Welcome to the world of machine learning! In this guide, we’ll explore how to set up a machine learning repository that aims to disseminate machine learning education in Portuguese. From installation to execution, we will walk you through each step with user-friendly explanations.

What Algorithms Are Implemented?

This repository includes a variety of algorithms across different categories:

• Classificação: Adaboost, Decision Trees, Naive Bayes, K-NN, Regression Logistic, Neural Networks
• Regressão: Linear, Polynomial, Multivariada
• Clusterização: K-Means, MeanShift
• Redução de Dimensionalidade: PCA, LDA

Moreover, there is a dedicated notebook focusing on Attribute Selection Methods, which include:

• Filter Methods: Pearson Correlation, Mutual Information
• Wrapper Methods: Recursive Elimination
• Embedded Methods: Regularization techniques like Lasso and Ridge, Mean Decrease Impurity, Mean Decrease Accuracy

Installation Steps

Before diving into using the repository, you need to install the necessary environment. Follow these steps carefully:

1. Download or clone the repository.
2. Download and install Miniconda. (For Windows, be sure to check the option to add Conda to environment variables (_$PATH_)).
3. Open your terminal and enter the following commands to set up your environment:

conda config --add channels bioconda
conda create -n ml python=3.5.3 numpy=1.12.1 pandas=0.20.1 matplotlib=2.0.2 scikit-learn=0.20.0 seaborn=0.7.1 jupyter=1.0.0 pydotplus==2.0.2

Using the Environment

Once the installation is complete, you are ready to use the environment. It’s important to follow the installation order before proceeding. Here’s how to execute the codes from this repository:

1. Open your terminal and enter:
• Windows:

  activate ml

• Linux/Mac:

  source activate ml

2. Launch Jupyter Notebook:

jupyter notebook

Understanding the Installation Instructions

Think of setting up your machine learning repository like preparing a complex dish. You must first ensure you have all the ingredients (software packages) ready, and only then can you begin mixing (installation commands) them together in the right order to create your masterpiece (machine learning models).

Troubleshooting

If you encounter issues during installation or while using the environment, here are some troubleshooting tips:

• Make sure you have the correct version of Python as specified in the installation commands.
• Check if Conda was added to your environment variables during installation on Windows.
• Ensure all commands were entered correctly without typos.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.