Category Archives: Machine Learning

Machine Learning Models Evaluation Techniques

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 …

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

Challenges for Machine Learning / AI Projects

Challenges related to Machine Learning Projects Implementations

In this post, you will learn about some of the key challenges in relation to achieving successful AI / machine learning (ML) or Data science projects implementation in a consistent and sustained manner. As AI / ML project stakeholders including senior management stakeholders, data science architects, product managers, etc, you must get a good understanding of what would it take to successfully execute AI / ML projects and create value for the customers and the business.  Whether you are building AI / ML products or enabling unique models for your clients in SaaS setup, you will come across most of these challenges.  Understanding the Business Problem Many times, the nature …

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Machine Learning Programming Languages List

machine learning programming languages

If you’re interested in pursuing a career in machine learning, you’ll need to have a firm grasp of at least one programming language. But with so many languages to choose from, which one should you learn? Here are three of the most popular machine learning programming languages, along with a brief overview of each. Python Python is a programming language with many features that make it well suited for machine learning. It has a large and active community of developers who have contributed a wide variety of libraries and tools. Python’s syntax is relatively simple and easy to learn, making it a good choice for people who are new to …

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Posted in Machine Learning, Programming, Python, R. Tagged with , .

Free Machine Learning Courses from Top US Universities

Online Courses Reskilling

Anyone looking to start learning machine learning has a plethora of resources at their disposal. However, with so many choices it can be difficult to know where to start. This blog post will outline four free machine learning courses from top US universities such as Harvard, Stanford, MIT, etc that are sure to get you on the right track. List of Online Free Courses on Machine Learning The following is a list of online free courses on machine learning from some of the top US universities: Harvard’s CS50p: Intro to Python (cs50.harvard.edu/python/2022/) MIT 6.S191: Intro to Deep Learning (https://introtodeeplearning.com/) Cornell Tech CS 5787: Applied machine learning course (https://cornelltech.github.io/cs5785-fall-2019/) Stanford’s Machine …

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Posted in Career Planning, Machine Learning, Online Courses.

Data Preprocessing Steps in Machine Learning

data preprocessing in machine learning

Data preprocessing is an essential step in any machine learning project. By cleaning and preparing your data, you can ensure that your machine learning model is as accurate as possible. In this blog post, we’ll cover some of the important and most common data preprocessing steps that every data scientist should know. Replace/remove missing data Before building a machine learning model, it is important to preprocess the data and remove or replace any missing values. Missing data can cause problems with the model, such as biased results or inaccurate predictions. There are a few different ways to handle missing data, but the best approach depends on the situation. In some …

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Supply chain management & Machine Learning

supply chain management and AI and Machine Learning use cases

As supply chains become more complex, businesses are looking for new ways to optimize and automate their supply chain operations. One area that is seeing a lot of growth is the use of artificial intelligence (AI) and machine learning in supply chain management. There are many different applications for these technologies in supply chain management, from forecasting demand to optimizing inventory levels. In this blog post, we will explore some of the most interesting use cases for AI and machine learning in supply chain management. What is supply chain management and what are its key components? Supply chain management is the process of coordinating and controlling the flow of goods, …

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Car Insurance & Machine Learning Use Cases

car insurance machine learning use cases

The car insurance industry is one of the many sectors that have been disrupted by the advent of machine learning. In the past, car insurance companies have relied on historical data to set premiums. However, machine learning / AI has enabled insurers to better predict risk and price insurance policies more accurately. As a result, AI / machine learning is transforming the car insurance industry by making it more efficient and customer-centric. In this blog, you will learn about some key car insurance use cases which can be dealt using machine learning. Detecting fraudulent car insurance claims Fraudulent car insurance claims are a problem for both insurers and policyholders. They …

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Posted in Insurance, Machine Learning. Tagged with , .

