# Author Archives: Ajitesh Kumar

## Softmax Regression Explained with Python Example

In this post, you will learn about the concepts of what is Softmax regression/function with Python code examples and why do we need them? As data scientist/machine learning enthusiasts, it is very important to understand the concepts of Softmax regression as it helps in understanding the algorithms such as neural networks, multinomial logistic regression, etc in a better manner. Note that the Softmax function is used in various multiclass classification machine learning algorithms such as multinomial logistic regression (thus, also called softmax regression), neural networks, etc. Before getting into the concepts of softmax regression, let’s understand what is softmax function. What’s Softmax function? Simply speaking, the Softmax function converts raw …

## Neural Network Explained with Perceptron Example

Neural networks are an important part of machine learning, so it is essential to understand how they work. A neural network is a computer system that has been modeled based on a biological neural network comprising neurons connected with each other. It can be built to solve machine learning tasks, like classification and regression problems. The perceptron algorithm is a representation of how neural networks work. The artificial neurons were first proposed by Frank Rosenblatt in 1957 as models for the human brain’s perception mechanism. This post will explain the basics of neural networks with a perceptron example. You will understand how a neural network is built using perceptrons. This …

## Classification Problems Real-life Examples

In this post, you will learn about some popular and most common real-life examples of machine learning classification problems. For beginner data scientists, these examples will prove to be helpful to gain perspectives on real-world problems which can be termed as machine learning classification problems. This post will be updated from time-to-time to include interesting real-life examples which can be solved by training machine learning classification models. Before going ahead and looking into examples, let’s understand a little about what is machine learning (ML) classification problem. You may as well skip this section if you are familiar with the definition of machine learning classification problems & solutions. You may want …

## Linear Regression Explained with Python Examples

In this post, you will learn about concepts of linear regression along with Python Sklearn examples for training linear regression models. Linear regression belongs to class of parametric models and used to train supervised models. The following topics are covered in this post: Introduction to linear regression Linear regression concepts / terminologies Linear regression python code example Introduction to Linear Regression Linear regression is a machine learning algorithm used to predict the value of continuous response variables. The predictive analytics problems that are solved using linear regression models are called supervised learning problems as it requires that the value of response/target variables must be present and used for training the models. Also, recall that …

## Normal Distribution Explained with Python Examples

What is normal distribution? It’s a probability distribution that occurs in many real world cases. In this blog post, you will learn about the concepts of Normal Distribution with the help of Python example. As a data scientist, you must get a good understanding of different probability distributions in statistics in order to understand the data in a better manner. Normal distribution is also called as Gaussian distribution or Laplace-Gauss distribution. Normal Distribution with Python Example Normal distribution is the default probability for many real-world scenarios. It represents a symmetric distribution where most of the observations cluster around the central peak called as mean of the distribution. A normal distribution can be thought of as a …

## Mean Squared Error or R-Squared – Which one to use?

In this post, you will learn about the concepts of the mean-squared error (MSE) and R-squared, the difference between them, and which one to use when evaluating the linear regression models. You also learn Python examples to understand the concepts in a better manner What is Mean Squared Error (MSE)? The Mean squared error (MSE) represents the error of the estimator or predictive model created based on the given set of observations in the sample. Intuitively, the MSE is used to measure the quality of the model based on the predictions made on the entire training dataset vis-a-vis the true label/output value. In other words, it can be used to …

## Linear Regression Explained with Real Life Example

In this post, the linear regression concept in machine learning is explained with multiple real-life examples. Both types of regression models (simple/univariate and multiple/multivariate linear regression) are taken up for sighting examples. In case you are a machine learning or data science beginner, you may find this post helpful enough. You may also want to check a detailed post on what is machine learning – What is Machine Learning? Concepts & Examples. What is Linear Regression? Linear regression is a machine learning concept that is used to build or train the models (mathematical models or equations) for solving supervised learning problems related to predicting continuous numerical value. Supervised learning problems …

## Tensor Broadcasting Explained with Examples

In this post, you will learn about the concepts of Tensor Broadcasting with the help of Python Numpy examples. Recall that Tensor is defined as the container of data (primarily numerical) most fundamental data structure used in Keras and Tensorflow. You may want to check out a related article on Tensor – Tensor explained with Python Numpy examples. Broadcasting of tensor is borrowed from Numpy broadcasting. Broadcasting is a technique used for performing arithmetic operations between Numpy arrays / Tensors having different shapes. In this technique, the following is done: As a first step, expand one or both arrays by copying elements appropriately so that after this transformation, the two tensors have the …

