Искусственный интеллект (Machine learning) для руководителей
Course ID : AI-001
Duration In-class (в days) : от 2 дн.
Duration Online : от 2 дн.
Сurriculum : очно, Virtual Instructor-Led Training - ONLINE
Overview
The course covers the basics of machine learning with a focus on its application in business.
The curriculum includes the study of basic machine learning models, as well as analysis of various cases of their application in real business scenarios.
Students will master the skills of collecting and processing data, analyzing and evaluating model results, as well as strategies for managing artificial intelligence projects.
Students will be ready to use machine learning tools to optimize business processes and make effective management decisions.
Audience for this course
- Heads of departments
- IT project managers
Objective
Learn to use the capabilities of AI and machine learning to solve applied problems in subject areas.
Outcomes
After completing the course, students will gain skills that will allow them to:
Know the basics of machine learning: Basic concepts and techniques of machine learning
Be able to analyze data: students will master methods of collecting, cleaning and analyzing data, which will allow them to work with large volumes of information and extract valuable insights from it
Evaluate the effectiveness and accuracy of machine learning models so you can make informed, data-driven decisions
Manage AI projects: students will learn methods for managing artificial intelligence projects, including developing strategies and assessing risks
Apply various machine learning models to solve business problems
Outline
1. Introduction to artificial intelligence and machine learning
2. Types of machine learning models and categories of tasks for using AI
3. Analysis of cases of using ML models in business problems
4. Data Science and Data Analysis Tools
5. Stages of development and application of the ML model
6. Analysis of cases of step-by-step development and implementation of ML models
7. Case analysis: where and how to apply the ML model (task identification)
8. Immersion in the stage of data collection and processing, assessment of data sufficiency
9. Dive into the model creation stage, classification and regression
10. Practical task on data processing and model selection
11. AI project management. AI implementation strategy. Evaluation of implementation results
12. Analysis of cases of AI project management and AI implementation
