Category Archives: Data Science

Graph Neural Networks Explained with Examples

Training a graph neural network model

Graph neural networks (GNNs) are a relatively new area in the field of deep learning. They arose from graph theory and machine learning, where the graph is a mathematical structure that models pairwise relations between objects. Graph Neural Networks are able to learn graph structures for different data sets, which means they can generalize well to new datasets – this makes them an ideal choice for many real-world problems like social network analysis or financial risk prediction. This post will cover some of the key concepts behind graph neural networks with the help of multiple examples. What are graph neural networks (GNNs)? Graphs are data structures which are used to …

Continue reading

Posted in Data Science, Deep Learning. Tagged with , .

Digital Transformation Strategy: What, Why & How?

digital transformation what why and 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. It helps …

Continue reading

Posted in Data Science. Tagged with , .

Data Storytelling Explained with Examples

MS Dhoni - Former Captain of Indian Cricket Team

Have you ever told a story to someone, but they just didn’t seem to understand it? They might have been confused about the plot or why the characters acted in certain ways. If this has happened to you before, then you are not alone. Many people struggle with data storytelling because they do not know how to communicate their data effectively.  In this blog post, you will learn about some of the key concepts in relation to data storytelling and why data scientists / data analyst should acquire this skill. Data storytelling is one of the key skills which data scientists would need to acquire in order to do a …

Continue reading

Posted in Data Science. Tagged with .

14 Python Automl Frameworks Data Scientists Can Use

Python automl frameworks

In this post, you will learn about Automated Machine Learning (AutoML) frameworks for Python that can use to train machine learning models. For data scientists, especially beginners, who are unfamiliar with Automl, it is a tool designed to make the process of generating machine learning models in an automated manner, user-friendly, and less time-consuming. The goal of Automl is not just about making it easier for machine learning (ML) developers but also democratizing access to model development. What is AutoML? AutoML refers to automating some or all steps of building machine learning models, including selection and configuration of training data, tuning the performance metric(s), selecting/constructing features, training multiple models, evaluating …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , .

20 Amazon (AWS) Machine Learning Services to Know

amazon machine learning services

Amazon Web Services is a cloud computing platform that offers machine learning as one of its many services. AWS has been around for over 10 years and has helped data scientists leverage the amazon cloud to train machine learning models. AWS provides an easy-to-use interface that helps data scientists build, test, and deploy their machine learning models with ease. AWS also provides access to pre-trained machine learning models so you can start building your model without having to spend time training it first! What are different AWS cloud services for machine learning? The following is a list of Amazon cloud services for machine learning. As data scientists, it is of …

Continue reading

Posted in AWS, Cloud, Data Science, Machine Learning. Tagged with , , .

Data Analytics – Different Career Options / Opportunities

data analytics career options

Data analytics career paths span a wide range of career options, from data scientist to data engineer. Data scientists are often interested in what they can do with the data that is analyzed, while data engineers are more focused on the analysis itself. Whether you’re looking for a career as a data scientist, data analyst, ML engineer, or AI researcher, there’s something for everyone! In this blog post, we will different types of jobs and careers available to those interested in data analytics and data science. What are some of the career paths in data analytics? Here are different career paths for those interested in data analytics career: Data Scientists: …

Continue reading

Posted in AI, Career Planning, Data analytics, data engineering, Data Science, Machine Learning. Tagged with , , , .

Using Theory of Change to Design Data-driven Solutions

theory of change for data-driven decision making

Have you ever wanted to design a solution for an issue but weren’t sure how to do it? One theory that can help is the theory of change. The theory of change provides a framework for designing solutions by focusing on the steps needed to achieve desired outcomes or results. It also helps identify what needs to happen in order for the solution to be implemented successfully and realizing the desired outcomes. The theory of change when combined with data-driven decision making can result in great impact. In order to design solutions that have an impact and are sustainable, it is important to understand the theory of change as well …

Continue reading

Posted in Data analytics, Data Science. Tagged with , , .

Key techniques for Evaluating Machine Learning models

AUC-ROC curve

Machine learning is a powerful machine intelligence technique that can be used to develop predictive models for different types of data. It has become the backbone of many intelligent applications and evaluating machine learning model performance at a regular intervals is key to success of such applications. A machine learning model’s performance depends on several factors including the type of algorithm used, how well it was trained and more. In this blog post, we will discuss  essential techniques for evaluating machine-learning model performance in order to provide you with some best practices when working with machine-learning models. The following are different techniques that can be used for evaluating machine learning …

Continue reading

Posted in Data Science, Machine Learning. Tagged with , .

