AI FUNDAMENTALS COURSE

AI FUNDAMENTALS

One-day course preparing decision makers and employees engaged in AI/ML project to drive better business by making insightful AI strategy decisions and beneficial project outcomes.
Upon completing the course participants receive a Certificate of Accomplishment.

Who is this course for?
Cross-industry decision makers, leaders, strategists (CXOs, directors, VPs, product managers, senior managers, team leads, project managers etc.), employees involved in the AI projects and interested in AI beyond the hype.

Booking

Book this session for your company group by sending us the following info:

  • Name of your organisaton
  • Contact person
  • Location and date for the course
  • Approximate number of attendees
  • Price:
    100.000 SEK

    Why choose this course?

    After taking this course, you will:

    • Gain a competitive edge in your role allowing you to have/take fruitful AI/ML discussions/decisions.
    • Learn the difference between AI, Machine Learning, Data Science, Big Data, Rule-based (RPA) AI, Predictive AI, IoT etc.
    • Know how to select and define business beneficial AI/ML uses cases, with ROI, and how to go about it.
    • Have explored number of applied AI business use cases and know which algorithms are best suitable for each and how they work.
    • Know how to launch and execute an AI/ML project, from the use case brainstorming session to the model training and deployment.

    And we will go deeper, and you will:

    • Learn about different ways machines learn from data, how they are trained, and models build.
    • Learn about what data is needed for what type of models and the importance of data quality.
    • Learn about roles, skills and experiences required in an AI and data science team or project.
    • Learn about the AI/ML/Data Science infrastructure components; frameworks, cloud foundations, platforms, etc.
    • Understand which role legal, compliance and security have in the data-driven projects.
    • Know how trusted and accurate AI machines are built and about challenges & pitfalls associated.
    • Have practically tried out building prediction models and code in our data science laboratory.

    Sections covered in this course

    Klick or hover over the boxes and read about the content in each section

    AI HYPE, BRIEF HISTORY & INTRO
    • Hype, Intelligence, AI Coining & Accelerators
    • Artificial Narrow, General & Super Intelligence
    • Data Science fields & AI buzziness
    DIFFERENT WAYS MACHINES LEARN
    • Supervised Learning – Used in majority of AI application
    • Unsupervised Learning – From patterns to predictions
    • Semi-supervised Learning – Combining clusters and labels
    • Reinforcement Learning – Taking suitable action to maximize reward
    APPLIED ML USE CASES WITH SUPERVISED LEARNING
    • Regression vs Classification anatomy
    • Most common SL algorithms
    • Walk through some AI SL use cases and algorithms behind
      • Expected load on authentication server?
      • Will the savings account have a long investment horizon?
      • Expected demand of pallet trucks?
      • Should the user be automatically logged out from the supplier portal or webpage?
      • Will the unit remain one month in the warehouse?
      • Should you grant a loan to applicant?
      • How to detect fraud transactions?
      • Is it time to service the conveyor belt?
      • Can the unit be delivered on time?
      • Is escalation from chatbot to human needed?
    DIVE INTO NEURAL NETWORKS
    • Artificial vs Human Neurons
    • The Perceptron
    • Neural Network Layers
    • Building & Training the Network: Feed forward, Backpropagation
    • Activation Functions, Loss  Functions, Gradient Descent
    DEEP LEARNING (DL) ARCHITECTURES
    • Multilayer Perceptron
    • Focus on: Image Recognition
      • CNN (Convolutional NN)
      • Autoencoder
      • GAN (Generative Adversarial Network)
    • Focus on: Text Analytics and NLP Technology
      • RNN (Recurrent NN)
      • Long Short-Term Memory – Type of RNN
      • Transformers
    AI/ML PROJECT WORKFLOW AND RESOURCES
    • Walk through 11-steps AI ML Workflow: From use case to model
    • Data Science Teams, Competences, Technical Architecture
    KEYS TO TRUSTWORTHY AI
    • How can we trust AI/ML?
    • Hidden costs in AI
    • Data Quality
    • State of Art of Data Science
    • Ethical AI
    HANDS-ON LAB WALK THROUGH
    • Explore data scientist working environment in Google Cloud Lab, Jupiter notebook with model for house pricing predictions
    • Tweak your own Regression & Neural Network modelling, no coding skills needed, easy followed instructions
    INSPIRATIONAL LATEST AI/ML PROGRESS
    • E.g. Can AI be creative?
    • E.g. Data security with AI/ML

    Meet our course instructors and AI/ML experts

    Dr. MARKUS BORG
    Senior Researcher

    PhD Software Engineering
    (LTH)

    RISE SICS: Senior Researcher
    Lund University: Adjunct
    Lecturer

    MELINA KATKIC
    Data Science Leader

    M.Sc. Computer Science (LTH)
    B. Sc. Business &
    Administration

     Founder: NordAxon
    Co-founder: Barrel AI

    OSCAR BAGGE
    Data Scientist Lead

    M.Sc. Industrial
    Engineering and
    Management
    (LTH)

    Data Science Manager:
    NordAxon

    DAVID BAO FU
    Junior Data Scientist

    M.Sc. Engineering Physics
    (LTH)

    Junior Data Scientist:
    NordAxon

    ISABELLA GAGNER
    Junior Data Scientist

    M.Sc. Engineering Physics
    (LTH)

    Junior Data Scientist:
    NordAxon

    EMIL WÅREUS
    Data Scientist

    M.Sc. Mechanical Engineering
    spec.Mechatronic (LTH)
    M.Sc. Artificial Intelligence
    (Nanyang Technical University)

    Head of Data Science:
    Debricked

    Do you have any questions?

    Send us a message and we will come back to you right away

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    > 500

    AI/ML upgraded client employees