<aside> ✅ A page with the aim of register all the process learning in Data Science and Machine Learning in the project of Google Developers Students Club with Omdena

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Introduction

Interesting Data

• Books and Documents on Project Gutenberg
• Kaggle Database
• Scraping Websites
• API Apps

Basic Requirements in Mathematics

Algebra Lineal

Standard Techniques in Data Science

• Classification: We put labels to our objects and we want to classify them, so we will use techniques of supervised learning to do this, like KNN, Naive Bayes, Decision Trees, XGBoost and Neural Networks
• Clustering: If we don't have labels on our objects, we can still classify them. To do this, we cluster the data by measures of similarity. This is call unsupervised learning, like K-means and DBSCAN
• Neural Networks: In summary, this technic is simplify a problem in small pieces that can be processed separately by neurons. Basic frameworks that do this: Keras and Tensorflow, also MNIST.
• Dimensionality Reduction: This will help us to understand better the Data and visualize it. Standar techniques like PCA and SVM
• Visualizing Data: The best way to present your researches and your conclusion is with a nice graph. We will talk about Plotly and Dash