Non-fungible tokens (NFTs) & Real-world examples
You may have heard the term “non-fungible tokens (NFT)” but what do they mean? Basically, they are a type of cryptocurrency that is unique and not interchangeable. Unlike regular Bitcoin or Ethereum, which can be divided and traded like shares, non-fungible tokens are indivisible and have their own value. This makes them perfect for use in specific applications like digital art or collectibles. Here we’ll discuss what are NFTs and what are some real-world examples of where non-fungible tokens are being used today. What are Non-fungible tokens (NFT) and how do they work? Non-fungible tokens are unique digital assets. The word non-fungible means that each token is not interchangeable with …
Stochastic Gradient Descent Python Example
In this post, you will learn the concepts of Stochastic Gradient Descent (SGD) using a Python example. Stochastic gradient descent is an optimization algorithm that is used to optimize the cost function while training machine learning models. The most popular algorithm such as gradient descent takes a long time to converge for large datasets. This is where the variant of gradient descent such as stochastic gradient descent comes into the picture. In order to demonstrate Stochastic gradient descent concepts, the Perceptron machine learning algorithm is used. Recall that Perceptron is also called a single-layer neural network. Before getting into details, let’s quickly understand the concepts of Perceptron and the underlying learning …
Dummy Variables in Regression Models: Python, R
In linear regression, dummy variables are used to represent the categorical variables in the model. There are a few different ways that dummy variables can be created, and we will explore a few of them in this blog post. We will also take a look at some examples to help illustrate how dummy variables work. We will also understand concepts related to the dummy variable trap. By the end of this post, you should have a better understanding of how to use dummy variables in linear regression models. As a data scientist, it is important to understand how to use linear regression and dummy variables. What are dummy variables in …
Book: First principles thinking for building winning products
Can innovation be taught and learned in a methodical manner? Can there be an innovation playbook using which, given a need to create a thing, product, or solve a complex problem, a set of well-defined steps be followed? How has Elon Musk been super successful time and again to create game-changing innovative products that created tremendous value for end-users and society at large? The answers to these questions can be found with a reasoning technique called first principles thinking. First principles thinking is defined as a method of reasoning or a thought process in which you try and understand the fundamental truth regarding different aspects of the existence of a …
Linear regression hypothesis testing: Concepts, Examples
In relation to machine learning, linear regression is defined as a predictive modeling technique that allows us to build a model which can help predict continuous response variables as a function of a linear combination of explanatory or predictor variables. While training linear regression models, we need to rely on hypothesis testing in relation to determining the relationship between the response and predictor variables. In the case of the linear regression model, two types of hypothesis testing are done. They are T-tests and F-tests. In other words, there are two types of statistics that are used to assess whether linear regression models exist representing response and predictor variables. They are …
Correlation Concepts, Matrix & Heatmap using Seaborn
In this blog post, we’ll be discussing correlation concepts, matrix & heatmap using Seaborn. For those of you who aren’t familiar with Seaborn, it’s a library for data visualization in Python. So if you’re looking to up your data visualization game, stay tuned! We’ll start with the basics of correlation and move on to discuss how to create matrices and heatmaps with Seaborn. Let’s get started! Introduction to Correlation Correlation is a statistical measure that expresses the strength of the relationship between two variables. The two main types of correlation are positive and negative. Positive correlation occurs when two variables move in the same direction; as one increases, so do …
What is Blockchain & How does it work?
Blockchain is a distributed database that allows for secure, transparent, and tamper-proof transactions. It was first introduced in 2009 as the underlying technology behind Bitcoin. Blockchain has since garnered a great deal of attention due to its potential applications in a variety of industries. In this blog post, we will explore what blockchain is and how it works! What is Blockchain? Blockchain is a distributed database that allows for secure, transparent, and tamper-proof transactions. Blockchain was originally conceived as the underlying technology for the cryptocurrency bitcoin. However, Blockchain has since been found to have many other potential use cases. The key concepts behind Blockchain are decentralization, immutability, and consensus. Blockchain …
When to Use Which Clustering Algorithms?
There are many clustering machine learning algorithms to choose from when you want to cluster data. But which one should you use in a particular situation? In this blog post, we will explore the different clustering algorithms and explain when each one is most appropriate. We will also provide examples so that you can see how these algorithms work in practice. What clustering is and why it’s useful Simply speaking, clustering is a technique used in machine learning to group data points together. The goal of clustering is to find natural groups, or clusters, in the data. Clustering algorithms are used to automatically find these groups. Clustering is useful because …
AI / Data Science Operating Model: Teams, Processes
Realizing value from AI/data science or machine learning projects requires the coordination of many different teams based on an appropriate operating model. If you want to build an effective AI/data science operation, you need to create a data science operating model that outlines the steps involved in how teams are structured, how data science projects are implemented, how the maturity of data science practice is evaluated and an overall governance model which is used to keep a track of data science initiatives. In this blog post, we will discuss the key components of a data science operating model and provide examples of how to optimize your data science process. AI …
Deductive & Inductive Reasoning: Examples, Differences
When it comes to data science, there are two main types of reasoning that you need to be familiar with: deductive and inductive. Both of these techniques are important in order to make sound decisions based on the data that you’re working with. In this blog post, we’ll take a closer look at what deductive and inductive reasoning are, what are their differences, and how they’re related to each other. What is deductive reasoning? Deductive reasoning is an important tool in data science. Deductive reasoning is the process of deriving a conclusion based on premises that are known or assumed to be true. In other words, deductive reasoning allows you …
What is Data Quality Management? Concepts & Examples
What is data quality? This is a question that many people ask, but it is not always easy to answer. Simply put, data quality refers to the accuracy and completeness of data. If data is not accurate, it can lead to all sorts of problems for businesses. That’s why data quality is so important – it ensures that your data is reliable and can be used for decision-making purposes. Data is at the heart of any enterprise. It is essential for making sound business decisions, understanding customers, and improving operations. However, not all data is created equal. In order to make the most out of your data, you need to …
Steps for Evaluating & Validating Time-Series Models
Time-series machine learning models are becoming increasingly popular due to the large volume of data that is now available. These models can be used to make predictions about future events, and they are often more accurate than traditional methods. However, it is important to properly evaluate (check accuracy by performing error analysis) and validate these models before you put them into production. In this blog post, we will discuss the different ways that you can evaluate and validate time series machine learning models. We will also provide some tips on how to improve your results. As data scientists, it is important to learn the techniques related to evaluating time-series models. …
What is Explainable AI? Concepts & Examples
What is explainable AI (XAI)? This is a question that more people are asking, as they become aware of the potential implications of artificial intelligence. Simply put, explainable AI is the form of AI that can be understood by humans. It is AI that provides an explanation for its decisions and actions. It provides humans with the ability to explain how decisions are made by machines. This helps people trust and understand what’s happening, instead of feeling like their information is being taken advantage of or used without their permission. This is important, as many people are concerned about the increasing use of AI in our lives, especially in healthcare. …
Machine Learning with Alteryx: Examples
Alteryx is a self-service data analytics software platform that enables users to easily prep, blend, and analyze data all in one place. It is a powerful tool that can be used in a variety of machine learning scenarios. It can be used to clean and prepare data, and develop, evaluate and deploy machine learning (ML) models. It offers a variety of features and tools that can be used to preprocess data, choose algorithms, train models, and evaluate results. In this blog post, we will discuss some of the ways that Alteryx can be used in machine learning. We will also provide examples of how to use Alteryx in machine learning scenarios. …
Digital Transformation Strategy: What, Why & How?
Digital transformation is a digital strategy that aims to change the way an organization operates. It’s not just about digital marketing anymore – digital transformation includes all aspects of digital engagement from customer service, product development, and delivery, operations, etc. And it requires a holistic approach to digital transformation without any silos or strategic gaps in between departments. In this blog post, we will cover what digital transformation is and why organizations should take advantage of this strategy. We’ll also look at how digital transformation is happening in different industries. What is digital transformation? Digital transformation is a digital strategy that aims to change the way an organization operates and …
Hate Speech Detection Using Machine Learning
Hate speech is a big problem on the internet. It can be found on social media, in comment sections, and even in online forums. Detecting hate speech is important because it can have harmful effects on society. In this blog post, we will discuss the latest techniques for detecting hate speech using machine learning algorithms. We will also provide examples of how these algorithms work. What is hate speech? Hate speech can be defined as any speech that targets a group of people based on their race, religion, ethnicity, national origin, sexual orientation, or gender identity. Hate speech is often used to spread hate and bigotry. It can also be …
I found it very helpful. However the differences are not too understandable for me