Machine Learning Tutorial for Starter-ups

1. Machine Learning learning map created by scikit-learn.
Often the hardest part of solving a machine learning problem can be finding the right estimator for the job.
Different estimators are better suited for different types of data and different problems.
The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data.
Click on any estimator in the chart below to see its documentation.

2. Applied Machine Learning for Security Informatics by albahnsen
Requiriments

Python version 3.5;
Numpy, the core numerical extensions for linear algebra and multidimensional arrays;
Scipy, additional libraries for scientific programming;
Matplotlib, excellent plotting and graphing libraries;
IPython, with the additional libraries required for the notebook interface.
Pandas, Python version of R dataframe
scikit-learn, Machine learning library!
A good, easy to install option that supports Mac, Windows, and Linux, and that has all of these packages (and much more) is the Anaconda.

GIT!! Unfortunatelly out of the scope of this class, but please take a look at these tutorials

Sessions

Session Notebook link Exercises
1 Introduction to Machine Learning
2 Introduction to Python 01 - Python & Numpy
3 Pandas Data Frame 03 - Baby names
4 Linear Regression 04 - Bikes Rent
5 Logistic Regression 05 - Intrusion Detection
6 Data Preparation and evaluation Intrusion Detection
7 Feature Selection Intrusion Detection
8 Decision Trees Fraud Detection
9 Ensemble Methods - Bagging Bagging
10 Ensemble Methods - Boosting
11 Support Vector Machines Phishing
12 Deep Learning
13 Model Deployment
14 Kaggle Competition

3. Awesome Machine Learning for Cyber Security
Table of Contents

Datasets
Papers
Books
Talks
Tutorials
Courses
Miscellaneous