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Introduction Every ML Engineer and Data Scientist must understand the significance of “Hyperparameter Tuning (HPs-T)” while selecting the right machine/deep learning model and improving the performance of the model(s). Making it simple, for every single machine learning model selection is a major exercise and it is purely dependent on selecting the equivalent set of hyperparameters, and allContinue reading “Hyperparameter Tuning and its Techniques in Machine Learning”

Introduction to Cosmos DB Hello! Data Engineers, I am sure this simple article will help you guys better understand Cosmos DB from Azure with nice features. Recently many customers have been looking forward to implementing the Data Migration into Cosmos DB. Before getting into deep dive, let’s understand Data Engineering. Let’s understand the Data andContinue reading “Exploring Azure Cosmos DB and Its Capabilities for Data Migration”

Guys! I hope you all 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 are dealing with NLP based problem statement, we mustContinue reading “Top 20 Data Science and Machine Learning Projects in Python (Part-II)”

Introduction I believe all you’re familiar with the terminology DevOps for these many years, this is the complete culture and process life cycle of CI/CD. Yes! This is the best fit for the traditional software that is managed in the production environment very effectively with a well-defined strategic path by Development and Operational team members,Continue reading “MLOps for Machine Learning Engineers”

Synopsis of Time Series Analysis A Time-Series represents a series of time-based orders. It would be Years, Months, Weeks, Days, Horus, Minutes, and Seconds A time series is an observation from the sequence of discrete-time of successive intervals. A time series is a running chart. The time variable/feature is the independent variable and supports theContinue reading “Time Series Analysis”

Young and dynamic data science and machine learning enthusiasts are all very interested in making a career transition by learning and doing as much hands-on learning as possible with these technologies and concepts as Data Scientists Machine Learning Engineers Data Engineers or Data Analytics Engineers. I believe they must have the Project Experience and aContinue reading “Twenty Projects in Data Science Using Python (Part-I)”

“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 andContinue reading “Machine Learning Model Selection strategy for Data Scientists and ML Engineers”

Pandas Panel + Data = Pandas Provides high-level data structures and functions. Ability to translate complex operations with data using simple commands. Methods for grouping, combining data, and filtering, as well as time-series functionality. Re-indexing, Iteration, Sorting, Aggregations, and Concatenations. Easy to reshape, slice, and dice the data. Execution time is very Fast and Expensive.Continue reading “Introduction To Python – Pandas”

Introduction Especially Python libraries for Data Science, Machine Learning models are very interesting, easy to understand, and absolutely you can apply straight away and you can feel the insight of the data and realize/visualize the nature of the data set. Even the complex algorithms can be implemented in two or three lines of code, allContinue reading “Explore More on Python Packages for Data Science”

Guys! If you’re beginners, No worries too much with detail…. Let’s have simple then you can build your confidence. Here I am narrating the high-level difference and understanding and steps to implement the same. Supervised learning is simply a process of learning algorithms from the training dataset. Unsupervised learning is modeling the hidden structure in the data in order to learnContinue reading “Quick Reference for Supervised Vs Unsupervised Learning”