Microsoft Artificial Intelligence Engineer – Bootcamp – A unique training program!

Microsoft Artificial Intelligence Engineer – Bootcamp – A unique training program!

Ai Learning & Certification Path – Newly updated 2025 version!

• 6-month bootcamp program (e-learning + live online courses every weekend!)
• Ask us about the next cohort schedule!
• Become a master of AI: from Foundation to advanced agentic applications with Microsoft.
• Apply deep learning, generative AI, and agentic AI with TensorFlow, AutoGen, and Copilot Studio.
• Thorough preparation for the AI-900 exam

The Microsoft AI Engineer Program offers a comprehensive curriculum designed to build comprehensive AI expertise. It covers key areas such as Python programming, applied data science, machine learning, and deep learning, while offering focused tracks in natural language processing, generative AI, and Microsoft Copilot. Participants apply their knowledge through hands-on projects and a final assignment that demonstrates their ability to tackle real-world business challenges.

The program also includes targeted preparation for the Microsoft Azure AI Fundamentals (AI-900) exam, along with training in Copilot development and advanced generative AI techniques using tools such as PyTorch and TensorFlow. Participants can further develop their skills through elective courses in exciting topics such as ChatGPT, transformers, and prompt engineering, giving them a solid foundation for a future-oriented career in AI. Led by experienced Microsoft instructors, the program ensures that participants gain valuable industry insight and are ready to take on influential roles in AI and machine learning.

Key features

• Course and materials in English
• Beginner to advanced level
• 6-month bootcamp program with a fixed schedule (ask us about the next round!)
• 185 hours of live online instruction every weekend with Microsoft experts
• 30 hours of e-learning materials
• Masterclass on agentic AI solutions with Copilot Studio and AutoGen
• Study time: approximately 250–300 hours
• 1 year of access to the learning platform and lesson recordings
• Microsoft Learn badge and certificate of completion for Microsoft course included
• 25+ projects and a final project for practical application
• Preparation for the AI-900 exam (exam included)

An engaging learning journey

• Peer-to-peer interaction: Experience a collaborative classroom environment by connecting with colleagues and interacting with mentors in real time via Slack.
• Flexible learning: Missed a session? Stay up to date with access to lesson recordings and continue learning alongside your classmates.
• Mentor sessions: Get expert help with problem solving, project support, and in-depth learning through mentor-led sessions.
• Dedicated learning support: Benefit from a group leader who will help you with questions and ensure steady progress throughout your learning journey.

Learning objectives

• Data Science with Python: Build essential Python skills for data analysis using modern tools and techniques to discover actionable insights.
• Machine learning: Gain knowledge of key ML concepts such as model development, optimization, and practical applications through hands-on projects.
• Deep learning: Master neural networks, propagation techniques, hyperparameter tuning, and model interpretation skills with practical experience using PyTorch.
• Preparation for the AI-900 exam: Acquire in-depth knowledge of Microsoft Azure AI services and cloud-based AI solutions to prepare for the Azure AI Fundamentals (AI-900) certification.
• Copilot development: Learn how to design and improve Microsoft Copilot solutions using RAG-based architectures, prompt flows, and performance evaluation in Copilot Studio.
• Low-code and open source tools: Explore low-code platforms such as Copilot Studio and open source frameworks such as AutoGen to create intelligent AI-driven workflows.
• Natural language processing (NLP): Develop expertise in NLP algorithms, text analysis, and industry applications of language technologies.
• Generative AI: Understand advanced GenAI techniques—including transformers, attention mechanisms, GPT, and BERT—to build the next generation of AI applications.
• Capstone projects: Apply AI and ML skills to a real-world problem and demonstrate your expertise and readiness to employers.
• Industry masterclasses: Participate in sessions led by experts on Agentic AI and advanced tools such as Copilot Studio and AutoGen.

Target

• Beginners with an interest in artificial intelligence who want to enter the technology industry
• Software developers and engineers who want to improve their AI and machine learning skills
• Data analysts or data scientists who want to deepen their AI knowledge
• Students and academics in computer science, IT, or related fields
• Technicians from other fields (such as DevOps, QA, or business intelligence) who are planning to transition to AI roles
• Professionals with a non-technical background (such as finance, healthcare, or marketing) with a strong interest in leveraging AI in their industry
• Entrepreneurs or start-up founders exploring AI-based solutions for business innovation

Prerequisites

• You must be 18 years or older and have a high school diploma (or equivalent).
• At least two years of professional experience is recommended but not required.
• Basic knowledge of programming and mathematics is a prerequisite.