Bagging vs Boosting Machine Learning Methods

boosting vs bagging differences examples

In machine learning, there are a variety of methods that can be used to improve the performance of your models. Two of the most popular methods are bagging and boosting. In this blog post, we’ll take a look at what these methods are and how they work with the help of examples. What is Bagging? Bagging, short for “bootstrap aggregating”, is a method that can be used to improve the accuracy of your machine learning models. The idea behind bagging is to train multiple models on different subsets of the data and then combine the predictions of those models. The data is split into a number of smaller datasets, or …

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Generative vs Discriminative Models Examples

generative vs discriminative models

f you’re working in the field of machine learning, it’s important to understand the difference between generative and discriminative models. These two types of models are both used in supervised learning, but they approach the problem in different ways. In this blog post, we’ll take a look at what generative and discriminative models are, how they work, and some examples of each. What are Generative Models? Generative models are a type of machine learning algorithm that is used to generate new data samples based on a training set. For example, a generative model could be trained on a dataset of pictures of cats, and then used to generate new cat …

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Weak Supervised Learning: Concepts & Examples

weak supervised learning

Supervised learning is a type of machine learning algorithm that uses a labeled dataset to learn and generalize from. The labels act as supervisors, providing the algorithm with feedback so it can learn to map input data to the correct output labels. In this blog post, we’ll be focusing on weak supervised learning, a subset of supervised learning that uses only partially labeled or unlabeled data. We’ll cover some of the most common weak supervision techniques and provide examples of each. What is Weak Supervised Learning? Weak supervised learning is a type of machine learning where the learner is only given a few labels to work with. Weak supervision is …

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Diabetes Detection & Machine Learning / AI

diabetes diagnosis using machine learning

Diabetes is a chronic disease that affects millions of people worldwide. The early detection of diabetes is crucial to preventing the development of serious complications. However, traditional methods of diabetes detection are often inaccurate and invasive. Machine learning / AI offers a promising solution for the early detection of diabetes. Machine learning algorithms can automatically detect patterns in data and use those patterns to make predictions. Machine learning is well suited for the detection of diabetes because it can handle the large amount of data required for accurate predictions. In addition, machine learning algorithms can automatically identify patterns that are too subtle for humans to discern.  Quick Overview on Machine …

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Posted in Healthcare, Machine Learning. Tagged with , .

Healthcare Claims Processing AI Use Cases

healthcare claims processing use cases AI and machine learning

In recent years, artificial intelligence (AI) / machine learning (ML) has begun to revolutionize many industries – and healthcare is no exception. Hospitals and insurance companies are now using AI to automate various tasks in the healthcare claims processing workflow. Claims processing is a complex and time-consuming task that often requires manual intervention. By using AI to automate claims processing, healthcare organizations can reduce costs, improve accuracy, and speed up the claims adjudication process. In this blog post, we will explore some of the most common use cases for healthcare claims processing AI / machine learning. Automated Data Entry One of the most time-consuming tasks in the claims process is …

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ESG & AI / Machine Learning Use Cases

ESG AI use cases

Environmental, social, and governance (ESG) factors are a set of standards used to evaluate a company’s performance on issues that have an impact on society and the environment. AI or machine learning can be used to help identify these factors. In this blog post, we will explore some use cases for how AI / machine learning can be used in conjunction with ESG factors. The following is a list of AI use cases related ESG. This list will be updated from time-to-time.  Predict ESG ratings using fundamental dataset: Investors (asset managers and asset owners) started to assess companies based on how they handle sustainability issues. To do this assessment, investors …

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Posted in AI, ESG, Machine Learning. Tagged with , , , .

Procurement 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 and the need for digital transformation across procurement organization, 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 business stakeholders such as category managers, sourcing managers, supplier relationship managers, business analysts / product managers, and data scientists implement different use cases using machine learning. Procurement analytics will allow you to use data very effectively in achieving data-driven decision making.  One can get started with procurement analytics with dashboards …

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Neural Network Types & Real-life Examples

deep neural network examples from real-life

Neural networks are a powerful tool for data scientists, machine learning engineers, and statisticians. But what exactly are they? In this blog post, we’ll explore the concept of different types of neural networks, provide real-life examples of how they’re used. By the end of this post, you’ll have a better understanding of how neural networks work and how they can be used to solve complex problems.  Before jumping into examples, you may want to check out some of my following posts on neural networks: Deep Learning Explained Simply in Layman Terms Neural network explained with perceptron example Perceptron explained with Python example Also, lets understand some terminologies which will later …

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Differences: Decision Tree & Random Forest

sample random forest

In machine learning, there are a few different algorithms that can be used for regression and classification tasks. Two of the most popular are decision trees and random forest. Both of these algorithms have their similarities and differences, and in this blog post, we’ll take a look at the key differences between them. What is decision tree algorithm? A decision tree is a machine learning algorithm that can be used for both classification and regression tasks. The algorithm works by splitting the data into smaller subsets, and then using these subsets to make predictions. Each split is based on a decision criterion, such as the purity of the data or …

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