Practical Machine Learning

Instructor: Alejandro Correa Bahnsen

• email: [email protected]
• twitter: @albahnsen
• github: 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
• Seaborn, used mainly for plot styling
• 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 Python	01.1 - Finding digits of Pi, 01.2 - OLS and Numpy
2	Introduction to Machine Learning	02 - Churn Model
3	Pandas Data Frame	03 - Baby names
4	Linear Regression	04 - Bikes Rent
5	Logistic Regression	05 - Titanic
6	Data preparation and Model Evaluation	06 - Titanic V2
7	Kaggle Competitions	07 - Titanic Kaggle Competition
8	Feature Selection	08 - Titanic V3
9	Naive Bayes	09 - Yelp Reviews
10	KNN	10 - NBA Stats
11	Information Retrieval	11 - Mashable
12	Natural Language Processing
13	Decision Trees	13 - Bike Sharing
14		14 - Kaggle Competition - Mashable
15	Unbalance Datasets	15 - Fraud Detection
16	Ensemble Methods - Bagging	16 - MashableV1
17	Ensemble Methods - Bagging cont & Boosting	17 - MashableV2
18	Support Vector Machines	18 - Wine
19	Regularization	19 - Wine V2
20	Cost-Sensitive Classification	20 - Churn
21	Intro Deep Learning
22	Model Deployment