To wersja testowa nowego serwisu infoShare Academy — wyświetlane treści i oferta nie są wiążące ani kompletne

AI

Comprehensive Machine Learning Training

Hands-on AI and data analytics workshops — built around your team's real cases.

Duration
45h · 6 days
Who it's for

Ideal for teams that…

1 For developers, data analysts, business analysts, marketers, designers, and anyone for whom machine learning can significantly facilitate their work
2 For people who already know a bit about data processing and analysis – this will make it easier to understand the material presented
3 For those with basic experience in programming in Python
Outcomes after the program

Hands-on AI and data analytics workshops — built around your team's real cases.

You will use Python in machine learning projects – leveraging libraries such as Pandas, NumPy, scikit-learn, and Matplotlib

After getting familiar with the essential tools and libraries, you will move on to further learning, including working with files, data cleaning, and machine learning models

You will learn practices that allow better management of code and project structure when building web applications

While writing code, you will pay particular attention to the possibility of integrating it with code written by other people

You will learn the language of developers, concepts, principles, and best practices of working with data, as well as how to communicate effectively in a programming team

Program

What we actually do

  • · Module 1: Introduction to Machine Learning Introduction to machine learning Key concepts The importance of splitting data into training, validation, and test sets Different types of machine learning Data as features Qualitative vs. quantitative data
  • · Module 2: Data Processing and Analysis Introduction to libraries: Pandas, NumPy, Matplotlib, scikit-learn Working with files Data cleaning Data wrangling
  • · Module 3: Jupyter Notebook Interactive environment Python virtual environments Cells, code, markdown Widgets IPython
  • · Module 4: Machine Learning Models Linear regression Logistic regression Decision trees Random forest XGBoost Naive Bayes KNN classification SVM
  • · Module 5: Model Comparison and Consolidation Methods for selecting models for specific use cases Comparing different models Practical exercises
Every module is adapted to your stack and context. The above is a starting point — not a fixed agenda.
How we work

From brief to retro in 30 days.

01

Brief & diagnosis

A call with the team lead + a short survey for participants. We define goals, gap and context.

02

Program customization

We adapt modules, case studies and code examples to your stack. Approval in 5 days.

03

Workshop

Trainer-led sessions, hands-on, code review. Mentor available between sessions too.

04

Retro + report

Outcome report for the team and lead. 30 days of consulting included.

Inquiry

Send a brief. We'll reply within 1 day.

After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.

Quote within 48h of the brief
First session within 30 days
Pilot before the full decision
VAT invoice, payment in instalments possible

How we handle your data

We process your business data (name, work e-mail, phone number, company, job title) in order to handle your corporate training inquiry and prepare an offer. The data controller is infoShare Academy Sp. z o.o., Al. Grunwaldzka 472B, 80-309 Gdańsk. Providing the data is voluntary but necessary to receive a response. You have the right to access, rectify, erase or restrict the processing of your data and to object to it. Full information is available in our data processing notice.

Optional marketing consents

The controller of your personal data is infoShare Academy sp. z o.o. The rules for processing personal data are set out in the Privacy Policy and the Data Processing Notice.