Machine Learning Demo is a practical machine learning tool that runs in your browser. Experience fundamental algorithms like linear regression and clustering without programming. This guide covers everything from usage to practical applications.

Table of Contents

1. What is Machine Learning Demo?
2. Key Features and Benefits
3. Step-by-Step Usage Guide
4. Practical Use Cases
5. Frequently Asked Questions (FAQ)
6. Machine Learning Fundamentals
7. Related Tools
8. Summary

1. What is Machine Learning Demo?

Machine Learning Demo is a practical machine learning tool running in your browser. It uses ONNX Runtime for fast inference.

Why Choose This Tool

What is Machine Learning?

Machine Learning (ML) is an AI technology that learns patterns from data to make predictions and classifications.

Main Categories:

• Supervised Learning: Learn from labeled data (regression, classification)
• Unsupervised Learning: Automatically discover patterns (clustering)
• Reinforcement Learning: Learn optimal actions through trial and error

2. Key Features and Benefits

Basic Features

• Linear Regression: Fundamental algorithm for numerical prediction
• K-means Clustering: Data grouping
• Data Visualization: Graph display of learning results
• Real-time Learning: Instant model updates

Linear Regression Features

Applications:

• Sales forecasting
• Price estimation
• Trend analysis
• Demand prediction

K-means Clustering Features

Applications:

• Customer segmentation
• Anomaly detection
• Data summarization
• Pattern discovery

Visualization Features

3. Step-by-Step Usage Guide

Step 1: Access the Tool

Navigate to the Machine Learning Demo page.

Step 2: Select Algorithm

Choose the algorithm to use.

Options:

1. Linear Regression: For numerical prediction
2. K-means Clustering: For data grouping

Step 3: Input Data

Enter training data.

Linear Regression Input Example:

[
  [1, 2],
  [2, 4],
  [3, 6],
  [4, 8],
  [5, 10]
]

K-means Input Example:

[
  [1.0, 2.0],
  [1.5, 1.8],
  [5.0, 8.0],
  [8.0, 8.0],
  [1.0, 0.6]
]

Step 4: Adjust Parameters

Configure algorithm parameters.

Step 5: Train Model

Click the "Train" button to train the model.

Training Process:

1. Data normalization
2. Initialize parameters
3. Iterative optimization
4. Convergence determination

Step 6: Make Predictions

Use the trained model to predict new data.

Prediction Input Example (Linear Regression):

6  // Predict y when x = 6

Expected Output:

12  // Predicted as y = 12

4. Practical Use Cases

Case 1: Sales Forecasting Model Training

Scenario: Monthly sales forecasting for retail store Data: Sales data from past 12 months Algorithm: Linear Regression Result: Successfully predicted next month's sales within 10% error

Training Data Example:

// [month, sales (in 10,000 yen)]
[[1, 100], [2, 110], [3, 105], [4, 120], [5, 130]]

Case 2: Customer Segmentation

Scenario: E-commerce customer analysis Data: Purchase amount and frequency Algorithm: K-means Clustering Result: Identified 3 customer groups and optimized marketing strategy

Cluster Interpretation:

• Cluster 1: High amount, low frequency (VIP customers)
• Cluster 2: Medium amount, high frequency (Regular customers)
• Cluster 3: Low amount, low frequency (New customers)

Case 3: Real Estate Price Prediction

Scenario: Apartment price estimation Data: Relationship between area and price Algorithm: Linear Regression Result: Accurately predicted prices from area

Case 4: Data Analysis Education

Scenario: University statistics introductory course Usage: Visual learning of machine learning fundamentals Result: Improved student understanding and practical skills

Case 5: Anomaly Detection

Scenario: Manufacturing line quality control Data: Sensor data clustering Algorithm: K-means Result: Automated detection of anomalies deviating from normal data

5. Frequently Asked Questions (FAQ)

Q: Can I use it without machine learning experience?

A: Yes, it operates visually without programming. Perfect for beginners.

Q: How much data is needed?

A: Linear regression requires minimum 3 points, K-means requires at least as many points as clusters. 10-100 points is appropriate for demos.

Q: Is data sent externally?

A: No, all processing is completed in your browser with no external data transmission.

Q: Can it be used for actual business?

A: This is a demo of basic algorithms. Professional tools are recommended for serious business use.

Q: Can I save the model?

A: Save functionality is not currently implemented, but learning parameters can be recorded.

Q: Which browsers are supported?

A: Works on modern browsers that support WebAssembly: Chrome, Firefox, Edge, and Safari.

Q: Can it handle large datasets?

A: Depends on browser memory, but can handle up to several thousand entries without issues.

Q: Can I use it on mobile?

A: Yes, works on smartphones and tablets, but PC use is recommended due to screen size.

Q: Will other algorithms be added?

A: Yes, we are considering adding neural networks, decision trees, and support vector machines.

Q: What is ONNX Runtime?

A: An open-source machine learning inference engine developed by Microsoft. Enables fast and efficient inference.

6. Machine Learning Fundamentals

Types of Machine Learning

Linear Regression Formula

Linear regression is modeled by the following formula:

y = wx + b

w: Weight (slope)
b: Bias (intercept)
x: Input value
y: Predicted value

K-means Clustering Algorithm

Steps:

1. Randomly initialize K cluster centers
2. Assign each data point to nearest center
3. Recalculate center of each cluster
4. Repeat steps 2-3 until convergence

Benefits of Browser ML

7. Related Tools

8. Summary

This guide has covered the comprehensive usage of the Machine Learning Demo tool.

Key Takeaways:

• ✅ Practical machine learning tool running in your browser
• ✅ Visual learning without programming
• ✅ Supports Linear Regression and K-means Clustering
• ✅ Real-time training and prediction
• ✅ Perfect for data science education

Ideal for those who want to learn machine learning fundamentals or perform simple data analysis. Try it now!

Related Articles:

• Introduction to Data Science
• Statistics Fundamentals
• Difference Between AI and Machine Learning

Tags: #MachineLearning #ML #DataScience #BrowserML #ONNX #LinearRegression #Kmeans #i4u

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Security and Privacy

All processing is done within your browser, and no data is sent externally. You can safely use it with personal or confidential information.

Troubleshooting

Common Issues

• Not working: Clear browser cache and reload
• Slow processing: Check file size (recommended under 20MB)
• Unexpected results: Verify input format and settings

If issues persist, update your browser to the latest version or try a different browser.