## Code notes & cookbook

### Python machine learning and data science notes

#### pre-basic concepts

- • High level introduction about probability distributions with Python
- • Introduction to Hypothesis Testing with Python

#### Basic concepts

- • Simple explanation of the tokenization process
- • Getting started with machine learning models
- • Getting started with clustering (unsupervised learning) in Python
- • Getting started with regression (supervised learning) in Phython
- • Getting started with classification (supervised learning) in Python
- • Key concepts of ensemble methods in ML
- • Understanding the basics of ROC curves

#### Results evaluation

#### Deep Learning

- • A simple Deep Learning overview
- • Convolutional Neural Networks (CNNs) made simple
- • Recurrent Neural Networks (RNNs) made simple

#### Specific models introduction concepts

- • Exploring K-means algorithm basic concepts
- • Exploring Hierarchical Agglomerative Clustering (HAC) basics
- • Exploring Density-based (DBSCAN) ML algorithm basic concepts
- • Exploring k-medoids (PAM) algorithm basic concepts
- • Exploring the KNN classifier algorithm basic concepts
- • Explore Support vector machines (SVMs) algorithm basic concepts