As a career Data-Scientist, all through your life you have to deal with Matrix form of data where data in **Numpy or Pandas or TensorFlow where Axis and Dimensions are the fundamental structural concept.**

**Basic Attributes of the ndarray Class**

Let's consider the below array

The “shape” of this array is a tuple with the number of elements per axis (dimension). In our example, the shape is equal to (6, 3), i.e. we have 6 lines and 3 columns.

Numpy has a function called “shape” which returns the shape of an array. …

The target of this blog post is to discuss the concept around and the Mathematics behind the below formulation of **Bias-Variance Tradeoff.**

And in super simple term

*Total Prediction Error = Bias + Variance*

The goal of any supervised machine learning model is to best estimate the mapping function (f) for the output/dependent variable (Y) given the input/independent variable (X). The mapping function is often called the target function because it is the function that a given supervised machine learning algorithm aims to approximate.

The **Expected Prediction Error** for any machine learning algorithm can be broken down into three parts:

Bias Error

Variance Error

Irreducible…

In this article, I shall go over the topic of arriving at the** Vectorized Gradient-Descent formulae for the Cost function of the for Matrix form of training-data Equations.** And along with that the Fundamentals of Calculus (especially Partial Derivative) and Matrix Derivatives necessary to understand the process.

**So our target of this article is to understand the full Mathematics and the flow behind arriving at the below formulae**, which is the Vectorized Gradient of the training-data Matrix

A matrix A over a field K or, simply, a matrix A (when K is implicit) is a rectangular array of scalars usually presented in the following…

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