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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Gradient Descent Explained: The Engine Behind ML
Gradient descent explained by building it: twenty lines of NumPy fit a regression line and land on the exact least-squares answer, then measurements show what a learning rate that is too small or too large does, why unscaled features need fifty times more steps, and how mini-batches trade noise for speed.
Handling Imbalanced Datasets: Techniques That Work
Imbalanced dataset techniques compared on real screening data where only 2.3% of cases are positive: what class weights, oversampling, SMOTE and undersampling really change, why moving the threshold did better, how a stronger model beat every resampling trick, and how resampling before the split fakes a great score.
Model Deployment: From Notebook to Production
The steps between a model that works in a notebook and one people can rely on: choosing batch or online serving, turning the notebook into a reproducible pipeline, a concrete case of training-serving skew and how a saved pipeline prevents it, a small validated prediction API, and what to monitor after launch.
Natural Language Processing (NLP) Roadmap for 2026
A natural language processing roadmap for 2026: handling raw text, building a classical baseline that is harder to beat than it looks, embeddings and semantic search, transformers and fine-tuning, working responsibly with large language models, and how to evaluate each, with a real baseline and a leakage trap measured.
Overfitting and Underfitting: Causes and Fixes
Overfitting and underfitting diagnosed and fixed on 19,020 real telescope readings: how train and validation scores reveal each problem, how depth limits, pruning, ensembles and early stopping change them, what a learning curve says about collecting more data, and why a 100% training score is not always a problem.
Pandas vs SQL for Data Analysis: When to Use Which
Pandas versus SQL for data analysis, compared on the same real dataset: identical answers from a GROUP BY and a groupby, a moving average as a window function and as rolling, a pivot that is one line in pandas and many in SQL, and why filtering in the database first matters when data is large.