# Axis and Dimensions in Numpy and Pandas Array

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

# What is the Shape of an Array

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. …

# Bias-Variance Trade-off in DataScience and Calculating with Python

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…

# Vectorizing Gradient Descent — Multivariate Linear Regression and Python implementation

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.

# First a Refresher on basic Matrix Algebra

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… 