AI & Machine Learning

Training course on Artificial intelligence & Machine learning: from fundamentals, to key technology applications and real-world use cases. Learn more!

Course description

The field of Artificial Intelligence includes computer science, natural language processing, mathematics, psychology, neuroscience, data science, machine learning and many other disciplines. Artificial Intelligence can be described as the "ability" of a technological system to solve problems or perform tasks and activities typical of the human mind and abilities.

With AI, in fact, computers have been able to emulate performances and functions usually relevant to human intelligence, thanks to the study of theories, methodologies and techniques of hardware and software system design.

Main topics:

  • What is Artificial Intelligence?
  • Key concepts of artificial intelligence
  • Real World AI and Statistics
  • Machine Learning: principles and applications
  • Performance Metrics
  • AI and Big Data
  • The application of AI
  • AI bias: the algorithmic bias (Bias)
  • The future of Artificial Intelligence

Who is the course for?

Job opportunities and roles that benefit from this course

After-sales

  • After sales inspector

Design

  • Electrical sw designer
  • Electrical hm designer
  • Mechanical designer

Sales

  • Project manager

Staff

  • Technical assistant
  • Management personnel responsible

Production

  • Acquirer
  • Logistics

Production

  • Production center employee
  • Electrical fitter
  • Mechanical fitter
  • After sales assistant

Course goals

Basic Concepts

Providing an introduction to the foundations of this discipline and its countless applications

AI and Data Science

Transfer to users of services AI based the methodologies, main objectives, contents and advantages in the field of Data science

Use cases

Verify, with practical applications on real cases, the results obtainable with the use of Artificial Intelligence

Bias

Understanding and mitigating the risks of AI algorithmic biases

This course includes

  • Examples of real business cases

  • Course completion certificate

  • Downloadable in-depth content

  • Help desk support

  • Access from mobile and tablet

  • Comprehensive monitoring of educational progress

  • Skills assessment test (mandatory)

  • Intermediate assessment tests (optional)

Course completion certificate

After completing the course it is possible to obtain a downloadable certificate to display as evidence of the skills achieved. The certification can be combined according to company needs.

Course's lecturer

Alessandro Ruberti

  • PHD Candidate - Politecnico di Milano
  • Lecturer - Università degli Studi di Milano
  • Years of consolidated experience in data science and supply chain

Course's contents

  1. History and thinking
  2. Why is it important?
  3. How could you define Artificial Intelligence?
  4. Different areas of AI

  1. Three stages of AI
  2. Artificial intelligence evolution
  3. Artificial intelligence field of application
  4. Image recognition
  5. The impact of AI on society (pros and cons)
  6. How can AI help analyze complex social phenomena?

  1. The evolution and increase in computing capacity.
  2. The statistical basis of AI
  3. Programming languages
  4. The learning and prediction capabilities of AI algorithms

  1. Cognitive computing
  2. The fundamentals of machine learning of machine learning
  3. Types of machine learning frameworks
  4. Supervised learning
  5. Unsupervised learning
  6. Deep learning -neural networks
  7. Reinforcement learningcome
  8. Machine learning: checking & testing the learning mechanism
  9. Checking & testing the learning mechanism

  1. What are performance metrics?
  2. Key methods for measuring predictive performance
  3. The key variables: accuracy, precision, specificity

  1. Types of data and source analysis
  2. The professional figures in the field of big data
  3. Managing and analyzing data quality
  4. Making predictions with AI

  1. Robotics (food, automotive)
  2. Planning
  3. Algorithms (translation, word recognition, trading)
  4. Security (spam filtering, public safety, healthcare)
  5. Games and art
  6. Application areas
  7. Examples of real-world use cases

  1. Problems that AI cannot solve
  2. The risk of algorithmic pre-judgment (algorithmic bias)
  3. The causes of bias, detection and mitigation of bias

  1. Future predictions: a future of predictions
  2. The social implications of AI

Entry requirements

Access to any browser

PC / Tablet / Smartphone device

Internet connection

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