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

• • Evaluating classification models
• • Evaluating regression models
• • Evaluating clustering models

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

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