Wednesday, January 27, 2021
Home Blog

StatQuest: Machine Learning Playlist #66DaysOfData

0

19Day 24 of #66DaysofData (01/24/21):

I watched the Youtube Video, StatQuest: PCA Practical Tips

18Day 24 of #66DaysofData (01/24/21):

I watched the Youtube Video, StatQuest: PCA main ideas in only 5 minutes!!!

17Day 23 of #66DaysofData (01/23/21):

I watched the Youtube Video, StatQuest: Principal Component Analysis (PCA), Step-by-Step

16Day 23 of #66DaysofData (01/23/21):

I watched the Youtube Video, StatQuest: Regularization Part 3: Elastic Net Regression

15Day 22 of #66DaysofData (01/22/21):

I watched the Youtube Video, StatQuest: Ridge vs Lasso Regression, Visualized!!!

14Day 21 of #66DaysofData (01/21/21):

I watched the Youtube Video, StatQuest: Regularization Part 2: Lasso (L1) Regression

13Day 20 of #66DaysofData (01/20/21):

I watched the Youtube Video, StatQuest: Regularization Part 1: Ridge (L2) Regression

12Day 19 of #66DaysofData (01/19/21):

I watched the Youtube Video, StatQuest: Deviance Residuals

11Day 18 of #66DaysofData (01/18/21):

I watched the Youtube Video, StatQuest: Saturated Models and Deviance

10Day 17 of #66DaysofData (01/17/21):

I watched the Youtube Video, StatQuest: Logistic Regression Details Pt 3: R-squared and p-value

 

9Day 16 of #66DaysofData (01/16/21):

I watched the Youtube Video, StatQuest: Logistic Regression Details Pt 2: Maximum Likelihood

8Day 15 of #66DaysofData (01/15/21):

I watched the Youtube Video, StatQuest: Logistic Regression Details Pt1: Coefficients

7Day 14 of #66DaysofData (01/14/21):

I watched the Youtube Video, StatQuest: Logistic Regression

6Day 13 of #66DaysofData (01/13/21):

I watched the Youtube Video, StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!!

 

5For Day 12 of #66DaysofData (01/12/21):

I watched the Youtube Video, StatQuest: Odds and Log(Odds).

 

4Day 4 of #66DaysofData:

I watched the Youtube Video, StatQuest: Machine Learning Fundamentals: Sensitivity and Specificity

3Day 3 of #66DaysofData:

I watched the Youtube Video, StatQuest: Machine Learning Fundamentals: The Confusion Matrix

2Day 2 of #66DaysofData:

I watched the Youtube Video, StatQuest: Machine Learning Fundamentals: Cross Validation

1For Day 1 of #66DaysofData:

I watched the Youtube Video, StatQuest: A Gentle Introduction to Machine Learning

 

StatQuest: Linear Models Pt.1 – Linear Regression

0

For Day 11 of #66DaysofData:

I watched the Youtube Video, StatQuest: Linear Models Pt.1 – Linear Regression.

 

Hierarchical and Network Models

0

For Day 10 of #66DaysofData:

I was reading about the Evolution of Data Models in the book DataBase Systems: Design, implementation, & Management by Carlos Coronel.

More specifically I read about the second generation (1960’s – 1970’s) models: Hierarchical and Network Models.

  • Hierarchical Model: An early database model whose basic concepts and characteristics formed the basis for subsequent database development. This model is based on an upside-down structure in which each record is called a segment. The top record is the root segment. Each Segment has a 1:M relationship to the segment directly below it. 
  • Segment: In the hierarchical data model, the equivalent of a file system’s record type.
  • Network model: An early data model that represented data as a collection of record types in 1:M relationships.
  • Schema: A logical grouping of database objects, such as tables, indexes, views and queries, that are related to each other. The conceptual organization of the entire database as viewed by the database administrator.
  • Subschema: The portion of the database that interacts with application programs. 
  • Data Manipulation Language ( DML): The set of commands that allows an end user to manipulate the data in the database, such as SELECT, INSERT, UPDATE, DELETE, COMMIT, AND ROLLBACK.
  • Data Definition Language (DDL): The language that allows a database administrator to define the database structure, schema, and subschema. 

 

In the 1980’s the relational data model replaced the hierarchical and network models because of its disadvantages and the lack of ad hoc query capabilities. 

 

Intro to Linear Modeling

0

Day 8, Day 9 of #66DaysofData: Datacamp – Introduction to Linear Modeling in Python.

1Exploring Linear Trends – (Day 8)

Interpolation is a model prediction for determining points between “inside” two known data points.
Extrapolation is a model prediction for estimating data points that are outside the range of the known data points.

y = mx + b

dy = (y2 – y1)
dx = (x2 – x1)
m = slope = rise-over-run = dy/dx

b = y-intercept

Variance measures how a single variable varies.
Covariance measures how two variables “vary together”.

2Building Linear Models – (Day 9)

Taylor Series.

Watched Model Optimization Video

Least Squares

0

Day 7 of #66DaysofData.

“The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems (sets of equations in which there are more equations than unknowns) by minimizing the sum of the squares of the residuals made in the results of every single equation.” Wikipedia

“A mathematical procedure for finding the best-fitting curve to a given set of points by minimizing the sum of the squares of the offsets (“the residuals”) of the points from the curve.” ~ Wolfram

I watched “Linear Regression” concept on Statquest.

Video:

Resources & References:

en.wikipedia.org/wiki/Least_squares

https://mathworld.wolfram.com/LeastSquaresFitting.html

 

Receiver Operating Characteristic Curve (ROC)

0

Day 6 of #66DaysofData.com: Receiver Operating Characteristic Curve (ROC)

POPULAR