--- title: "AI Notebooks - Tutorial - Build your spam classifier" description: "How to build your Spam classifier thanks to Machine Learning" url: https://docs.ovhcloud.com/es/guides/public-cloud/ai-machine-learning/ai-notebooks-spam-classifier lang: es lastUpdated: 2022-11-22 --- # AI Notebooks - Tutorial - Build your spam classifier ## Objective This tutorial will show you how to build a simple spam classifier with **OVHcloud AI Notebooks**. You will be able to learn the concepts of logistic regression, dimension reduction, stop words, quantiles and much more. A very simple Machine Learning model will be used: the **logistic regression**. At the end of this tutorial, you will have learnt the principal methods to build your own **spam classifier**. ![image](/images/public-cloud/ai-machine-learning/notebook-tuto-09-spam-classifier/spam-classifier.png) :::info We will be able to create this model with the dataset named **SMSSPamCollection**. Find it on the SMS Spam Collection Dataset [link](https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection). ::: ## Requirements - An AI Notebooks project created inside a [Public Cloud project](https://www.ovhcloud.com/es-es/public-cloud/) in your OVHcloud account - A user for AI Notebooks *** ### OVHcloud Control Panel Access - **Direct link:** Public Cloud Projects - **Navigation path:** Public Cloud > Select your project *** ## Instructions You can launch your notebook from the OVHcloud Control Panel or via the ovhai [CLI](/es/guides/public-cloud/ai-machine-learning/ai-cli-run-notebook.md) . ### Launching a Jupyter notebook with "Miniconda" via UI To launch your notebook from the OVHcloud Control Panel , refer to the following steps. #### Code editor Choose the `Jupyterlab` code editor. #### Framework In this tutorial, the `Miniconda` framework is used. :::warning With **Miniconda**, you will be able to set up your environment by installing the Python libraries you need. ::: You can choose the `conda` version you want. :::info The default version of `conda` is functional for this tutorial. ::: #### Resources You can choose the number of CPUs or GPUs you want. :::info Here, using `4 CPU` is sufficient. ::: ### Launching a Jupyter notebook with "Miniconda" via CLI If you want to launch it with the CLI, choose the `jupyterlab` editor and the `conda` framework. To access the different versions of `conda` available, run the following command. ```console ovhai capabilities framework list -o yaml ``` :::info If you do not specify a version, your notebook starts with the default version of `conda`. ::: Choose the number of CPUs (``) to use in your notebook and use the following command. ```console ovhai notebook run conda jupyterlab \ --name \ --framework-version \ --cpu ``` You can then reach your notebook’s URL once the notebook is running. ### Accessing the notebook Once the repository has been cloned, find your notebook by following this path: `ai-training-examples` > `notebooks` > `natural-language-processing` > `text-classification` > `miniconda` > `spam-classifier` > `notebook-spam-classifier.ipynb`. A preview of this notebook can be found on GitHub [here](https://github.com/ovh/ai-training-examples/blob/main/notebooks/natural-language-processing/text-classification/miniconda/spam-classifier/notebook-spam-classifier.ipynb). ## Go further - If you are interested in **NLP** (Natural Language Processing), familiarise yourself with speech to text by following this [tutorial](/es/guides/public-cloud/ai-machine-learning/ai-notebooks-speech-to-text-recognition.md). If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/es-es/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project. ## Feedback Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud)