Choose a topic to start learning by building.
syntax, data types, control flow, functions, OOP, modules, file handling, error handling, libraries, testing, and deployment.
Supervised, unsupervised, model evaluation, deployment.
Neural nets, CNNs/RNNs, transformers, training tips.
Detection, tracking, segmentation, OCR projects.
Pandas, NumPy, visualization, statistics.
Queries, joins, window functions, best practices.
HDFS, MapReduce, Hive commands with examples.
PySpark, DataFrames, MLlib on real datasets.
APIs, CRUD apps, auth, and deployment.
MVT, admin, DRF basics, production checklists.
Dashboards, DAX, data modeling & storytelling.
Branching, pull requests, team workflows.
Hands-on build guides for students & beginners.
Practice problems: loops, functions, OOP, file I/O, and small projects.
Hands-on datasets for preprocessing, modeling, and evaluation.
Build CNNs, fine-tune transformers, and troubleshooting tasks.
Object detection, segmentation, OCR practice problems.
Pandas tasks, aggregation, time-series, and visualization prompts.
Query puzzles: joins, window functions, and optimization.
HDFS + Hive tasks, MapReduce thought-exercises, and CLI practice.
PySpark DataFrame transformations and mini-projects.
Small API exercises: endpoints, auth, and deployment checks.
Model design, admin customizations, and DRF endpoints.
Dataset modeling, DAX mini-challenges, and storytelling tasks.
Rebase/merge scenarios, PR review exercises, and branching workflows.