Author Archives: Ajitesh Kumar
Top Healthcare Data Aggregation Companies
Data aggregation is the process of collecting data from multiple sources and compiling it into a single database. This process is essential for healthcare professionals, companies and startups because it allows them to track and analyze patient data, which can be used to improve patient care. There are many companies that offer healthcare data aggregation services. However, not all of them are created equal. To help you choose the right company for your needs, we’ve compiled the following list of the top healthcare data aggregation companies. This list will be updated from time-to-time. Athenahealth: Athenahealth is a healthcare data aggregation company that provides electronic health records, practice management software, and …
How to Prepare for a Python Interview
Python has become the most popular programming language in the world and is one of the most in-demand languages by employers. It is a widely used high-level interpreted programming language. Its design philosophy emphasizes code readability and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java. The language provides constructs intended to enable clear programs on both a small and large scale. As such, if you’re a Python programmer, you’re likely to face stiff competition when applying for jobs. In order to increase your chances of landing an interview, it’s important to be well prepared. In …
ESG & AI / Machine Learning 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 …
Checklist for Training Deep Learning Models
Deep learning is a powerful tool for solving complex problems, but it can be difficult to get started. In this blog post, we’ll provide a checklist of things to keep in mind when training and evaluating the deep learning models and deciding whether they are suitable to deploy in production. By following this checklist, you can ensure that your models are well-trained and ready to tackle real-world tasks. Validation of data distribution The distribution of data can have a significant impact on the performance of deep learning models. When training a model, it is important to ensure that the training data is representative of the distribution of the data that …
Machine Learning with Limited Labeled Data
One of the biggest challenges in machine learning is having enough labeled data to train a model. This is especially true for supervised learning tasks such as image classification, where a large dataset is often required. However, what do you do when you only have limited labeled data? In this blog post, we will discuss some of the following techniques that can be used to train machine learning models when you only have limited labeled data. Self-supervised learning Semi-supervised learning Weakly-supervised learning Active learning Few/zero-shot learning Transfer learning Challenges with Machine Learning models trained with limited labeled data When a machine learning model is trained with limited labeled data, it …
List of Machine Learning Topics for Learning
Are you looking for a list of machine learning topics to learn more about? If so, you’ve come to the right place. In this post, we will share a variety of machine learning topics that you can explore to boost your knowledge and skills. So, whether you’re a data scientist or machine learning engineer, there’s something here for everyone. The following represents a list of topics which can be taken up for learning and mastering artificial intelligence / machine learning: Introduction to data science Introduction to machine learning Check out this detailed post on machine learning concepts & examples. Introduction to deep learning Introduction to reinforcement learning Introduction to linear …
Model Compression Techniques – Machine Learning
In recent years, there has been an explosion of interest in machine learning (ML). This is due in large part to the availability of powerful and affordable hardware, as well as the development of new ML algorithms that are able to achieve state-of-the-art results on a variety of tasks. However, one of the challenges of using ML is that many algorithms require a large amount of data and computational resources in order to train a model that generalizes well to new data. To address this challenge, a number of model compression techniques have been developed that allow for the training of smaller, more efficient models that still achieve good performance …
What are Features in Machine Learning?
