Machine Learning In JavaScript

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Machine Learning (ML)

Machine Learning is often considered equivalent with Artifical Intelligence.

This is not correct. Machine learning is a subset of Artificial Intelligence.

Machine Learning is a disipline of AI that uses data to perform supervised or unsupervised machine training.

What is Machine Learning?

ML systems combines Input to produce Predictions.

Key terminologies are:

• Labels
• Features
• Models
• Training
• Inference
• Functions

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Machine Learning Labels

In Machine Learning terminology, the label is the thing we want to predict.

It is like the y in a linear graph:

y = ax + b

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Machine Learning Features

In Machine Learning terminology, the features are the input.

They are like the x values in a linear graph:

y = ax + b

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Machine Learning Models

A Model defines the relationship between the label (y) and the features (x).

There are three phases in the life of a model:

• Data Collection
• Training
• Inference

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Data Collection

Machine Learning can teach a computer to solve many questions like:

• Is this cancer?
• Is this a banana?

Before Machine Learning can start, you need to collect some data.

If you want to predict house prices, you need to collect some information about house prices.

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Machine Learning Training

The goal of training is to create a model that can answer our question. Like what is the expected price for a house?

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Machine Learning Inference

Inference is when the trained model is used to infer (predict) values using live data. Like putting the model into production.

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Using a Linear Regression Function

This Model predicts prices using a linear regression function:

Example

# Name the Axis
plt.title('House Prices vs Size')
plt.xlabel('Square Meters')
plt.ylabel('Price in Millions')

# Set x and y values
x = np.array([50,60,70,80,90,100,110,120,130,140,150,160])
y = np.array([7,8,8,9,9,9,9,10,11,14,14,15])

# Call Linear Regression Function
slope, intercept, r, p, std_err = stats.linregress(x, y)

# Plot Data
plt.scatter(x, y)
plt.plot(x, slope * x + intercept)
plt.show()

Try it Yourself »

In the example above, the slope and intercept is calculated by a function called linregress.

From Previous Chapter

A linear relationship is written as y = ax + b

Where:

• y is the price we want to predict
• a is the slope of the line
• x are the input values
• b is the intercept

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With Machine Learning

With ML, a linear relationship is written as y = b + wx

Where:

• y is the label we want to predict
• w is the weight (the slope)
• x are the features (input values)
• b is the intercept

Sometimes there can be many features (input values) with different weights:

y = b + w1x1 + w2x2 + w3x3 + w4x4

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https://web.archive.org/web/20210220012931/https://www.w3schools.com/ai/ai_machine_learning.asp

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