Demystifying
Machine Learning

Why ?

How it works ?

f(a, b) = a + b

Machine Learning is the study of algorithms that infer the function they compute from example data.

Supervised Learning

Logistic Regression

Support Vector Machines

SVM - Kernel Trick

Other supervised learning models

• Decision Trees
• Neural Networks
• Naive Bayes
• Ensemble Learners (Boosting, Random Forest)

Unsupervised learning

K-Nearest Neighbors

Reinforcement learning

Q-Learning

Deep learning

Artificial Neural Networks

Convolutional Neural Networks

Other types of ANNs

• Recurrent Neural Networks
• Long Short Term Memory Cell
• Encoder-decoder
• Generative adversarial networks

Bias and Variance

What it looks like ?

scikit-learn (sklearn)

Tensorflow

Keras

Challenges

• Gather relevant data
• Prepare data
• Domain knowledge
• Feature engineering
• Bias vs Variance trade-off
• Training time

Further reading/studying

• An Introduction to Statistical Learning (free pdf)
• The Elements of Statistical Learning (free pdf)
• Deep Learning Book (free html)
• Machine Learning Engineer Nanodegree @ Udacity
• Andrew Ng's Machine Learning Course @ Coursera

Questions