Thursday, November 25, 2021
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Bias – Variance Tradeoff

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Day 5 of #66DaysofData.com: Bias – Variance Tradeoff.

1Videos

Sensitivity & Specificity

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Day 4 of #66DaysofData.com.

I watched videos on Sensitivity & Specificity.

Sensitivity = TP / (TP + FN)

Specificity: TN / (TN + FP)

1Videos

Confusion Matrix

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Day 3 of #66DaysofData.com.

I watched 2 Confusion Matrix videos: One on StatQuest and the other on Udemy(Machine Learning A-Z™: Hands-On Python & R In Data Science).

False Positive (Type I Error)
False Negative (Type II Error)

Accuracy Paradox: Should not base our judgment just on accuracy rate.

1Video

 

Cross Validation

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Day 2 of #66DaysofData.com: Cross Validation. I watched 2/69 videos in the playlist “Machine Learning” on Youtube’s StatQuest.

1Video

 

Intro to Machine Learning

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Day 1 of Ken Jee’s #66DaysofData.com: Intro to Machine Learning. I watched the 1st/69 videos in the playlist “Machine Learning” on Youtube’s StatQuest with Josh Starmer.

1What is Machine Learning?

“Field of study that gives computers the ability to learn without being explicitly programmed” ~ Arthur Samuel, 1959

“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” ~ Tom Mitchell, 1998

Machine Learning learns patterns from existing data and applies it to new data.

2Applications of Machine Learning:

Machine learning is used in Image Recognition, Speech Recognition, Medical Diagnosis, Traffic prediction, Product recommendations, Self-driving cars, Email Spam and Malware Filtering, Virtual Personal Assistant, Online Fraud Detection, Stock Market trading, Automatic Language Translation, etc.

3Types of Machine Learning Algorithms

  • Supervised machine learning algorithms
  • Unsupervised machine learning algorithms
  • Semi-supervised machine learning algorithms
  • Reinforcement machine learning algorithms

4Code

A simple machine learning code from TeamTreeHouse.

5Videos

StatQuest: A Gentle Introduction to Machine Learning

 

Data Models

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The following info is from Chapter 2 of Database Systems by Carlos Coronel

1Basic Building Blocks

Entity: A person, place, thing, concept or event for which data can be stored.

Attribute: A characteristic of an entity or object.

Relationship: An association between entities.

One-to-many (1:M or 1..*) relationship: Associations among two or more entities that are used by data models.
In a 1:M relationship, one entity instance is associated with many instances of the related entity.
A painter creates many different paintings, but each is painted by only one painter.
“PAINTER paints PAINTING”

Many-to-many (M:N or *..*) relationship: Association among two or more entities in which one occurrence of an entity is associated with many occurrences of a related entity and one occurrence of the related entity is associated with many occurrences of the first entity.
An employee may learn many job skills, and each job skill may be learned by many employees.
“EMPLOYEE learns SKILL”

One-to-one (1:1 1..1) relationship: Associations among two or more entities that are used by data models.
“EMPLOYEE manages STORE”

Constraint: A restriction placed on data.


2Business Rules

Business rule: A description of a policy, procedure, or principle within an organization.

3Resources

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