Top 50 Interview Questions for Beginner Data Scientists

interview questions for machine learning

What interview questions should a beginner data scientist prepare for? This is an important question that many interviewees have. If you are going for a data scientist interview and don’t know what interview questions will you be asked, this blog post has some of the common interview questions that will help you excel in your interview. These interview questions are perfect for beginners because they cover basic topics about data science and machine learning and how it works. We hope this list helps! What is the difference between AI, machine learning, deep learning? Do you know how machine learning works? How is machine learning different from statistical modeling techniques like linear …

Continue reading

Posted in Data Science, Interview questions, Machine Learning. Tagged with , , .

13 Machine Learning Use Cases for Climate Change

Climate change is a serious issue facing the world. The climate changes which are already affecting our planet can be seen in rising sea levels, melting ice caps and glaciers, more severe storms and hurricanes, more droughts, and wildfires increased precipitation in some areas of the world while other regions experience less rainfall. It’s important that we do what we can to reduce climate change risks by reducing greenhouse gas emissions into the atmosphere as well as adapting to climate impacts. Artificial intelligence (AI), machine learning (ML)/ deep learning (DL), advanced analytics have been widely used for decades across different industries such as finance, healthcare, etc., but their use cases …

Continue reading

Posted in Climate Change, Data Science, Machine Learning. Tagged with , .

How to Create & Detect Deepfakes Using Deep Learning

create and detect deepfake using deep learning

Deepfake are becoming a more common occurrence in today’s world. What is deepfake and how can you create it using deep learning? This blog post will help data scientists learn techniques for creating and detecting deepfakes, so they can stay ahead of this technology. A deepfake is a video or audio that alters reality by changing the way something appears. For example, someone could place your face onto someone else’s body in a video to make it seem like you were there when you really weren’t. There are many ways that one can detect if a photo has been manipulated with software such as Photoshop or Gimp. What is deepfake? …

Continue reading

Posted in Data Science, Deep Learning, Machine Learning. Tagged with , , .

Mean Squared Error vs Cross entropy loss function

In this post, you will be learning the difference between two common types of loss functions: Cross-Entropy Loss and Mean Squared Error (MSE) Loss. These are both used in machine learning for classification & regression tasks, respecitively, to measure how well a model performs on unseen dataset. Both these losses are ways of measuring how well the predictions are made by classification and regression algorithms, and they both provide different information about the performance of models. As a data scientist, it is very important for you to understand the difference between loss functions in a great manner. What is cross-entropy loss? Cross entropy loss is used in classification tasks where …

Continue reading

Posted in Data Science, Machine Learning. Tagged with , .

Procurement: Key Advanced Analytics Use Cases

procurement analytics use cases

The procurement analytics applications are poised to grow exponentially in the next few years. With so much data available, it’s important to know how procurement analytics can help you make better business decisions. This blog will cover procurement analytics and key use cases of advanced analytics that will be useful for data scientists implementing different use cases using machine learning. It is aimed at procurement professionals and data scientists who would like to use procurement analytics for making better business decisions. With so much data available nowadays it’s important to know how procurement analytics can help you make the best decision possible based on cost, quality, Procurement analytics allows you …

Continue reading

Posted in Data Science, Machine Learning, Procurement. Tagged with , , .

How do we build Deep Neural Network using Perceptron?

Single layer neural network

In this post, you will understand about how a deep neural network is built using a perceptron. This is a very important concept in relation to getting a good understanding of deep learning. You will also learn related Tensorflow / Keras code snippet. Here are the key concepts related to how deep neural network is built using one or more perceptrons: First and foremost, it is key to understand what is a Perceptron? A perceptron is the most fundamental unit which is used to build a neural network. A perceptron resembles a neuron in the human brain. In case of a neuron, multiple input signals are fed into a neuron …

Continue reading

Posted in Data Science, Deep Learning, Machine Learning. Tagged with , .

Examples of Generative Adversarial Network (GAN)

In this post, you will learn examples of generative adversarial network (GAN). The idea is to put together some of the interesting examples from across the industry to get a perspective on what problems can be solved using GAN. As a data scientist or machine learning engineer, it would be imperative upon us to understand the GAN concepts in a great manner to apply the same to solve real-world problems. This is where GAN network examples will prove to be helpful. Here are some examples of GAN network usage. Text to image translation Image editing / manipulating Creating images (2-dimensional images) Recreating images of higher resolution Creating 3-dimensional object Text to …

Continue reading

Posted in Data Science, Deep Learning, Machine Learning. Tagged with , , .

Perceptron Explained using Python Example

In this post, you will learn about the concepts of Perceptron with the help of Python example. It is very important for data scientists to understand the concepts related to Perceptron as a good understanding lays the foundation of learning advanced concepts of neural networks including deep neural networks (deep learning).  In this post, the following topics are covered: What is Perceptron? Perceptron Python code example What is Perceptron? Perceptron is a machine learning algorithm which mimics how a neuron in the brain works. It is also called as single layer neural network consisting of a single neuron. The output of this neural network is decided based on the outcome of just one activation …

Continue reading

Posted in Data Science, Deep Learning, Machine Learning. Tagged with , , , .