Master the theoretical foundation of machine learning by systematically learning statistics from the basics
Series Overview
This series is a practical educational content consisting of 5 chapters that allows you to learn the statistics necessary for machine learning step by step from the basics.
Statistics is an important academic field that forms the theoretical foundation of machine learning. You will systematically learn descriptive statistics that summarizes data characteristics, probability theory that quantifies uncertainty, statistical estimation that infers population properties from data, hypothesis testing that verifies the validity of hypotheses, and Bayesian statistics that utilizes prior knowledge. This knowledge is essential for understanding machine learning algorithms, evaluating models, and quantifying prediction uncertainty. Starting from mean and variance, you will learn probability distributions, estimation and testing, Bayesian statistics, and applications to machine learning with practical Python code examples.
Features:
- ✅ From Basics to Applications: Systematic learning from descriptive statistics to Bayesian statistics
- ✅ Implementation-Focused: 30+ executable Python code examples, utilizing NumPy/SciPy/Matplotlib
- ✅ Visual Understanding: Intuitive understanding through histograms, box plots, and probability distribution visualizations
- ✅ Bridge to Machine Learning: Clear demonstration of how to apply statistical knowledge to machine learning
- ✅ Practical Exercises: Statistical analysis using real data, practice with hypothesis testing
Total Learning Time: 120-150 minutes (including code execution and exercises)
How to Learn
Recommended Learning Order
For Complete Beginners (No statistics knowledge):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (All chapters recommended)
- Time Required: 120-150 minutes
For Intermediate Learners (Experience with basic statistics):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Time Required: 90-110 minutes
For Specific Topic Enhancement:
- Descriptive Statistics/Probability: Chapter 1 (Focused learning)
- Probability Distributions: Chapter 2 (Focused learning)
- Estimation/Testing: Chapter 3 (Focused learning)
- Bayesian Statistics: Chapter 4 (Focused learning)
- Machine Learning Applications: Chapter 5 (Focused learning)
- Time Required: 20-30 minutes/chapter
Chapter Details
Chapter 1: Descriptive Statistics and Probability Basics
Difficulty: Beginner
Reading Time: 20-25 minutes
Code Examples: 8
Learning Content:
- Basic descriptive statistics measures (mean, median, mode, variance, standard deviation)
- Data visualization (histograms, box plots, scatter plots)
- Probability basics (definition and axioms, conditional probability, Bayes' theorem)
- Mathematical definitions and calculations of expected value and variance
- Implementation of statistical calculations and visualizations in Python
Learning Objectives:
- Summarize data characteristics with numerical indicators
- Visualize data with appropriate graphs
- Perform basic probability calculations
- Conduct statistical analysis using NumPy/SciPy/Matplotlib
Chapter 2: Probability Distributions (Coming Soon)
Difficulty: Beginner
Reading Time: 25-30 minutes
Code Examples: 7
Learning Content:
- Discrete probability distributions (Bernoulli distribution, binomial distribution, Poisson distribution)
- Continuous probability distributions (normal distribution, exponential distribution, gamma distribution)
- Properties of normal distribution and the central limit theorem
- Parameter estimation for probability distributions
- Visualization and simulation of probability distributions
Learning Objectives:
- Understand characteristics of major probability distributions
- Select appropriate probability distributions
- Understand the meaning and importance of the central limit theorem
- Manipulate probability distributions using SciPy
Chapter 3: Statistical Estimation and Hypothesis Testing (Coming Soon)
Difficulty: Intermediate
Reading Time: 30-35 minutes
Code Examples: 8
Learning Content:
- Theory of point estimation and interval estimation
- Principles and implementation of maximum likelihood estimation
- Calculation and interpretation of confidence intervals
- Framework of hypothesis testing (null hypothesis, alternative hypothesis, p-value)
