Gain insights into simplifying machine learning with PyCaret. Delve into regression, classification, clustering, and anomaly detection, and learn to deploy applications using Streamlit.

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PyCaret is an low-code, open-source, machine learning library for Python. It can be used in machine learning tasks such as data preparation and model deployment.

In this course, you will learn multiple topics related to machine learning. You will start with a brief introduction to the basic concepts of machine learning, and then continue with case studies of regression, classification, clustering, and anomaly detection based on the respective modules of the PyCaret library. Finally, we will focus on using the Streamlit library to develop and deploy machine learning applications.

By the end of this course, you will have the skills to deploy the robust PyCaret library for any of your machine learning projects.

Simplifying Machine Learning with PyCaret in Python

# Software requirements
The code in this course should work on all major operating systems, that is, Microsoft Windows, Linux, and Apple macOS. We’ll need Python 3 and JupyterLab installed on our computer. Another useful tool is [Anaconda](https://www.anaconda.com/), a machine learning and data science toolkit that includes numerous helpful libraries and software packages. Anaconda can be freely downloaded at this [link](https://www.anaconda.com/products/individual). Alternatively, you can use a cloud service like [Google Colab](https://colab.research.google.com/) to run Python code without worrying about installing anything on your machine.




# Installing PyCaret

The PyCaret library can be installed locally by executing the following command on your Anaconda terminal. You can also execute the same command on Google Colab or a similar service, to install the library on a remote server.

`pip install pycaret[full]==2.3.4`

After executing this command, PyCaret will be installed and we’ll be able to run all code examples of the course. It is recommended to install the optional dependencies as well, by including the `[full]` specifier. Furthermore, installing the correct package version ensures maximum compatibility. This course uses PyCaret ver. 2.3.4. Finally, [creating a conda environment](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html#managing-envs) for PyCaret is considered to be best practice because it will help you avoid conflicts with other packages and make sure you always have the correct dependencies installed.


# Installing Spacy and Textblob
PyCaret NLP module requires the Spacy English language model, as well as the TextBlob corpora. These aren't included with the PyCaret installation, so you have to download them manually, by executing the following commands on your Anaconda terminal.

```
python -m spacy download en_core_web_sm
python -m textblob.download_corpora
```

After doing that, the additional resources are going to be downloaded and you'll be able to use the natural language processing module without any problems. Obviously, if you have already downloaded those files for another project that utilizes the Spacy and TextBlob libraries, you can safely skip this step

# Using JupyterLab
**JupyterLab** is a powerful web-based user interface that lets us create and execute Jupyter notebooks containing Python code. We can run JupyterLab by executing the following command on the Anaconda terminal. Substituting `lab` with `notebook` will run the older Jupyter Notebook web interface, but do that only if you have problems or compatibility issues with JupyterLab.

`jupyter lab`

When JupyterLab starts, you can create a new Jupyter Notebook and run the code that is provided in each chapter of the course. After executing a Jupyter Notebook, both the code and its output are displayed to the user. This file format can also contain text, figures, and visualizations. Jupyter Notebook is a powerful format that has become the standard in data science and machine learning. You can refer to the official [JupyterLab Documentation](https://jupyterlab.readthedocs.io/en/stable/index.html) for more information and help about using JupyterLab. In the above chapters, each code snippet represents a single Jupyter Notebook cell that can be executed by pressing the “Shift” + “Enter” keyboard shortcut.

# Installing Streamlit
You can easily install the Streamlit Python library by executing the following command on our terminal. `pip install streamlit`.

# Github repository
You can access all code examples included in this course at the official [Github repository](https://github.com/CourseMaterial/pycaret-course). Each folder contains the Python code of the same-titled course chapter. You can also clone the repository to your local machine if you wish. Git is the de facto standard for version control, so it’s assumed that most developers will be familiar with it. In case you haven’t used it, please refer to the official [quick reference guide](https://training.github.com/downloads/github-git-cheat-sheet/).

Learn how to install and set up PyCaret on your local machine.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Installation of PyCaret

Software requirements

Installing PyCaret