Bootcamp course plan

1. Python for AI
2. Applied data science with Python
3. Machine learning with Python
4. Specialization in deep learning
5. Fundamentals of Microsoft Azure AI AI 900
6. Fundamentals of Microsoft Copilot
7. Gen AI knowledge
8. AI Engineer Capstone

Elective courses:

• Natural language processing (NLP)
• Advanced Generative AI
• Masterclass in Agent-based AI Solutions with Copilot Studio and AutoGen

1. Python for AI

This course provides a solid foundation in Python programming and teaches the core skills needed for your learning journey. You will learn how to use Python to implement AI algorithms, perform data analysis, and develop intelligent systems efficiently.

Learning objectives:

• Confidently install Python, understand its syntax, and work with basic programming constructs.
• Work with Python data types, operators, conditional statements, and loops.
• Create and use Python functions for modular coding.
• Apply the principles of object-oriented programming (OOP) in Python.
• Understand and implement threading and multithreading in Python programs.

Topics covered:

• Introduction to Python programming
• Python data types and operators
• Conditional statements and loops
• Python functions
• Basic features of Python programming
• Object-oriented programming with Python

2. Applied data science with Python

This course introduces fundamental concepts in data science, including data preparation, model building, and evaluation. You will strengthen your Python skills through topics such as strings, lists, and Lambda functions, while delving deeper into NumPy, linear algebra, and statistical principles such as central tendency, dispersion, skewness, covariance, and correlation. You will also explore hypothesis testing methods (Z-tests, T-tests, ANOVA), practice advanced data manipulation with Pandas, and improve your analytical skills with practical visualization techniques.

Learning objectives:

• Gain a solid understanding of the data science lifecycle and its core elements.
• Develop your knowledge of Python and its data science libraries.
• Use NumPy and Pandas effectively for data manipulation and analysis.
• Create compelling and insightful visualizations with Matplotlib, Seaborn, Plotly, and Bokeh.
• Learn data management and preprocessing techniques to prepare data sets for analysis.

Topics covered:

• Introduction to data science
• Basics of Python programming
• Basics of NumPy
• Linear algebra for data science
• Basics of statistics
• Probability distributions
• Advanced statistics
• Working with Pandas
• Data analysis techniques
• Data management methods
• Data visualization tools
• Apply statistics from start to finish in Python

3. Machine Learning with Python

Learn the fundamental concepts of machine learning and explore different types and applications. The course guides you through the entire ML process, with a strong focus on supervised learning using regression and classification models. You will also delve into unsupervised learning, clustering methods, and ensemble techniques. In addition, you will work with frameworks such as TensorFlow and Keras and gain hands-on experience using PyTorch to build a recommendation system.

Learning objectives

• Understand the main categories of machine learning and their unique characteristics.
• Explore the machine learning workflow and the role of MLOps.
• Apply supervised learning techniques to real-world problems.
• Recognize and handle overfitting and underfitting in models.
• Implement various regression models and evaluate their practical use cases.
• Gain knowledge of ensemble techniques such as bagging, boosting, and stacking.

Topics

• Fundamentals of machine learning
• Supervised learning methods
• Regression models and their applications
• Classification models and use cases
• Unsupervised learning methods
• Ensemble learning strategies
• Building recommendation systems

4. Specialization in deep learning

This course explores the foundations and real-world applications of deep learning, with an emphasis on how it differs from traditional machine learning. You will study core concepts such as neural networks, forward and backward propagation, hyperparameter tuning, and model interpretability. The curriculum also covers advanced topics, including TensorFlow 2, Keras, convolutional neural networks (CNNs), transfer learning, object detection, recurrent neural networks (RNNs), autoencoders, transformer models for NLP, and practical implementation with PyTorch.

Learning objectives

• The difference between deep learning and machine learning.
• Explore practical use cases for deep learning in various industries.
• Apply forward and backward propagation in deep neural networks.
• Perform hyperparameter tuning and improve the interpretability of models.
• Implement regularization techniques such as dropout and early stopping.
• Gain practical expertise with CNNs for vision-based tasks such as object detection.

Topics

• Introduction to deep learning
• Artificial neural networks (ANN)
• Deep neural networks (DNN)
• TensorFlow framework
• Model optimization and performance tuning
• Convolutional neural networks (CNN)
• Transfer learning methods
• Object detection techniques
• Recurrent neural networks (RNN)
• Transformer models for NLP
• Introduction to autoencoders
• Basics of PyTorch

5. Microsoft Azure AI Fundamentals AI 900

This module is designed to help you prepare for the Microsoft Azure AI Fundamentals (AI-900) exam. You will explore the fundamental concepts, benefits, and key components of Azure cloud services with a focus on AI-driven applications. The training also covers important areas such as cost management, governance, and compliance, so you are ready to work effectively in the Azure ecosystem.

