Accounts Payable Machine Learning Use Cases
The machine learning for accounts payable market is expected to grow from $6.1 million in 2016 to $76.8 million by 2021, at a compound annual growth rate (CAGR) of 53 percent. The software industry is rapidly embracing machine learning for account payable. As account payable becomes more automated, it also becomes more data-driven. Machine learning is enabling account payables stakeholders to leverage powerful new capabilities in this arena. In this blog post, you will learn machine learning / deep learning / AI use cases for account payable. Key Business Processes for Accounts Payable Here are the key business processes in relation to Accounts Payable: Supplier onboarding: Collect basic information about …
Stock Price Prediction using Machine Learning Techniques
In the past few decades, many advances have been made in the field of data analytics. Researchers are now able to predict stock prices with higher accuracy due to analytical predictive models. These predictive techniques utilize data from previous stock price movements and look for patterns that could indicate future stock price changes in the market. The use of these machine learning techniques will allow investors to make better decisions and invest more wisely by maximizing their returns and minimizing their losses. In this blog post, you will learn about some of the popular machine learning techniques in relation to making stock price movement (direction of stock price) predictions and …
Type I & Type II Errors in Hypothesis Testing: Examples
This article describes Type I and Type II errors made during hypothesis testing, based on a couple of examples such as House on Fire, and Covid-19. You may want to note that it is key to understand type I and type II errors as these concepts will show up when we are evaluating a hypothesis such as those related to machine learning algorithms (linear regression, logistic regression, etc). For example, in the case of linear regression models, the significance value is compared with the p-value and, the null hypothesis that the parameter/coefficient is equal to zero is either rejected or failed to be rejected. You may want to check my …
Hypothesis Testing Explained with Real-life Examples
Hypothesis testing is a statistical technique that helps researchers test the validity of their theories. It’s often used in statistics and data science to analyze whether an event has occurred, or if it will occur based on past events. This blog post will cover some of the key statistical concepts along with examples in relation to how to formulate a hypothesis for hypothesis testing. The knowledge of hypothesis formulation and hypothesis testing would prove key to building various different machine learning models. In later articles, hypothesis formulation for machine learning algorithms such as linear regression, logistic regression models, etc., will be explained. What is a Hypothesis? Simply speaking, hypothesis testing …
Data Science: P-Value Explained with Examples
Many describe p-value as the probability that the null hypothesis holds good. That is an incorrect definition. The concept of p-value is understood differently by different people and is considered as one of the most used & abused concepts in statistics. In this blog post, you will learn the P-VALUE concepts with multiple different examples. It is extremely important to get a good understanding of P-value if you are starting to learn data science/machine learning as the concepts of P-value are key to hypothesis testing. The following use cases and related hypotheses made about the population will either be accepted or rejected based on the P-VALUE: Whether a coin is fair …
Bias-Variance Trade-off Concepts & Interview Questions
Bias vs variance tradeoff is a big problem machine learning models face. In this post, you will learn about the the concepts of bias & variance in relation to the machine learning (ML) models. Bias refers to how well your model can represent all possible outcomes, whereas variance refers to how sensitive your predictions are to changes in the model’s parameters. In addition to learning the concepts related to Bias vs variance trade-off, you would also get a chance to take quiz which would help you prepare for data scientists / ML Engineer interviews. As data scientists / ML Engineer, you must get a good understanding of Bias and Variance concepts …
Difference between Parametric vs Non-Parametric Models
Machine learning models can be parametric or non-parametric. Parametric models are those that require the specification of some parameters before they can be used to make predictions, while non-parametric models do not rely on any specific parameter settings and therefore often produce more accurate results. This blog post discusses parametric vs non-parametric machine learning models with examples along with the key differences. What are parametric and non-parametric models? Training machine learning models is about finding a function approximation built using input or predictor variables, and whose output represents the response variable. The reason why it is called function approximation is because there is always an error in relation to the …
Machine Learning: Inference & Prediction Difference
In machine learning, prediction and inference are two different concepts. Prediction is the process of using a model to make a prediction about something that is yet to happen. The inference is the process of evaluating the relationship between the predictor and response variables. In this blog post, you will learn about the differences between prediction and inference with the help of examples. Before getting into the details related to inference & prediction, let’s quickly recall the machine learning basic concepts. What is machine learning and how is it related with inference & prediction? Machine learning is about learning an approximate function that can be used to predict the value …
