Machine Learning: Day 2

Christian Darvin — Wed, 01 May 2024 14:29:52 +0000

Types of Machine Learning Algorithms

Supervised Learning
Unsupervised Learning
Recommender Systems
Reinforcement Learning

Supervised Learning

Refers to algorithms that learn input-to-output mappings.
Give learning algorithm examples to learn from that include the "output" label for a given input X.
Eventually learns to take just the input alone without the output label and gives a reasonably accurate prediction or guess of the output.
Learns from being given "right answers."

Examples of Supervised Learning

Input (X)	Output (Y)	Application
Email	Spam (0/1)	Spam Filtering
Audio	Text Transcripts	Speech Recognition
English	Spanish, Chinese, etc	Machine Translation
Ad, User Info	User Click (0/1)	Online-Advertisments
Image, Radar Info	Position of other cars	Self-Driving Cars

How does it learn? (Simplified)

Split the data by 80% for training data and 20% for test data.
Train the model with examples of Input (X) and Labels (Y) using the 80% training data.
Use the 20% test data or unseen data, to predict or guess the output.

"Eventually learns to take just the input alone without the output label and gives a reasonably accurate prediction or guess of the output"

Andrew Ng

Types of Supervised Learning Algorithm

1. Regression

It is used to analyze the relationship between the independent variables and dependent variables.
Predict a number from infinitely many possible numbers.
Example: house prices (Y), size of the house (X)
When you see a plot that uses linear regression, the Y always refers to the value we want to predict.

2. Classification

We are trying to predict only a small number of possible outputs or categories.
There are more than two possible outputs.
Class/Category is the term that we use for the output.
Predict categories, usually non-numeric.
Find the boundary line that separates 0 and 1.
Breast Cancer Detection [0: benign, 1: malignant]
Examples: Cat or Dog, Benign or Malignant

Image References

Linear Regression Plot

Classification Plot

Machine Learning: Day 1

Christian Darvin — Sat, 27 Apr 2024 13:38:32 +0000

What is Machine Learning?

Machine Learning is a field of study gives computers the ability to learn without being explicitly programmed.
- Arthur Samuel (1959)

Who is Arthur Samuel?

Arthur Samuel was a Computer Scientist who popularized the term Machine Learning. He developed a program named Samuel Checkers-Playing Program that eventually became better than him at the game.

How did the Samuel Checkers-Playing Program learn?

He programmed the computer to play hundreds or thousands of games against itself. Over time, the program learned by identifying which board positions led to wins and losses, thereby developing an understanding of good and bad moves.

Applications of Machine Learning

Recommender Systems (Netflix, YouTube, Spotify)
Speech Recognition (Amazon Echo, Google Home, Siri)
Spam Email Detection
Healthcare (Medical Diagnosis)

Forem: Christian Darvin