Learning objectives

• Understand the key concepts and services of the Azure cloud platform.
• Learn how to manage costs, apply governance practices, and maintain compliance.
• Build a strong foundation in cloud and AI fundamentals to prepare for the AI-900 certification with confidence.

Topics

• Overview of Azure cloud services
• Cost management and governance in Azure
• Security and compliance in the Azure cloud

6. Microsoft Copilot Foundation

This module provides an introduction to Microsoft Copilot and its key features. You will learn how to design and manage copilots using the Copilot Studio interface, publish bots, and evaluate their performance. The course also explores how to develop RAG-based Copilot solutions, apply fundamental language model techniques, and use prompt flow to improve Copilot development and functionality.

Learning objectives

• Create and manage copilots with Microsoft Copilot Studio
• Build RAG-enabled copilots and apply model-grounding techniques
• Develop prompt flow skills to optimize Copilot capabilities.

Topics

• Work with the Microsoft Copilot Studio interface
• Publish bots and monitor their performance
• Design RAG-based Copilot solutions
• Apply prompt flow and fundamental concepts in Copilot development

7. Generative AI Competency

Develop a solid foundation in generative AI and machine learning (ML) by understanding fundamental concepts, key algorithms, and practical use cases. Gain knowledge of deep learning techniques, large language models (LLMs), and AI-powered tools, equipping you with the skills to design and apply AI solutions in the real world.

Learning objectives

• Distinguish between AI, machine learning, deep learning, and generative AI, and understand the relationships between them.
• Identify the differences and practical applications of supervised, unsupervised, and reinforcement learning.
• Study generative AI techniques, including neural networks, GANs, and transformers.
• Learn how large language models (LLMs) power intelligent assistants and chatbots.
• Explore popular AI models such as ChatGPT, Gemini, Claude, and Falcon.
• Understand image generation methods such as GANs, diffusion models, and VAEs.
• Gain hands-on experience with creative AI tools such as DALL·E 2, Stable Diffusion, and MidJourney.
• Work with video generation platforms such as Runway ML, Synthesia, and Gen-2 by Runway.
• Discover the role that open source ecosystems such as Hugging Face play in driving AI innovation.
• Learn how AI marketplaces track new tools and explore prompt marketplaces such as PromptBase.
• Build strong skills in prompt engineering for chatbots and AI-powered search.
• Experiment with OpenAI Playground and adjust parameters such as temperature and sampling for tailored results.

Topics covered:

• The basics of machine learning and generative AI
• Categories of machine learning: supervised, unsupervised, and reinforcement learning
• Generative AI techniques: neural networks, GANs, and transformers (e.g., GPT models)
• Large language models (LLMs) and chatbots (e.g., ChatGPT, Gemini, Claude, Falcon)
• Image generation methods: GAN, diffusion models, VAE with practical exercises using DALL·E 2, Stable Diffusion, and MidJourney
• Video generation methods: GAN, diffusion models, transformers with tools such as Runway ML, Synthesia, and Gen-2 from Runway
• Explore the open AI ecosystem: Hugging Face and AI marketplaces
• Prompt Engineering for developing chatbots and AI-powered interactions

8. Get started with advanced AI using transformers

The Capstone project is the final milestone of the program, where you will have the opportunity to summarize all the skills and knowledge you have acquired. You will apply AI and machine learning techniques to tackle real-world, industry-specific challenges. This hands-on project not only demonstrates your expertise, but also provides you with a portfolio-worthy showcase to showcase your skills to potential employers.

Learning objectives

• Use AI and ML techniques to tackle practical industry problems.
• Gain practical experience by creating AI-driven solutions.
• Demonstrate your skills with a comprehensive final project.

Topics

• Solve industry-relevant AI challenges
• Design and implement complete AI/ML solutions
• Present projects and build a professional portfolio.

Optional

• Natural Language Processing (NLP)

This masterclass covers low-code agentic AI solutions with tools such as Copilot Studio and AutoGen, enabling rapid implementation of workflows. You will explore natural language understanding, generation, speech recognition, text-to-speech, and voice assistants. Upon completion, you will have the knowledge necessary to design advanced NLP and speech-based applications.

This course in generative AI provides an in-depth study of advanced AI capabilities, with an emphasis on models such as VAE, GAN, LLM, and Transformers. You will explore attention mechanisms, LangChain workflows, and advanced prompt engineering, while gaining the skills to design, build, and optimize real-world applications powered by generative AI.

• Advanced Generative AI

This course in generative AI provides a comprehensive overview of modern AI innovations, including VAE, GAN, LLM, and Transformer architectures. Participants explore attention mechanisms, LangChain workflow design, and advanced prompt engineering while gaining the skills to design, build, and optimize generative AI applications.