What are Actionable Insights: Examples & Concepts
The idea of actionable insights is something that has gone mainstream across different departments in any and every business. Actionable insights are at the heart of many successful business decisions, and are used to help your company grow further than ever before. Actionable insights are key to any data analytics initiatives. Analytics centered around actionable insights is also termed actionable analytics. In this blog post, actionable insights are explained with examples along with few actionable analytics tools which are used when dealing with actionable insights. What are actionable insights? Actionable insights are defined as insights which can help in making decisions and taking action. Actionable insights can be used to …
How to Create Data-Driven Culture: Key Steps
In today’s competitive business environment, companies are looking for the cutting edge they can get to stay ahead. One of the ways to beat the competition is by establishing a culture of data-driven decision making. In this blog post, we will explore how to create a data-driven culture that values data analytics and provides actionable insights into what needs to be done next in order to create a future-ready digital organization. What is data-driven culture? Data-driven culture is about creating an organization that is data-driven, where everything from business processes to culture supports the need for data-based decision making. In other words, every step of a business process must be …
Overfitting & Underfitting Concepts & Interview Questions
Machine learning models are built to learn from training and test data and make predictions on new, unseen data set. The machine learning model is said to overfit the data when it learns patterns that exist only in the training set make prediction with high accuracy. On the other hand, machine learning model underfits if it cannot find any pattern or relationship between variables in both training and testing data sets. In this post, you will learn about some of the key concepts of overfitting and underfitting in relation to machine learning models. In addition, you will also get a chance to test you understanding by attempting the quiz. The …
Data Readiness Levels Assessment: Concepts
Data readiness levels (DRLs) and related assessments are an important part of data analytics. Data readiness levels is a concept where different stages represent the quality and maturity of data. Data science is becoming increasingly popular, but not all companies have the right level of data readiness for this type of work. Performing data readiness levels assessment is important because it gives an insight into the quality and quantity of your current datasets and helps determine future success of the data analytics project. This blog post will explain what data readiness levels are and why assessment tests are important in relation to them. What are data readiness levels? Data readiness …
Data Science / AI Team Structure – Roles & Responsibilities
Setting up a successful artificial intelligence (AI) / data science or advanced analytics practice or center of excellence (CoE) is key to success of AI in your organization. In order to setup a successful data science COE, setting up a well-organized data science team with clearly defined roles & responsibilities is the key. Are you planning to set up the AI or data science team in your organization, and hence, looking for some ideas around data science team structure and related roles and responsibilities? In this post, you will learn about some of the following aspects related to the building data science/machine learning team. Focus areas Roles & responsibilities Data Science Team – Focus …
Sentiment Analysis & Machine Learning Techniques
Artificial intelligence (AI) / Machine learning (ML) techniques are getting more and more popular. Many people use machine learning to analyze the sentiment of tweets, for example, to make predictions related to different business areas. In this blog post, you will learn about different machine learning / deep learning and NLP techniques which can be used for sentiment analysis. What is sentiment analysis? Sentiment analysis is about predicting the sentiment of a piece of text and then using this information to understand users’ (such as customers) opinions. . The principal objective of sentiment analysis is to classify the polarity of textual data, whether it is positive, negative, or neutral. Whether …
Leading & Lagging KPIs – Concepts & Examples
Key performance indicators (KPIs) are important for any organization. They measure the success or failure of an initiative with specific metrics and can be used to make informed decisions about future strategies. However, there is no one single definition of what a KPI is; instead, they come in many forms. KPIs are key metrics for product and project managers and are used to track the success of products and projects. This blog post will explore two types of KPIs – leading KPIs and lagging KPIs – as well as provide some examples. What are KPIs? KPIs are defined as a quantitative measure that indicates the performance of a project or …
ESG Metrics & KPIs: What ESG team Needs to Know
This blog post is geared towards ESG professionals primarily associated with the procurement department in any organization. ESG initiatives are important for organizations to measure their ESG performance. It is of utmost importance to understand ESG KPIs / metrics and how to track ESG metrics. ESGs can help companies improve their operational efficiencies, environmental impact, financial position, governance, transparency, and societal contributions while managing risks. Data analytics can play key role in identifying KPIs, data needed for that KPIs and building dashboards for tracking those KPIs. What is ESG? ESG is an acronym that stands for Environment, Social, and Governance. ESGs encompass issues such as ethics, diversity, social justice, employee …
