An Extremely Brief Intro to Machine Learning in Python

Ha Khanh Nguyen (hknguyen)


1. What is Machine Learning?


2. Machine Learning Tasks

2.1 Supervised Learning

2.1.1 Regression

x1 x2 x3 y
A -0.66 0.48 14.09
A 1.55 0.97 2.92
A -1.19 -0.81 15.00
A 0.15 0.28 9.29
B -1.09 -0.16 17.57
B 1.61 1.94 2.12
B 0.04 1.72 8.92
A 1.31 0.36 4.40
C 0.98 0.30 4.40
C 0.88 -0.39 4.52

2.1.2 Classification

x1 x2 x3 y
Q -0.66 0.48 B
Q 1.55 0.97 C
Q -1.19 -0.81 B
Q 0.15 0.28 A
P -1.09 -0.16 B
P 1.61 1.94 B
P 0.04 1.72 C
P 1.31 0.36 C
Q 0.98 0.30 B
P 0.88 -0.39 B

2.2 Unsupervised Learning

2.2.1 Clustering

2.2.2 Density Estimation


3. Evaluating the Functions/Models


4. First Application: Classifying Iris Species

4.1 Setting up your system for ML

conda install scikit-learn

4.2 Take a look at the data

4.3 Building your first model: k-Nearest Neighbors

4.4 Making predictions

4.5 Evaluating the model


References: