Explainable Artificial Intelligence (XAI) for AI & ML Engineers
This article will provide you with the required intangible view on EXPLAINABILITY TECHNIQUES for machine learning (ML), along with key explanation methods and their approaches, which are required for the stakeholders and consumers to understand the transparency and interpretability of algorithms beyond their scope. Generally speaking, there are multiple questions on the benefits of AI and ML…
Modern Generative AI with ChatGPT and OpenAI Models – Review
Generative AI with ChatGPT and OpenAI are major milestones and unavoidable tools. These technologies are taking every industry to different levels and huge benefits to building ground-breaking applications, in this book the author has given the essence of generative AI along with the fundamentals of generative AI, ChatGPT and OpenAI precisely for new learners in…
Exploring Data as a Service (DaaS) in Data Engineering and Data Science
As we know that cloud services came into the picture, during the late Y2K, and we started speaking PasS, SaaS, IaaS, etc. I hope you all know those three main pillars of cloud computing with the suffix “as a service” and each has its own degree of capabilities and responsibilities. The digital industries are growing…
K-Fold Cross Validation Technique and its Essentials
Introduction Guys! Before getting started, just have a look at the below visualization and tell me, what are your observations? Yes, here we’re monitoring the performance of the model before moving into production. Why is this necessary for the ML space? Of course, this is a very important stage during model accuracy validation, whatever you…
Feature Store for Machine Learning
As we know that the Data-driven decision-making has become key to the success of any domain in this digital business, Herer Machine Learning plays a vital role in achieving that and helping every industry. In the ML life cycle, the Feature Engineering stage is one of the major and critical to make any kind of…
A Comprehensive Guide on Data Modeling Techniques in Morden Datawarehouse for AI & BI
Introduction Hello, data enthusiast! In this article let’s discuss “Data Modeling” right from the traditional and classical ways and aligning to today’s digital way, especially for analytics and advanced analytics. Yes! Of course, last 40+ years we all worked for OLTP and followed by we started focusing on OLAP. After the cloud ear come into…
Top 20 Data Science and Machine Learning Projects in Python (Part-II)
Guys! I hope you all are enjoyed reading my earlier article Part – I 10/20, and I trust that would be useful for you. Let’s discuss the rest of the project quickly. 11. Learn to prepare data for your next machine learning project. Problem Statement & Solution When you’re dealing with NLP based problem statement, we must…
Model Selection strategy for Data Scientists and ML Engineers
“Thus learning is not possible without inductive bias, and now the question is how to choose the right bias. This is called model selection.” ETHEN ALPAYDIN (2004) p33 (Introduction to Machine Learning) There are many more definitions concerning Model Selection. In this article, we are going to discuss Model Selection and its strategy for Data Scientists and…
Python Libraries for Data Handling Techniques in Data Science
Introduction Data Engineers and Data Scientists need data for their Day-to-Day job. Of course, It could be for Data Analytics, Data Prediction, Data Mining, Building Machine Learning Models Etc., All these are taken care of by the respective team members and they need to work towards identifying relevant data sources, and associated with the business problems.…
Pandas and NumPy for Data Science (Part -II)
Learn More On Advance Pandas and NumPy for Data Science (Part -II) Cont……. Welcome Back to the Part II article. Hope you all enjoyed the content coverage there, here will continue with the same rhythm and learn more in that space. We have covered Reshaping DataFrames and Combining DataFrames in Part I. Working with Categorical data: …
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