# Machine Learning 101

## My Journey into Machine Learning

Hey there! 👋

I want to share what I've learned about machine learning from building my own projects and experimenting with real datasets. When I first started, the math felt overwhelming and the terminology was confusing. But once I wrote my first model that actually predicted something correctly, everything clicked.

**Machine learning isn't magic** — it's pattern recognition backed by mathematics and implemented with code.

## What You'll Learn

This series covers everything I wish someone had explained when I started learning ML:

### Foundations

* **What is Machine Learning?**: Core concepts, types of ML, and when to use what
* **Data Preprocessing**: Cleaning, transforming, and preparing data for models
* **Feature Engineering**: Creating meaningful inputs that help models learn

### Core Algorithms

* **Supervised Learning**: Regression, classification, decision trees, random forests
* **Unsupervised Learning**: Clustering, dimensionality reduction, anomaly detection

### Production Skills

* **Model Evaluation**: Metrics, cross-validation, hyperparameter tuning
* **Neural Networks**: Introduction to deep learning fundamentals
* **End-to-End Project**: Full ML pipeline from data to deployment

## Tech Stack

All examples use Python with these libraries:

```txt
# requirements.txt
numpy>=1.26.0
pandas>=2.1.0
scikit-learn>=1.4.0
matplotlib>=3.8.0
seaborn>=0.13.0
jupyter>=1.0.0
```

Setup:

```bash
python3.12 -m venv ml-env
source ml-env/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
```

## Prerequisites

To get the most out of this guide:

* Basic Python knowledge (variables, functions, loops, classes)
* Familiarity with NumPy arrays and basic Pandas DataFrames
* High school math (algebra, basic statistics — I'll explain the rest)

Don't worry if your math is rusty — I focus on intuition first, formulas second.

## What Makes This Guide Different

Everything here comes from:

* Personal projects where I built and trained my own models
* Hands-on experimentation with real datasets
* Mistakes I made and debugging sessions that taught me more than any course
* Practical knowledge from writing Python ML code daily

No "imagine you work at Company X" scenarios — just what I've learned from building things myself.

## The Articles

| Part | Title                                                                                                                                             | What You'll Build                       |
| ---- | ------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
| 1    | [What is Machine Learning?](https://blog.htunnthuthu.com/ai-and-machine-learning/machine-learning-101/part-1-what-is-machine-learning)            | Your first ML model in 10 lines         |
| 2    | [Data Preprocessing and Feature Engineering](https://blog.htunnthuthu.com/ai-and-machine-learning/machine-learning-101/part-2-data-preprocessing) | Data cleaning pipeline                  |
| 3    | [Supervised Learning](https://blog.htunnthuthu.com/ai-and-machine-learning/machine-learning-101/part-3-supervised-learning)                       | Classification and regression models    |
| 4    | [Unsupervised Learning](https://blog.htunnthuthu.com/ai-and-machine-learning/machine-learning-101/part-4-unsupervised-learning)                   | Clustering and dimensionality reduction |
| 5    | [Model Evaluation and Selection](https://blog.htunnthuthu.com/ai-and-machine-learning/machine-learning-101/part-5-model-evaluation)               | Cross-validation and tuning pipeline    |
| 6    | [Neural Networks Introduction](https://blog.htunnthuthu.com/ai-and-machine-learning/machine-learning-101/part-6-neural-networks-intro)            | Simple neural network from scratch      |
| 7    | [End-to-End ML Project](https://blog.htunnthuthu.com/ai-and-machine-learning/machine-learning-101/part-7-end-to-end-project)                      | Complete ML pipeline with deployment    |

## The Path Forward

Machine learning is a skill built through practice. Each article includes runnable Python code you can experiment with on your own machine. I encourage you to modify the examples, try different parameters, and break things — that's how I learned the most.

Let's start building.

***

**Navigation:**

* Start with [What is Machine Learning?](https://blog.htunnthuthu.com/ai-and-machine-learning/machine-learning-101/part-1-what-is-machine-learning) for core concepts
* Jump to [Supervised Learning](https://blog.htunnthuthu.com/ai-and-machine-learning/machine-learning-101/part-3-supervised-learning) if you already know the basics
* See the [End-to-End Project](https://blog.htunnthuthu.com/ai-and-machine-learning/machine-learning-101/part-7-end-to-end-project) for the complete picture