- Practice with t-tests, chi-square tests, F-tests
- Multiple testing problem and Bonferroni correction
Learning Objectives:
- Understand principles of statistical estimation
- Correctly interpret confidence intervals
- Select appropriate hypothesis testing methods
- Correctly understand the meaning of p-values
Chapter 4: Introduction to Bayesian Statistics (Coming Soon)
Difficulty: Intermediate-Advanced
Reading Time: 25-30 minutes
Code Examples: 6
Learning Content:
- Deep understanding of Bayes' theorem
- Relationship between prior distribution, likelihood, and posterior distribution
- Use of conjugate prior distributions
- Implementation of Bayesian estimation
- Introduction to Markov Chain Monte Carlo (MCMC) methods
- Comparison of Bayesian statistics and frequentist statistics
Learning Objectives:
- Understand the concept of Bayesian statistics
- Incorporate prior knowledge into statistical inference
- Implement Bayesian estimation
- Understand applications of Bayesian statistics to machine learning
Chapter 5: Applications to Machine Learning (Coming Soon)
Difficulty: Intermediate
Reading Time: 25-30 minutes
Code Examples: 7
Learning Content:
- Statistical interpretation of linear regression and least squares
- Logistic regression and maximum likelihood estimation
- Implementation of Naive Bayes classifier
- Prediction uncertainty estimation with Gaussian processes
- Model evaluation and statistical testing
- Statistical methods for A/B testing
Learning Objectives:
- Understand statistical foundations of machine learning algorithms
- Apply statistical knowledge to machine learning
- Quantify prediction uncertainty
- Perform statistical model evaluation
Prerequisites
Mathematical Knowledge
- High School Mathematics - Algebra, functions, basics of calculus
- Sigma Notation - Summation notation
- Exponential/Logarithmic Functions - Basic properties and calculations
Programming Skills
- Python Basics - Variables, functions, control structures
- NumPy Basics - Array manipulation, numerical computation
- Matplotlib Basics - Basic graph drawing
Recommended Prior Learning
- 📚 Python Programming Introduction (In preparation)
- 📚 NumPy/SciPy Introduction (In preparation)
- 📚 Data Visualization Introduction (In preparation)
Required Environment
Python Libraries
- NumPy 1.24+ - Numerical computation and array manipulation
- SciPy 1.10+ - Statistical functions and probability distributions
- Matplotlib 3.7+ - Data visualization
- pandas 2.0+ - Data manipulation (optional)
- seaborn 0.12+ - Statistical visualization (optional)
Development Environment
- Python 3.8+ - Programming language
- Jupyter Notebook / Lab - Interactive development environment
- Google Colab - Browser-based execution environment (free)
Installation Method
# Batch installation using pip
pip install numpy scipy matplotlib pandas seaborn jupyter
# If using conda
conda install numpy scipy matplotlib pandas seaborn jupyterLet's Get Started!
Are you ready? Start with Chapter 1 and master the basics of statistics!
Chapter 1: Descriptive Statistics and Probability Basics →
Next Steps
After completing this series, we recommend proceeding to the following topics:
Deep Dive Learning
- 📚 Multivariate Analysis: Principal component analysis, factor analysis, discriminant analysis
- 📚 Time Series Analysis: ARIMA, state space models, forecasting methods
- 📚 Causal Inference: Experimental design, propensity scores, causal effect estimation
- 📚 Nonparametric Statistics: Kernel density estimation, rank tests
Related Series
- 🎯 Mathematics for Machine Learning Introduction - Linear algebra, calculus
- 🎯 Supervised Learning Introduction - Regression, classification algorithms
- 🎯 Data Science Practice (In preparation) - Real data analysis projects
- 🎯 Probabilistic Machine Learning (In preparation) - Bayesian machine learning, probabilistic modeling
Practical Projects
- 🚀 A/B Test Analysis - Statistical evaluation of website improvements
- 🚀 Quality Control System - Implementation of statistical process control
- 🚀 Risk Analysis Tool - Financial risk assessment using probability distributions
- 🚀 Experimental Data Analysis - Statistical analysis of scientific experimental data
Update History
- 2025-12-01: v1.0 Initial release
Your statistics learning journey starts here!