IT Career Guides & Free Resources
Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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Top 10 Machine Learning Algorithms Explained for Beginners
The ten machine learning algorithms beginners should know, each explained by how it works, what it is good at and where it fails, plus a head-to-head benchmark of the seven classifiers on three real datasets under identical cross-validation, and what published studies of hundreds of datasets add to that picture.
Machine Learning Interview Questions: Concepts and Answers
Machine learning concept questions are easy to answer with definitions and hard to answer well. Seven core concepts are shown here in numbers from 30,000 real credit card clients: the accuracy trap, overfitting by tree depth, L1 regularisation, thresholds, calibration, cross-validation spread and learning curves.
BigQuery ML: Machine Learning with SQL
BigQuery ML trains and runs machine learning models with SQL, on data that never leaves the warehouse. This guide covers the statements, model types and pricing, then reproduces a logistic regression locally on 300,000 real trips to show what the evaluation numbers mean and three ways an easy model can mislead.
Ensemble Learning: Bagging, Boosting and Stacking Explained
Ensemble learning explained with measurements on one real dataset: bagging steadies an unstable decision tree, boosting turns 89%-accurate stumps into a 97% model, and stacking combines model types, yet plain logistic regression still edges ahead. What each fixes, why diverse errors matter, and how to choose.
Testing Machine Learning Code and Data
ML code can pass every unit test and still ship a broken model, because data and model behaviour need tests of their own. This guide writes nine pytest checks for a real model, covering schema, invariants, plausibility, a baseline gate and behaviour, and one catches a genuine sensor fault in the data.
Containerizing a Machine Learning Model Step by Step
Most container tutorials stop when the container starts. This walkthrough containerizes a real scikit-learn model step by step, then measures what matters: build and rebuild times, image size, the user it runs as, time to healthy, memory, and whether the container's predictions match training exactly.