Master AI & Machine Learning in 6 Months
Build production-ready AI systems with hands-on projects led by industry experts.
Save €1,200 with early bird pricing
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We work with top organizations, applying cutting-edge AI skills in real-world scenarios
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Course Overview
Start Date
March 11, 2026
Duration
6 months
Language
English
Investment
4,000€ 2,800€ Early Bird
Why Choose TUTAI?
World-Class Instructors
Learn from PhD holders and industry leaders with extensive experience.
Portfolio-Focused Learning
Build a professional portfolio by publishing your work on Medium, GitHub, YouTube, and other platforms while you learn.
Personalized Learning Experience
Work on projects tailored to your industry and interests, with datasets and problems relevant to your career goals.
Real Community Engagement
Engage with the global AI community through peer reviews, discussions, and collaborative projects.
Comprehensive Curriculum
Master everything from advanced analytics to cutting-edge AI, including LLMs, Generative AI, and AI Agents.
Industry-Standard Evaluation
Benefit from our unique evaluation system that mirrors real-world development cycles with iterative feedback.
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
Learning Outcomes
Build expertise across the entire AI spectrum with hands-on experience
Strong Foundational Knowledge
Gain a robust understanding of data science, AI principles, and their role in business decision-making.
Advanced Analytical Skills
Develop expertise in statistical and data analysis techniques.
Data Science and AI
Understand the end-to-end workflow for building and deploying machine learning models.
AI and LLM Applications
Gain in-depth knowledge of how LLMs work and explore their practical applications in automation and data analytics.
Generative AI Tools Insight
Acquire hands-on experience with cutting-edge generative AI tools to drive innovation in the future of data analytics.
Agentic AI
Learn to design and deploy AI agents to automate complex tasks and enhance productivity through advanced tool integration.
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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