## Regularization in Machine Learning: Concepts & Examples

In machine learning, regularization is a technique used to avoid overfitting. This occurs when a model learns the training data too well and therefore performs poorly on new data. Regularization helps to reduce overfitting by adding constraints to the model-building process. As data scientists, it is of utmost importance that we learn thoroughly about the regularization concepts to build better machine learning models. In this blog post, we will discuss the concept of regularization and provide examples of how it can be used in practice. What is regularization and how does it work? Regularization in machine learning represents strategies that are used to reduce the generalization or test error of …

## Difference: Binary, Multiclass & Multi-label Classification

There are three main types of classification algorithms when dealing with machine learning classification problems: Binary, Multiclass, and Multilabel. In this blog post, we will discuss the differences between them and how they can be used to solve different problems. Binary classifiers can only classify data into two categories, while multiclass classifiers can classify data into more than two categories. Multilabel classifiers assign or tag the data to zero or more categories. Let’s take a closer look at each type! Binary classification & examples Binary classification is a type of supervised machine learning problem that requires classifying data into two mutually exclusive groups or categories. The two groups can be …

## Measure Code Quality using Cyclomatic Complexity

The article talks about how McCabe’s cyclomatic complexity could be used to measure several different aspects of code quality. The objective of this article is to help developers quickly assess code quality by looking at the code. However, let’s try and quickly understand what is cyclomatic complexity and how could it be measured? Thanks for reading it further. And, apologies for spelling mistakes. What is Cyclomatic Complexity? Cyclomatic complexity is a measure of code quality that takes into account the number of independent paths through a piece of code. A high cyclomatic complexity indicates that a piece of code is more difficult to understand and maintain, and is, therefore, more …

## 80+ Free Online Books, Courses – Machine Learning & Data Science

This post represents a comprehensive list of 80+ free books/ebooks and courses on machine learning, deep learning, data science, optimization, etc which are available online for self-paced learning. This would be very helpful for data scientists starting to learn or gain expertise in the field of machine learning / deep learning. Please feel free to comment/suggest if I missed mentioning one or more important books that you like and would like to share. Also, sorry for the typos. Following are the key areas under which books are categorized: Data science Pattern Recognition & Machine Learning Probability & Statistics Neural Networks & Deep Learning Optimization Data mining Mathematics Here is my post …

## Most Common Machine Learning Tasks

This article represents some of the most common machine learning tasks that one may come across while trying to solve machine learning problems. Also listed is a set of machine learning methods that could be used to resolve these tasks. Please feel free to comment/suggest if I missed mentioning one or more important points. Also, sorry for the typos. You might want to check out the post on what is machine learning?. Different aspects of machine learning concepts have been explained with the help of examples. Here is an excerpt from the page: Machine learning is about approximating mathematical functions (equations) representing real-world scenarios. These mathematical functions are also referred …

## What is Machine Learning? Concepts & Examples

Machine learning is a machine’s ability to learn from data. It has been around for decades, but machine learning is now being applied in nearly every industry and job function. In this blog post, we’ll cover a detailed introduction to what is machine learning including different definitions. We will also learn about different types of machine learning tasks, algorithms, etc along with real-world examples. What is machine learning & how does it work? Simply speaking, machine learning can be used to model our beliefs about real-world events. For example, let’s say a person came to a doctor with a certain blood report. A doctor based on his belief system learned …

## Statistics – Random Variables, Types & Python Examples

Random variables are one of the most important concepts in statistics. In this blog post, we will discuss what they are, their different types, and how they are related to the probability distribution. We will also provide examples so that you can better understand this concept. As a data scientist, it is of utmost importance that you have a strong understanding of random variables and how to work with them. What is a random variable and what are some examples? A random variable is a variable that can take on random values. The key difference between a variable and a random variable is that the value of the random variable …

## Frequentist vs Bayesian Probability: Difference, Examples

In this post, you will learn about the difference between Frequentist vs Bayesian Probability. It is of utmost importance to understand these concepts if you are getting started with Data Science. What is Frequentist Probability? Probability is used to represent and reason about uncertainty. It was originally developed to analyze the frequency of the events. In other words, the probability was developed as frequentist probability. The probability of occurrence of an event, when calculated as a function of the frequency of the occurrence of the event of that type, is called Frequentist Probability. Frequentist probability is a way of assigning probabilities to events that take into account how often those events actually occur. Frequentist …