# The Data Frog beginner

## Matplotlib for Machine Learning

Learn the basics of matplotlib in 1h. You'll make your first plots with a toy machine learning example.

## Numpy Crash Course for Machine Learning

Learn the minimum numpy needed to get started with machine learning (1h course)

## Python Crash Course for Machine Learning

You want to get started with machine learning but you don't know python? You're at the right place! (1h course)

## Data pipeline with Docker, InfluxDB, and Grafana

In this beginner's guide, you will learn how to set up a typical data pipeline as may be used in experimental science and IOT (Internet of things), with modern tools: Docker, InfluxDB, and Grafana.

## Fighting Bugs in Python

Everybody writes bugs. Let's see how to make sure they don't live long with debugging and unit tests. Introduction to pdb and unittest and a few advices for easy and reliable development.

## Overfitting Illustrated

Overfitting is one of the most important issues in machine learning. Here, it's illustrated in a small 2D classification problem.

## Logistic Regression vs Neural Network: Non Linearities

Back to the logistic regression, aka the 1-neuron network! Here we see that it works well in 2D as well for linear problems. We also find out that it can be necessary to introduce non linearity by adding hidden layers

## The 1-Neuron Network: Logistic Regression

So far we've used neural networks as a black box. Today, we're opening the box. To keep it easy, we'll do it for a very, very simple neural network, with a single neuron.

## First Neural Network with Keras

Keras is the easiest and most powerful way to deal with neural networks in python. Give it a try by training your first neural net to classify handwritten digits

## Handwritten Digit Recognition with scikit-learn

In this tutorial, we apply supervised machine learning to train a neural network to classify images of handwritten digits into the following categories: 0, 1, 2, ... , 9. If you're just getting started with machine learning, this image recognition tutorial is for you!

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