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Code related to supervised and unsupervised machine learning methods. In this project, there are examples in Python and R.

R 0.09% HTML 24.57% TeX 5.09% Makefile 0.01% Python 0.06% PostScript 0.16% Jupyter Notebook 70.01%

data-science machine-learning python r

ml-course's Introduction

Hi, I'm Fabricio Barth 👋

🔭 Today, my main interest is focused on how to build autonomous systems using machine learning and artificial intelligence concepts and tools in a large scale.
🌱 In my projects, you will see many examples of supervised and unsupervised learning, including reinforcement learning. The new implementations are in Python, but the old ones are in R.
🤘 There are also generic things, like the implementation of search algorithms.
I'm trying to keep all the projects organized. However, this is a little hard. So, be prepared! There are a lot of them which are not organized! Some of them have README files and so on. Also, there are a lot of documents in Portuguese because I'm from Brazil.

Artificial Intelligence (AI) and Reinforcement Learning (RL) projects

🧠 Are you interested in AI or RL? Maybe, those projects below will be helpful to you!

Undergraduate AI subject: Insper/ia_bcc
A library to help you learn AI: Insper/ai_gym
Undergraduate RL subject: Insper/rl

ml-course's People

Contributors

Stargazers

Watchers

Forkers

ricardopedrotti fishermanzi igoredm jerfsonbruno cesar-mlw

ml-course's Issues

Projeto 1: pré-processamento e análise descritiva

Neste projeto cada equipe deverá entregar um Notebook cujo o objetivo é explicar e descrever com exemplos como realizar o pré-processamento e análise descritiva usando Python.

Este notebook deve ser composto por duas partes:

pré-processamento, e;
análise descritiva.

A parte do pré-processamento deverá incluir todas as operações discutidas em sala de aula e descritas nos slides sobre pré-processamento. Além disso, o material deverá incluir as funções de merge, concat, replace, subset, groupby e conversão de tipos.

A parte de análise descritiva deverá incluir todas as operações discutidas em sala de aula e descritas nos slides sobre análise descritiva. Entre elas, histograma, boxplot, scatterplot, correlação, média, mediana, desvio padrão, entre outras.

A data de entrega é até 10/09/2019 (terça-feira) às 23:00 horas. O notebook deverá ser um dos itens do projeto no Watson Studio. O professor deverá ter acesso ao projeto. A submissão no blackboard deverá ser apenas o link do notebook ou nome do projeto junto com o nome do notebook.

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