Category Archives: Data Science
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 …
Poisson Distribution Explained with Python Examples
Poisson distribution is a probability distribution that can be used to model the number of events in a fixed interval. It is often referred to as “random poisson process” or “poisson process”. The poisson distribution describes how many occurrences of an event occur within a given time frame, for example, how many customers visit your store or restaurant every hour. In this post, you will learn about the concepts of Poisson probability distribution with Python examples. As a data scientist, you must get a good understanding of the concepts of probability distributions including normal, binomial, Poisson etc. What is Poisson distribution? Poisson distribution is the discrete probability distribution which represents the …
Accuracy, Precision, Recall & F1-Score – Python Examples
Classification models are used in classification problems to predict the target class of the data sample. The classification model predicts the probability that each instance belongs to one class or another. It is important to evaluate the performance of the classifications model in order to use these models in production for solving real world problems. Performance measures in machine learning classification models are used to assess how well machine learning classification algorithms perform in a given context. These performance metrics include accuracy, precision, recall and F1-score. Because it helps us understand the strengths and limitations of these models when making predictions in new situations, model performance is essential for machine …
Bagging Classifier Python Code Example
Bagging is a type of ensemble machine learning approach that combines the outputs from many learner to improve performance. These algorithms function by breaking down the training set into subsets and running them through various machine-learning models, after which combining their predictions when they return together to generate an overall prediction for each instance in the original data. In this blog post, you will learn about the concept of Bagging along with Bagging Classifier Python code example. Bagging is commonly used in machine learning for classification problems, particularly when using decision trees or artificial neural networks as part of a boosting ensemble. It has been applied to various machine-learning algorithms including decision stumps, …
Demand Forecasting & Machine Learning Techniques
Machine learning is a technology that can be used for demand forecasting in order to make demand forecasts more accurate and reliable. In demand forecasting, machine learning techniques are used to forecast demand for a product or service. There are different types of machine learning/deep learning techniques used in demand forecastings such as neural networks, support vector machines, time series forecasting, and regression analysis. This blog post will introduce different machine learning & deep learning techniques for demand forecasting and give an overview of how they work. What is the demand forecasting process? The demand forecasting process is defined as the creation of demand forecasts, demand planning, and demand decision …
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 …
Agriculture Use Cases & Machine Learning Applications
Today agriculture is in a state of flux. Farmers are faced with the challenges of producing more food in face of a changing climate and population growth, while also adapting to evolving technologies that have changed agriculture forever. Machine learning has been applied to agriculture for many different use cases, from irrigation scheduling to pest management. In this post, we will explore agriculture use cases for machine learning & deep learning that can help farmers meet these challenges head-on. Different machine learning applications can be built around these agricultural use cases. It will be helpful for data scientists to get a high level idea around use cases and related machine …
Gaussian Mixture Models: What are they & when to use?
Gaussian mixture models (GMMs) are a type of machine learning algorithm. They are used to classify data into different categories based on the probability distribution. Gaussian mixture models can be used in many different areas, including finance, marketing and so much more! In this blog, an introduction to gaussian mixture models is provided along with real-world examples, what they do and when GMMs should be used. What are Gaussian mixture models (GMM)? The Gaussian mixture model is defined as a clustering algorithm that is used to discover the underlying groups of data. It can be understood as a probabilistic model where Gaussian distributions are assumed for each group and they …
Credit Card Fraud Detection & Machine Learning
Credit card fraud detection is a major concern for credit card companies. With credit cards so prevalent in our society, credit card companies must be able to prevent credit card fraud and protect their customers. Machine learning techniques can provide a powerful and effective way of detecting credit card fraud. In this blog post we will discuss machine learning techniques that data scientists can use to design appropriate credit card fraud detection solutions including algorithms such as Bayesian networks, support vector machines, neural networks and decision trees. What are different types of credit card fraud? The following are different types of credit card fraud: Counterfeit credit cards: Counterfeit credit cards …
Account Receivables Use Cases for Machine Learning
Account receivables (AR) account for a significant portion of total assets and revenue. However, the account receivable process is typically handled manually by accountants or finance staff. This can lead to inefficiencies when it comes to identifying account issues and resolving them quickly. In this blog post, you will learn about account receivables use cases and how AI/machine learning and deep learning techniques can be used to streamline account receivable processes. For product managers and data scientists, this post will prove to be useful to understand different machine learning use cases related to AR processes. What are key account receivables (AR) processes? Account receivables processes help companies track account receivables …
Quantum machine learning: Concepts and Examples
Machine learning has been a hot topic for many years now. There are different types of machine learning algorithms that data scientists and engineers use in their projects, depending on the type of problem they’re trying to solve. Recently, quantum machine learning has emerged as an alternative to classical machine learning techniques. The future of quantum computing holds tremendous possibilities promising exponential speedups over current technology. In this blog post, we’ll explore quantum machine learning (QML), its benefits over traditional machine learning methods, and the common quantum computing concepts it relies on. What are key concepts related to quantum computing? Quantum computing takes advantage of the computing power available through …
Supplier Relationship Management & Machine Learning
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 …
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 what machine learning entails, how it differs from traditional programming. What is machine learning? Simply speaking, machine learning is a technology where in machine learns to perform a prediction/estimation task based on past experience represented by historical data set. There are three key aspects of machine learning which are following: Task: Task can be related to prediction problems Experience: Experience represents historical dataset Performance: The goal is to perform better in the prediction task …
Covid-19 Machine Learning Use Cases
The covid-19 virus is a type of coronavirus. It has been linked to severe acute respiratory syndrome (SARS). The covid-19 virus can be contracted through contact with saliva or mucous from an infected person. Symptoms include fever, cough, sore throat, headache, muscle aches, and fatigue. There are several problems related to the Covid-19 pandemic which can be solved using machine learning/data science techniques. In this blog post, we will look into some of these Covid-19 use cases which can be solved using machine learning classification and clustering techniques. What are Covid-19 data sets publicly available? One of the datasets available for studying Covid-19 is GISAID data (https://www.gisaid.org/) that represents million …
Hidden Markov Models Explained with Examples
Hidden Markov models (HMMs) are a type of statistical modeling that has been used for several years. They have been applied in different fields such as medicine, computer science, and data science. The Hidden Markov model (HMM) is the foundation of many modern-day data science algorithms. It has been used in data science to make efficient use of observations for successful predictions or decision-making processes. This blog post will cover hidden Markov models with real-world examples and important concepts related to hidden Markov models. What are Markov Models? Markov models are statistical models that are used to predict the next state based on the current hidden or observed states. Markov …
CNN Basic Architecture for Classification & Segmentation
Convolutional neural networks (CNNs) are deep neural networks that have the capability to classify and segment images. CNNs can be trained using supervised or unsupervised machine learning methods, depending on what you want them to do. CNN architectures for classification and segmentation include a variety of different layers with specific purposes, such as a convolutional layer, pooling layer, fully connected layers, dropout layers, etc. In this blog post, we will go over how CNNs work in detail for classification and segmentation problems. Description of basic CNN architecture for Classification The CNN architecture for classification includes convolutional layers, max-pooling layers, and fully connected layers. Convolution and max-pooling layers are used for …
