRACTITIONERS
Machine Learning Basics
- Supervised vs. unsupervised learning
- Common algorithms (e.g., linear and logistic regression, decision trees, random forests, gradient boosting)
- Model evaluation and validation
AI and Data Science Recap
- Data science workflow, from data ingestion to model deployment
- Core AI/ML concepts (training, inference, supervised vs. unsupervised)
- Real-world AI use cases across industries
Deep Learning and Transformer Essentials
- Neural networks vs. traditional machine learning
- Fundamentals of the Transformer architecture and attention mechanisms
- Why Transformers revolutionized NLP and generative tasks
🎓 MASTERCLASS: CAUSAL INFERENCE IN AI
Workshop Overview
- Hands-on causal modeling exercises
- Real-world case studies and applications
3. INTRODUCTION TO LARGE LANGUAGE MODELS (LLMS)
Overview of Generative AI and LLMs
- The AI Landscape: Key players, foundational models vs. vertical integration vs. the application layer, and closed-source vs. open-source models
- Key advancements from earlier models (GPT-2, GPT-3) to GPT-4, and techniques like Chain of Thought (CoT), Test-Time Compute (TTC), and the impact in newer models such as OpenAI's o3
How They Work and Important Concepts
- Transformer architecture basics
- Pre-training and fine-tuning processes
- Retrieval-augmented generation (RAG) systems
- Multimodal learning: combining text, images, and beyond
- Challenges in training large-scale models (e.g., computational resources, data requirements)
Use Cases
- Customer service automation (chatbots, virtual assistants)
- Enhancing meeting productivity (searching, summarization, keyword extraction)
4. INTRODUCTION TO GENERATIVE AI PRODUCTS FOR THE FUTURE OF DATA ANALYTICS
Comprehensive Overview of Generative AI Tools
- Introduction to leading AI tools: ChatGPT, Claude, Gemini, and Perplexity AI for data exploration, analysis, and automation
- AI coding copilots: Cursor for data-driven coding assistance and rapid prototyping
- Introduction to lightweight web development with AI assistance through V0
Data Pipelines in the AI Era
- Utilizing LangChain to build custom AI data solutions and pipelines
- Hands-on examples bridging data analytics with coding and web-based solutions
Productivity Enhancement with AI-Driven Tools
- Research and document automation with NotebookLM for streamlined reporting
- Advanced data analysis using PandasAI to automate data manipulation and generate insights
- Industry case studies showcasing productivity improvements across sectors
5. APIS FOR AI MODELS
API Integration for LLMs
- Accessing and utilizing GPT-4, Claude, Gemini, and other text-generation APIs
- Best practices: prompt engineering, security management, and cost control
- Handling advanced tasks: summarization, sentiment analysis, and text classification
Vision, Multimodel, Search, Function Calling and Structured Outputs
- Large Vision Models (LVMs) for image classification and object detection
- Native image-generation APIs
- Accessing APIs that combine text, images, and structured data
- Search APIs for retrieving relevant information from a vast knowledge base
- Function Calling and Structured Outputs for executing complex tasks and generating structured data
Real-Time and Streaming AI
- Speech-to-text and text-to-speech integration (real-time voice applications)
- Streaming data pipelines for live inference (e.g., sensor data, chatbots)
- Scaling challenges and strategies for high-throughput AI inference
🎓 MASTERCLASS: BUILDING AI PRODUCTS FROM SCRATCH
Workshop Overview
- AI Product strategy
- Product ideation and validation
- Real-world case study
6. BUILDING AND DEPLOYING AI AGENTS
Introduction to AI Agents
- Definitions and evolution of AI agents (reactive, proactive, hybrid)
- Architecture: combining LLMs, rules engines, and other AI components
- Tools and frameworks for agent development (e.g., LangChain, custom frameworks)
Designing Intelligent Agents
- Programming and configuring agent behaviors with Gemini, GPT-4, Claude, or open-source LLMs
- Handling tasks, dialogues, and multi-step interactions
- Best practices: logging, monitoring, and fallback scenarios
Use Cases and Deployment
- Industry verticals adopting AI agents (customer support, finance, healthcare)
- Challenges and limitations in real-world settings (compliance, bias, interpretability)
- Case studies of successful AI agent implementation, from chatbots to autonomous process automation
Our Unique Evaluation Model
We believe in learning by doing, sharing, and engaging with the community. Our evaluation framework ensures you graduate with both knowledge and a professional portfolio.
Valuable, Community-Focused Outputs
We emphasize creating work that holds real value in the AI community.
- •All content is published on Medium, X, YouTube, and GitHub
- •Active engagement with AI researchers and practitioners worldwide
- •Focus on practical, industry-relevant deliverables
Building a Public Portfolio
From day one, your submissions are designed to be publicly showcased.
- •Articles, code, and demos are publicly accessible
- •Graduate with a visible, credible portfolio
- •Demonstrate your skills to peers and employers
Peer Review & Community Engagement
Learn through active participation in the global AI conversation.
- •Critique and improve others' work
- •Receive valuable peer feedback
- •Community engagement is part of your grade
- •Participate in global AI discussions
High Standards & Iteration
Refine your work through professional feedback cycles.
- •Strong quality standards for publication
- •Iterative feedback and improvement process
- •Mirrors real-world research and development
- •Professional-grade output requirements
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AI Ecosystem
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