• Masterclass in Agent-based AI Solutions with Copilot Studio and AutoGen

This masterclass offers live sessions on low-code agent-based solutions, using tools such as Copilot Studio and open-source frameworks such as AutoGen, and demonstrates how these platforms accelerate AI development and streamline the implementation of intelligent workflows.

Industry projects:

• Project 1: MLB Digital Platform Enhancement: Develop backend modules to manage statistics, schedules, and bookings while adding multi-threaded reporting for faster performance.
• Project 2: EdTech Backend System: Build backend features for managing student data and courses, improving user experience, and supporting UI upgrades.
• Project 3: Sales Strategy Analysis: Examine state-level sales data to identify high-performing regions and recommend strategies for underperforming areas.
• Project 4: Marketing Strategies with EDA: Perform exploratory data analysis and hypothesis testing to discover drivers of customer acquisition and refine marketing plans.
• Project 5: Employee turnover prediction: Build an ML model to predict employee turnover by analyzing tenure, satisfaction, and work patterns to gain insights into employee retention.
• Project 6: Song classification with cluster analysis: Use clustering techniques to create personalized playlists and increase user engagement through better recommendations.
• Project 7: Analysis of mortgage data: Build a deep learning model to predict loan defaults using historical, unbalanced datasets, improving loan security.
• Project 8: Analysis of Lending Club loan data: Develop a predictive model using Lending Club loan data from 2007–2015 to assess the risk of payment defaults and address class imbalance.
• Project 9: ChatGPT-based storytelling: Create an interactive storytelling tool with ChatGPT that enables collaborative storytelling without coding to improve creative writing.
• Project 10: Virtual project management consultant: Design ChatGPT prompts to provide project management support in planning, risk assessment, and team collaboration.
• Project 11: AI-driven HR assistant for Nestlé: Build an HR assistant with OpenAI GPT and Gradio to extract answers from policy PDF files and streamline employee support.
• Project 12: AI-driven design: Use DALL·E and Gradio UI to transform text prompts into creative designs and highlight the role of AI in digital marketing.

Frequently asked

How is the Bootcamp structured?

The program is an intensive online bootcamp with a fixed class and schedule held every weekend afternoon and evening, based on a learning path from beginner to advanced level.

These learning paths consist of different courses and topics related to specific skills for a role or job. You will have access to our Learning Management System, which will help you navigate all future schedules and course materials. In addition to this, there are additional e-learning courses that you can complete at your own pace. There is always someone available to help and support you if you have questions about the skills you are learning.

How long does it take to complete Bootcamp?

Thanks to the combination of e-learning and bootcamp with live online lessons, the program normally takes 6 months (5–10 hours/week). A new round starts every two or three months. Please contact us for more information.

When can I take the Bootcamp courses online?

The live classroom is only held on weekends. If you miss a session, you can always catch up by watching the recordings, and you will be marked as present. So you never miss any content.

When can I unlock my master certificate?

Once you have completed at least 85% of the course material, you can unlock your certificate. This applies to all master's programs/bootcamps. One of the criteria for obtaining the master's certificate is to participate in the live courses. However, exceptions can be made if you are unable to participate live, but you are still required to watch the recordings. Read more about your specific course or email us for more information.

What is the Microsoft AI Engineer program?

The Microsoft AI Engineer certification is designed to give professionals the skills they need to build comprehensive AI solutions with Microsoft Azure. The program covers key areas such as Python programming, machine learning, deep learning, natural language processing (NLP), and generative AI. Participants gain hands-on experience with tools such as Azure OpenAI and Copilot, while working on real-world projects to improve their practical expertise.

What certification will I receive after completing the program?

Upon completing the Microsoft AI Engineer certification, you will receive an official certificate from Microsoft. You will also receive individual certificates for each completed module along with a Microsoft Learn badge. These credentials strengthen your resume and validate your expertise as a certified AI engineer, helping you stand out to employers in the AI and technology sector.

What tools and platforms will I learn to use?

In the AI Engineer program, you'll work directly with key tools and platforms used by AI professionals. These include Python, TensorFlow, PyTorch, and Azure OpenAI for model development and deployment. You'll also explore generative AI technologies such as ChatGPT, DALL·E, and other advanced Microsoft AI solutions, giving you the opportunity to tackle real-world projects across a range of industries.

How is this program different from other AI courses?

This AI Engineer certification has been created in partnership with Microsoft, one of the world's leading technology companies. It combines live sessions led by experts with project-based learning using tools such as Azure OpenAI, ChatGPT, and TensorFlow. With its strong focus on practical skills, industry relevance, and career preparation, the program stands out as an ideal choice for those looking to pursue a career in artificial intelligence.