Category Archives: Data Science
Supplier Relationship Management & Machine Learning / AI
Supplier relationship management (SRM) is the process of managing supplier relationships to develop and maintain a strategic procurement partnership. SRM includes focus areas such as supplier selection, procurement strategy development, procurement negotiation, and performance measurement and improvement. SRM has been around for over 20 years but we are now seeing new technologies such as machine learning come into play. What exactly does advanced analytics such as artificial intelligence (AI) / machine learning (ML) have to do with SRM? And how will AI/ML technologies transform procurement? What are some real-world machine learning use cases related to supplier relationships management? What are a few SRM KPIs/metrics which can be tracked by leveraging …
Feedforward Neural Network Python Example
A feedforward neural network, also known as a multi-layer perceptron, is composed of layers of neurons that propagate information forward. In this post, you will learn about the concepts of feedforward neural network along with Python code example. We will start by discussing what a feedforward neural network is and why they are used. We will then walk through how to code a feedforward neural network in Python. In order to get good understanding on deep learning concepts, it is of utmost importance to learn the concepts behind feed forward neural network in a clear manner. Feed forward neural network learns the weights based on backpropagation algorithm which will be discussed …
Artificial Intelligence (AI) for Telemedicine: Use cases, Challenges
In this post, you will learn about different artificial intelligence (AI) use cases of Telemedicine / Telehealth including some of key implementation challenges pertaining to AI / machine learning. In case you are working in the field of data science / machine learning, you may want to go through some of the challenges, primarily AI related, which is thrown in Telemedicine domain due to upsurge in need of reliable Telemedicine services. What is Telemedicine? Telemedicine is the remote delivery of healthcare services, using digital communication technologies. It has the potential to improve access to healthcare, especially in remote or underserved communities. It can be used for a variety of purposes, including …
Scatter plot Matplotlib Python Example
If you’re a data scientist, data analyst or a Python programmer, data visualization is key part of your job. And what better way to visualize all that juicy data than with a scatter plot? Matplotlib is your trusty Python library for creating charts and graphs, and in this blog we’ll show you how to use it to create beautiful scatter plots using examples and with the help of Matplotlib library. So dig into your data set, get coding, and see what insights you can uncover! What is a Scatter Plot? A scatter plot is a type of data visualization that is used to show the relationship between two variables. Scatter …
Why AI & Machine Learning Projects Fail?
AI / Machine Learning and data science projects are becoming increasingly popular for businesses of all sizes. Every organization is trying to leverage AI to further automate their business processes and gain competitive edge by delivering innovative solutions to their customers. However, many of these AI & machine learning projects fail due to various different reasons. In this blog post, we will discuss some of the reasons why AI / Machine Learning / Data Science projects fail, and how you can avoid them. The following are some of the reasons why AI / Machine learning projects fail: Lack of understanding of business problems / opportunities Ineffective solution design approaches Lack …
Weight Decay in Machine Learning: Concepts
Weight decay is a popular technique in machine learning that helps to improve the accuracy of predictions. In this post, we’ll take a closer look at what weight decay is and how it works. We’ll also discuss some of the benefits of using weight decay and explore some possible applications. As data scientists, it is important to learn about concepts of weight decay as it helps in building machine learning models having higher generalization performance. Stay tuned! What is weight decay and how does it work? Weight decay is a regularization technique that is used to regularize the size of the weights of certain parameters in machine learning models. Weight …
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 …
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 …
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 …
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 …
SVM Classifier using Sklearn: Code Examples
In this post, you will learn about how to train an SVM Classifier using Scikit Learn or SKLearn implementation with the help of code examples/samples. An SVM classifier, or support vector machine classifier, is a type of machine learning algorithm that can be used to analyze and classify data. A support vector machine is a supervised machine learning algorithm that can be used for both classification and regression tasks. The Support vector machine classifier works by finding the hyperplane that maximizes the margin between the two classes. The Support vector machine algorithm is also known as a max-margin classifier. Support vector machine is a powerful tool for machine learning and has been widely used …
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 …
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 …
I found it very helpful. However the differences are not too understandable for me