Saturday, October 23, 2021
Home Blog

StatQuest: Machine Learning Playlist #66DaysOfData

By
Asaad Khattab
-
0

46Day 52 of #66DaysofData (03/08/21):

I watched the Youtube Video, StatQuest: XGBoost Part 1 (of 4): Regression

45Day 51 of #66DaysofData (02/27/21):

I watched the Youtube Video, StatQuest: Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)

44Day 50 of #66DaysofData (02/22/21):

I watched the Youtube Video, StatQuest: Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)

43Day 49 of #66DaysofData (02/20/21):

I watched the Youtube Video, StatQuest: Support Vector Machines Part 1 (of 3): Main Ideas!!!

42Day 48 of #66DaysofData (02/18/21):

I re-watched the Youtube Video, StatQuest: Gradient Boost Part 1 (of 4)

41Day 47 of #66DaysofData (02/17/21):

I watched the Youtube Video, StatQuest: Gradient Boost Part 2 (of 4): Regression Details

40Day 46 of #66DaysofData (02/16/21):

I watched the Youtube Video, StatQuest: Gradient Boost Part 1 (of 4): Regression Main Ideas

39Day 45 of #66DaysofData (02/15/21):

I watched the Youtube Video, StatQuest: Adaboost

38Day 44 of #66DaysofData (02/14/21):

I watched the Youtube Video, StatQuest: Stochastic Gradient Descent

37Day of #66DaysofData (02/13/21):

 

36Day 43 of #66DaysofData (02/12/21):

I watched the Youtube Video, StatQuest: Gradient Descent, Step-by-Step

 

35Day 42 of #66DaysofData (02/11/21):

I watched the Youtube Video, StatQuest: The chain rule

34Day 41 of #66DaysofData (02/10/21):

I watched the Youtube Video, StatQuest: Random Forests Part 2: Missing data and clustering.

33Day 40 of #66DaysofData (02/09/21):

I watched the Youtube Video, StatQuest: Random Forests Part 1 – Building, Using and Evaluating

32Day 39 of #66DaysofData (02/08/21):

I watched the Youtube Video, StatQuest: How to Prune Regression Trees.

31Day 38 of #66DaysofData (02/07/21):

I watched the Youtube Video, StatQuest: Regression Trees.

30Day 37 of #66DaysofData (02/06/21):

I watched the Youtube Video, StatQuest: Decision Trees – part 2.

29Day 36 of #66DaysofData (02/05/21):

I watched the Youtube Video, StatQuest: Decision Trees.

28Day 35 of #66DaysofData (02/04/21):

I watched the Youtube Video, StatQuest: Gaussian Naive Bayes

27Day 34 of #66DaysofData (02/03/21):

I watched the Youtube Video, StatQuest: Naive bayes

26Day 33 of #66DaysofData (02/02/21):

I watched the Youtube Video, StatQuest: K-nearest neighbors

25Day 32 of #66DaysofData (02/01/21):

I watched the Youtube Video, StatQuest: K-means clustering

24Day 31 of #66DaysofData (01/31/21):

I watched the Youtube Video, StatQuest: Hierarchical Clustering

23Day 30 of #66DaysofData (01/30/21):

I watched the Youtube Video, StatQuest: t-SNE, Clearly Explained

22Day 29 of #66DaysofData (01/29/21):

I watched the Youtube Video, StatQuest:MDS and PCoA

21Day 28 of #66DaysofData (01/28/21):

I watched the Youtube Video, StatQuest: Linear Discriminant Analysis (LDA) clearly explained.

20Day 27 of #66DaysofData (01/27/21):

I watched the Youtube Video, StatQuest: PCA in Python

19Day 26 of #66DaysofData (01/26/21):

I watched the Youtube Video, StatQuest: PCA Practical Tips

18Day 25 of #66DaysofData (01/25/21):

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

17Day 24 of #66DaysofData (01/24/21):

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

By
Asaad Khattab
-
0

For Day 11 of #66DaysofData:

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

 

Hierarchical and Network Models

By
Asaad Khattab
-
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

By
Asaad Khattab
-
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

3Resources

learn.datacamp.com/courses/introduction-to-linear-modeling-in-python

Least Squares

By
Asaad Khattab
-
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)

By
Asaad Khattab
-
0

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

1234Page 1 of 4

POPULAR

EDITOR PICKS

POPULAR POSTS

POPULAR CATEGORY

© 2021 makeitDev by Asaad Khattab