# Tutorials that will get you onboarded

These tutorials will introduce you to the Peltarion Platform and give you a solid base to stand on when you start to build your projects. Nothing beats hands-on experience.

We have divided the tutorials based on input data. Pick the tutorials that are most similar to your data.

## Tabular data tutorials

1. Single-label tabular classification - Buy or not? Predict from tabular data
Money!! Understanding what makes a user willing to cash up and buy a product has always been key to businesses.
This tutorial will show you how you can build simple AI models using the spreadsheets that so many of us work with.

2. Tabular regression - Sales forecasting with spreadsheet integration
This tutorial will show you how to use the full power of the Peltarion Platform in a real-world situation, predicting daily sales revenue from many parameters.
You will build a model that solves this so-called regression problem. The platform lets you deploy this model for production, allowing you to directly integrate predictions in your Google Sheets or integrate predictions in your Microsoft Excel spreadsheets.

We’ve created add-ins for Google spreadsheets and Excel to make it super easy for you to leverage the Peltarion Platform from your favorite tool.

## Image data tutorials

1. Single-label image classification - Deploy an operational AI model
Starting with MNIST is good for anybody who wants to try deep learning techniques and pattern recognition methods on real-world data while spending minimal effort on preprocessing and formatting.

2. Regression - Predict real estate prices
Predict real estate prices, a so-called regression problem, and we will solve it using both table data and images. Using multiple kinds of data is often hard, but the Peltarion Platform makes it easy.

3. Image similarity - Find similar images of fruits
Image similarity is a way to quantify how similar two images are.

## Text data tutorials

1. Single-label text classification - Find out sentiment of a movie review
Solve a single-label text classification using BERT (Bidirectional Encoder Representations from Transformers). The input is an IMDB dataset consisting of movie reviews, tagged with either positive or negative sentiment – i.e., how a user or customer feels about the movie.

2. Multilingual classification - Classify text in any language
Learn how to use the Peltarion Platform and its Multilingual BERT snippet to create a model that is able to work with 100 languages simultaneously!

3. Text similarity - Text similarity search
Text similarity will help you build models that compare and find texts that are similar in context and meaning, without them sharing a single common word.