Training Introduction to machine learning
Practical information about training
- CATEGORY: Technologies
- SUBCATEGORY: AI
- TRAINING CODE: IT-AI-139
- DURATION: 1 day
- PRICE INFORMATION from: 1850 PLN net
- LANGUAGE OF TRAINING: polish
- FORM OF IMPLEMENTATION: stationary, online
Training description
A one-day introductory training course on the foundations of machine learning that combines theory and practice. During the course, participants learn basic ML concepts and algorithms through practical examples and exercises. The program is delivered in the form of interactive workshops, where each theoretical issue is immediately verified through self-implementation.
Participant profile
- Programmers who want to start working in the ML area
- Business analysts interested in ML opportunities
- Technology project managers
- IT professionals planning to expand into AI
- Technical students
- Beginner data scientists
- Individuals changing their career path towards ML
Agenda
- Fundamentals of machine learning
- Types of machine learning
- Key terms and definitions
- Typical use cases
- ML model building process
- Data preparation and analysis
- Exploratory data analysis
- Data preprocessing
- Feature engineering
- Data quality validation
- Basic algorithms
- Linear and logistic regression
- Decision trees
- Classification algorithms
- Model evaluation methods
- Practical aspects of ML
- Tools and libraries
- Best practices
- Typical problems and solutions
- Implementation of ML models
Benefits
Upon completion of the training, the participant will understand the fundamental concepts and principles of machine learning. He will gain practical knowledge to start working with basic ML algorithms. He will develop the ability to analyze and prepare data for modeling. Will be able to assess the quality of a model and interpret its results. Will learn to identify use cases where machine learning can bring business value. Will learn the most important tools and libraries used in ML projects.
Required preparation of participants
- Basic knowledge of mathematics and statistics
- Knowledge of programming in any language
- Ability to think analytically
- Fundamentals of data analysis
Issues
- Types of machine learning
- Data preprocessing
- Feature engineering
- Validation of models
- Quality measures
- Regression and classification
- Decision trees
- Overfit and underfit
- Cross-validation
- Data visualization
- Interpretation of results
- ML tools
Do you have any questions?
Feel free to contact us.
Monika Fengler
+48 532 081 700
monika.fengler@eitt.academy
31 Ząbkowska Street 03-736 Warsaw
Forms of training delivery
Stationary training
- Training at the customer's premises or at a designated location
- Training room equipped with the necessary equipment
- Training materials in electronic form
- Coffee breaks and lunch
- Direct interaction with the trainer
- Networking in a group
- Workshop exercises in teams
Remote training
- Virtual training environment
- Electronic materials
- Interactive online exercises
- Breakout rooms for group work
- Technical support during the training
- Recordings of the session (optional)
Possibility of funding
The training can be financed with public funds under:
- National Training Fund (KFS)
- Development Services Base (BUR)
- EU projects implemented by PARP
- HR Academy Program (PARP)
- Regional operational programs
If you are interested in funding, our team will help you prepare the required documentation.
HAVE A QUESTION?
Contact us for more information about our training, programs and cooperation. We will be happy to answer all your inquiries!
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Do you have any questions?
Feel free to contact us.
Monika Fengler
+48 532 081 700
monika.fengler@eitt.academy
31 Ząbkowska Street 03-736 Warsaw
FAQ - Frequently Asked Questions
- One-pager invitation with deadlines
- Project kick-off
- Strategic leadership and thinking
- Communication and Cooperation. Conflict management
- Motivating, engaging and difficult decisions in business
- Managing Change and Innovation. Leadership in crisis
- Building the organization of the future
- Best practices workshop - retrospective; creating a coherent program for middle and lower management levels























