Data Transformation (7/8) Target Based Transformations

11-4 Key Properties of Eigenvalues and Eigenvectors Part 3 (Linear Algebra for Data Science)

Data Transformation (6/8) Data Discretization

11-3 Key Properties of Eigenvalues and Eigenvectors Part 2 (Linear Algebra for Data Science)

Data Transformation (5/8) Data Aggregation

11-2 Key Properties of Eigenvalues and Eigenvectors Part 1 (Linear Algebra for Data Science)

Data Transformation (4/8) Binarization

11-1 Basics of Eigenvalues and Eigenvectors (Linear Algebra for Data Science)

Data Transformation (3/8) Encoding Categorical Variables

10-4 Gram-Schmidt Process (Linear Algebra for Data Science)

Data Transformation (2/8) Scaling

10-3 Theory and Computation of Orthogonal Projection (Linear Algebra for Data Science)

Data Transformation (1/8) Introduction to Data Transformation

10-2. Orthogonal Sets and Bases (Linear Algebra for Data Science )

Data Cleaning (32/32) De Duplication (Part 4) Automating the Deduplication Process

10-1. Orthogonal Vectors & Projections (Linear Algebra for Data Science)

Data Cleaning (31/32) De Duplication (Part 3) De-Duplication Approaches

9-12. Practical Applications: Completeness & Hilbert Spaces (Linear Algebra for Data Science)

Data Cleaning (30/32) De Duplication (Part 2) Duplicate Detection by Machine Learning

9-11. Completeness & Hilbert Spaces (Linear Algebra for Data Science)

Data Cleaning (29/32) De Duplication (Part 1) Duplicate Detection

9-10. Inner Product--Completeness (Linear Algebra for Data Science)

Data Cleaning (28/32) Correcting Data Inconsistencies

9-9. Practical Applications Normed Space & Inner Product Space 2 (Linear Algebra for Data Science)

Data Cleaning (27/32) Detecting Data Inconsistency using Fuzzy matching

9-8 Normed Space & Inner Product Space―Linear Algebra for Data Science

Data Cleaning (26/32) Detecting Data Inconsistency using Regular Expression (Regex)

9-7. Practical Application―Inner Product & Vector Space (Linear Algebra for Data Science)

Data Cleaning (25/32) Outlier Treatment by Python

9-6. Inner Product & Vector Space (Linear Algebra for Data Science)

Data Cleaning (24/32) Outlier Treatment

9-5 Practical Applications of Inner Product (Linear Algebra for Data Science)

Data Cleaning (23/32) Outlier Detection by Shrinkage Covariance Matrix (SCM) Part 2

9-4. Inner Product & Vector Norm (Linear Algebra for Data Science)

Data Cleaning (22/32) Outlier Detection by Shrinkage Covariance Matrix (SCM) Part 1

9-3. Inner Product (Linear Algebra for Data Science)

Data Cleaning (21/32) Outlier Detection by MCD (Minimum Covariance Determinant)

9-2. Dot Product (Part 2)―Properties & Applications (Linear Algebra for Data Science)

Data Cleaning (20/32) Multivariate Outlier Detection by Mahalanobis Distance Part 2

9-1. Dot Product (Part 1) (Linear Algebra for Data Science)

Data Cleaning (19/32) Multivariate Outlier Detection by Mahalanobis Distance Part 1

8-5. Systems of Linear Equations and Linear Regression (Linear Algebra for Data Science)

Data Cleaning (18/32) Q-Q Plot: Advanced Univariate Outlier Detection

8-4. Determinant and Non Trivial Solutions (Linear Algebra for Data Science)

Data Cleaning (17/32) Robust Z-score & Winsorization: Advanced Univariate Outlier Detection 2

8-3. LU Decomposition (Linear Algebra for Data Science)

8-2. Gaussian elimination (Linear Algebra for Data Science)

Data Cleaning (16/32) Advanced Methods for Univariate Outlier Detection 1: Trimmed Mean, Median, MAD

8-1. Systems of Linear Equations (Linear Algebra for Data Science)

Data Cleaning (15/32) Common Pitfall in Outlier Detection

7-6. Cofactor Expansion (Linear Algebra for Data Science)

Data Clearning (14/32) Outlier Detection

7-5. Properties of Determinants (Linear Algebra for Data Science)

Data Cleaning (13/32) Rubin's Rule: Missing Data Imputation

7-4. Why Inverse Matrix Useful in Data Science (Linear Algebra for Data Science)

Theory of Planned Behavior

Data Cleaning (12/32) Mutiple Imputation by Python: Missing Data Imputation

7-3. What Is an Inverse Matrix and Why Does It Matter in Data Science?

Data Cleaning (11/32) Multiple Imputation: Missing Data Imputation

7-2. Transposed Matrix & Linear Regression (Linear Algebra for Data Science)