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Author: gozsari

README.md

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-# ML_Intro
-This is a one-day machine learning introductory course for beginners
+
+
+# Introduction to Machine Learning: One-Day Course 
+This is a one-day machine learning introductory course for beginners. The course covers the basics of supervised and unsupervised learning, including regression, classification, clustering, dimensinality reduction and anomaly detection. It also includes hands-on exercises and examples using popular Machine Learning (ML) libraries like Scikit-learn.
+
+The [slides](presentation/ML_intro.pdf) are used to guide the instructor through the course, providing a structured outline of the topics to be covered. 
+
+## Table of Contents
+1. [Introduction to Machine Learning](#1-introduction-to-machine-learning)
+2. [Understanding the Machine Learning Workflow](#2-understanding-the-machine-learning-workflow)
+3. [Supervised Learning](#3-supervised-learning)
+   - [3.1 Regression](#31-regression)
+   - [3.2 Classification](#32-classification)
+4. [Unsupervised Learning](#4-unsupervised-learning)
+   - [4.1 Clustering](#41-clustering)
+   - [4.2 Other Unsupervised Learning Techniques](#42-other-unsupervised-learning-techniques)
+5. [In-Class Assignment](#5-in-class-assignment)
+
+---
+
+## 1. Introduction to Machine Learning
+- **What is AI and ML?**
+  - AI: The ability of machines to simulate intelligent behavior.
+  - ML: A branch of AI where models are trained to learn from data and improve over time.
+- **Applications**:
+  - ChatGPT, Netflix recommendations, fraud detection, self-driving cars, etc.
+- **Types of ML**:
+  - Supervised Learning, Unsupervised Learning, Reinforcement Learning.
+- **Key Terminology**:
+  - Dataset, Features, Labels, Model, Training, Testing, Hyperparameters, Overfitting, Underfitting.
+
+### Highlights
+- Comparison between supervised and unsupervised learning using Linear Regression and K-Means examples.
+- Basic visualizations of regression and clustering tasks.
+
+
+---
+
+## 2. Understanding the Machine Learning Workflow
+
+### Steps in the Workflow
+1. **Define the Problem**: Set objectives (e.g., regression, classification).
+2. **Collect and Clean Data**: Handle missing values, duplicates, outliers.
+3. **Explore and Visualize Data**: Use summary statistics and visual tools like histograms and scatterplots.
+4. **Feature Engineering**:
+   - **Selection**: Remove irrelevant features.
+   - **Transformation**: Normalize and encode data.
+   - **Creation**: Generate new features.
+5. **Split Data**: Divide into training, validation, and test sets.
+6. **Choose and Train a Model**: Select an algorithm based on the task.
+7. **Evaluate the Model**: Use metrics like RMSE, Accuracy, and Silhouette Score.
+8. **Hyperparameter Optimization**: Use `GridSearchCV` or `RandomizedSearchCV` for fine-tuning.
+
+### Highlights
+- End-to-end example of an ML pipeline using Scikit-learn.
+- Visualization of preprocessing and evaluation results.
+
+---
+
+## 3. Supervised Learning
+
+### 3.1 Regression
+
+- **Goal**: Predict continuous outputs (e.g., house prices, temperature).
+- **Common Algorithms**:
+  - Linear Regression, Polynomial Regression, Ridge, and Lasso.
+- **Evaluation Metrics**:
+  - MAE, MSE, RMSE, \( R^2 \).
+
+#### Highlights
+- Hands-on example of Linear Regression with visualization of results.
+- Analysis of regression coefficients.
+
+---
+
+### 3.2 Classification
+
+- **Goal**: Predict discrete categories (e.g., spam detection, disease diagnosis).
+- **Types**:
+  - Binary, Multi-Class, Multi-Label Classification.
+- **Evaluation Metrics**:
+  - Accuracy, Precision, Recall, F1-Score, Confusion Matrix.
+
+#### Highlights
+- Logistic Regression example for binary classification.
+- Hands-on exercise with Random Forest Classifier.
+- Visualization of confusion matrix results.
+
+---
+
+## 4. Unsupervised Learning
+
+### 4.1 Clustering
+
+- **Goal**: Group data points into clusters based on similarity without labels.
+- **Types**:
+  - Partition-Based: K-Means.
+  - Hierarchical: Agglomerative Clustering.
+  - Density-Based: DBSCAN.
+- **Evaluation Metrics**:
+  - Silhouette Score, Inertia, Visualization.
+
+#### Highlights
+- K-Means Clustering example with synthetic data.
+- Visualizing clusters and centroids.
+
+---
+
+### 4.2 Other Unsupervised Learning Techniques
+
+- **Dimensionality Reduction**:
+  - Reduces input features while preserving patterns.
+  - Example: PCA (Principal Component Analysis).
+- **Anomaly Detection**:
+  - Identifies outliers or unusual patterns.
+  - Examples: Isolation Forest, Z-scores.
+- **Association Rule Mining**:
+  - Finds relationships between items (e.g., market basket analysis).
+
+#### Highlights
+- PCA visualization of high-dimensional data projected into 2D.
+- Hands-on example of Isolation Forest for anomaly detection.
+- Apriori algorithm for discovering association rules.
+
+---
+
+## 5. In-Class Assignment
+
+- **Objective**:
+  - Develop a classification model using a dataset of your choice.
+  - Save the model as a pickle file.
+- **Steps**:
+  - Preprocess the data (handle missing values, encode categorical variables).
+  - Train, evaluate and optimize the model.
+  - Submit the pickle file of the trained model.
+
+---
+
+### Usage
+This ReadMe serves as:
+1. A reference for instructors to organize class materials.
+2. A guide for students reviewing ML concepts and techniques.
+3. A roadmap for practical ML workflows.
+
+
+
+### Acknowledgements
+Thanks to [**Leon Boschman**](https://github.com/lboschman) for contributing his ideas, slides and feedback to this course material.