Probability, Statistics & ML Almanack

Probability, Statistics & Machine Learning Almanack

PROJECTS · STATISTICS, ML & AI

Probability, Statistics, Machine Learning, AI

250h

Jan. 2022 - Present

INTRO

Project Description

This GitHub repository serves as a non-curated collection of probability, statistics, and machine learning exercises and applications, primarily implemented in Python. The content spans a wide range of topics, including Monte Carlo simulations, stochastic processes, financial risk analysis, hypothesis testing, regression models, and clustering techniques. These projects, gathered from various explorations over time, offer a hands-on approach to fundamental and advanced concepts in data analysis and statistical modeling.

SKILLS

Fields of Expertise

Probability Theory (Monte Carlo Analysis, Random Variables and Processes, Stochastic Analysis…):

Statistics (Descriptive, Inferential, Hypothesis Testing, Regression, Time-Series Analysis, Outlier Detection…)

Machine Learning (Regression, Classification, Clustering, Dimensionality Reduction…):

Coding (Python, R):

· · ·

Complexity: ★★★☆☆

Tools: Python, R

See Project in GitHub

Project Type: Personal Project

01 INTRO

CHAPTER

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Project Motivation

Over time, I have worked on numerous probability, statistics, and machine learning problems, each addressing specific theoretical or practical aspects of data analysis. While individually these projects may not justify a dedicated project or repository, collectively they form a valuable compilation of diverse methodologies and applications.

This repository aims to centralize and share these explorations, covering topics such as Monte Carlo probabilistic analysis, stochastic process modeling, risk-return assessments, and outlier detection in financial time series. It includes both theoretical exercises, such as hypothesis testing and head-run analysis, and real-world applications like Bitcoin market exploration and Sharpe ratio analysis.

By gathering these works in one place, the goal is to provide a useful reference for students, researchers, and practitioners interested in applied probability, statistics, and machine learning, while also reflecting a deep understanding of these disciplines.