Machine learning is a field of machine intelligence concerned with the design and development of algorithms and models that allow computers to learn without being explicitly programmed. Machine learning has many applications including those related to regression, classification, clustering, natural language processing, audio and video related, computer vision, etc. Machine learning requires training one or more models using different algorithms. Check out this detailed post in relation to learning machine learning concepts – What is Machine Learning? Concepts & Examples. One of the most important aspects of the machine learning model is identifying the features which will help create a great model, the model that performs well on unseen data. …
K-Nearest Neighbors (KNN) Python Examples
If you’re working with data analytics projects including building machine learning (ML) models, you’ve probably heard of the K-nearest neighbors (KNN) algorithm. But what is it, exactly? And more importantly, how can you use it in your own AI / ML projects? In this post, we’ll take a closer look at the KNN algorithm and walk through a simple Python example. You will learn about the K-nearest neighbors algorithm with Python Sklearn examples. K-nearest neighbors algorithm is used for solving both classification and regression machine learning problems. Stay tuned! Introduction to K-Nearest Neighbors (K-NN) Algorithm K-nearest neighbors is a supervised machine learning algorithm for classification and regression. In both cases, the input consists …
Role of Data in Digital Transformation
In order to understand the role of data in digital transformation, it is important to first understand what digital transformation is. Digital transformation is the process of using digital technologies to create new or improved business processes, products, or services. This can be done through the use of big data, cloud computing, mobile technologies, and the Internet of Things (IoT). Data is a key enabler of digital transformation. It helps organizations to identify new opportunities, make better decisions, and improve operational efficiency. Big data, in particular, is playing an increasingly important role in digital transformation initiatives. Big data refers to large volumes of data that can be structured, unstructured, or …
How to Identify Use Cases for AI / Machine Learning
As artificial intelligence (AI ) and machine learning (ML) solutions and technologies continue to evolve, more and more businesses are looking for ways to incorporate them into their operations to realize a greater business impact. But with so many potential applications, it can be difficult to know where to start. In this blog post, we’ll outline some tips for identifying AI / ML use cases. We’ll also provide a few examples of how AI & machine learning can be used in business settings. So if you’re thinking about adding AI or machine learning to your toolkit, read on! This blog post will be appropriate for product managers, business analysts, data science …
Predicting Customer Churn with Machine Learning
Customer churn, also known as customer attrition, is a major problem for businesses that rely on recurring revenue. Customer churn costs businesses billions of dollars every year, and it’s only getting worse as customers become more and more fickle. In fact, it’s been estimated that the average company loses 10-15% of its customers each year. That number may seem small, but it can have a huge impact on a company’s bottom line. Fortunately, there’s a way to combat churn: by using machine learning to predict which customers are likely to churn. In this blog post, we’ll discuss how customer churn prediction works and why it’s so important. We’ll also provide …
Stacking Classifier Sklearn Python Example
In this blog post, we will be going over a very simple example of how to train a stacking classifier machine learning model in Python using the Sklearn library and learn the concepts of stacking classifier. A stacking classifier is an ensemble learning method that combines multiple classification models to create one “super” model. This can often lead to improved performance, since the combined model can learn from the strengths of each individual model. What are Stacking Classifiers? Stacking is a machine learning ensemble technique that combines multiple models to form a single powerful model. The individual models are trained on different subsets of the data using some type of …
Decision Tree Hyperparameter Tuning Grid Search Example
The output prints out grid search across different values of hyperparameters, the model score with best hyperparameters and the most optimal hyperparameters value. In the above code, the decision tree model is train and evaluate our for each value combination and choose the combination that results in the best performance. In this case, “best performance” could be defined as either accuracy or AUC (area under the curve). Once we’ve found the best performing combination of hyperparameters, we can then train our final model using those values and deploy it to production. Conclusion In this blog post, we explored how to use grid search to tune the hyperparameters of a Decision …
Reinforcement Learning Real-world examples
In this blog post, we’ll learn about some real-world / real-life examples of Reinforcement learning, one of the different approaches to machine learning where other approaches are supervised and unsupervised learning. Reinforcement learning is a type of machine learning that enables a computer system to learn how to make choices by being rewarded for its successes. This can be an extremely powerful tool for optimization and decision-making. It’s one of the most popular machine learning methods used today. Before looking into the real-world examples of Reinforcement learning, let’s quickly understand what is reinforcement learning. Introduction to Reinforcement Learning (RL) Reinforcement learning is an approach to machine learning in which the agents …
Passive Aggressive Classifier: Concepts & Examples
The passive aggressive classifier is a machine learning algorithm that is used for classification tasks. This algorithm is a modification of the standard Perceptron algorithm. The passive aggressive classifier was first proposed in 2006 by Crammer et al. as a way to improve the performance of the Perceptron algorithm on linearly separable data sets. In this blog, we will learn about the basic concepts and principles behind the passive aggressive classifier, as well as some examples of its use in real-world applications. What is the passive aggressive classifier and how does it work? The passive aggressive classifier algorithm falls under the category of online learning algorithms, can handle large datasets, …
I found it very helpful. However the differences are not too understandable for me