SkillBrain Python Homework Fork

This repository contains advanced Python homework assignments organized under assignments/hw_adv. The modules progress from Python fundamentals to data science, dashboards, computer vision, and deep learning projects.

Table of Contents

• Repository Layout
• Quick Start
• Assignment Descriptions
• Assignment Scope Map
• Notes

Repository Layout

• assignments/hw_adv: main homework modules (a_03 to a_40 ranges)
• course: course materials and references
• sessions: session exercises and checkpoints
• hw_templates: templates and starter structures
• requirements-all.txt: broad dependency snapshot used across work

Quick Start

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements-all.txt

Some assignments also provide local requirements.txt files for isolated environments.

Assignment Descriptions

a_03_loops

Covers loop control, runtime complexity tradeoffs, and geometry rendering both in terminal and plot form.

a_04_collections

Implements practical collection-driven mini systems and includes similarity/embedding datasets used for recommendation-style tasks.

a_05_functions

Refactors multi-step exercises into reusable functions and modules, including utility-style helper code.

a_06_oop

Focuses on OOP design and experimentation around class structure, inheritance behavior, and perception-themed data.

a_07_recap

Builds a quiz authoring + playing workflow with persisted JSON quiz banks and a GUI interface.

a_08_9_data_struct

Combines sorting/search fundamentals with performance comparisons on larger synthetic workloads.

a_10_fleet_game

Scope focused on extending an already built game by integrating new agent behavior. This assignment adds two agents, restructures the UI and gameplay flow, introduces autoplay mode, and simplifies controls for a smoother play loop.

a_11_hashing

Hands-on hashing practice for indexing, duplicate detection, counters, and custom hash structures.

a_12_caching_lru

Explores caching patterns from manual dictionaries to LRU and custom decorators, including timing comparisons.

a_13_14_numpy

Introduces core NumPy operations then escalates into advanced array transformations and broadcasting patterns.

a_15_numpy

Connects NumPy-based tensor intuition to neural-network preparation concepts.

a_16_pandas

Builds and prepares DataFrames from raw arrays and applies transformation/export workflows.

a_17_pandas

Implements a richer cleaning pipeline with temporal slicing, pivot operations, NA strategy, and reusable functions.

a_18_matplotlib

Beginner charting exercises with multiple saved visual outputs and dashboard composition.

a_19_matplotlib

Advanced plotting assignment with confidence intervals, heatmaps, and styled dashboards.

a_20_21_seaborn

A guided seaborn curriculum with progressive exercises and dataset exploration.

a_22_plotly

Interactive/animated Plotly exercises with shareable HTML outputs and configuration experiments.

Selected lightweight exports:

• ex1_scatter_age_experience.html (~10 KB)
• ex2_budget_dashboard.html (~10 KB)
• ex3_team_boxplot.html (~12 KB)
• ex4_random_heatmap.html (~9 KB)
• challenge2_animated_scatter.html (~25 KB)

Skipped in README due to size:

• ex5_scatter_share_full.html (~4.7 MB)

a_23_basic_dashboard

Entry-level Streamlit app with table filters and KPI-style summary metrics.

Live app: basic-dashboard-c.streamlit.app

a_24_advanced_analytics

Adds interactive analytics controls, correlation exploration, and grouped aggregation views.

Live app: advanced-analytics.streamlit.app

a_25_professional_dashboard

Moves to a professional dashboard style with sidebar controls and management-oriented summaries.

a_26_db_integration

Integrates Streamlit with SQLite for dataset persistence, query execution, and round-trip reads.

Live app: db-integration.streamlit.app

a_27_leet_code

Notebook-based algorithm drills spanning arrays, strings, validation problems, and search patterns.

a_28_oos_computer_vision

Experimental deepface sandbox combining detection pipelines for smoke/fire, face verification, and person/face camera detection.

a_28_opencv

Operational-style surveillance system with motion/smoke alerts, event logging, and image snapshot evidence.

a_29_face_recognition

A larger surveillance architecture with configurable CLI run mode, GUI control panel, event logging, and recognition workflow.

a_30_facial_expression_pipeline

End-to-end facial-expression ML pipeline from data generation and labeling through features, training, and realtime inference tools.

a_31_iris_classification

Classic sklearn baseline project comparing SVM and Random Forest with saved models.

a_32_advanced_regression

Structured regression benchmark for real-estate prices with preprocessing pipelines and model diagnostics.

a_33_algorithmic

Contrasts hardcoded domain rules with machine learning on a medical classification dataset.

a_34_p1

Performs text token frequency analysis and renders a stylized word cloud artifact.

a_35_0_spam_ham

NLP classification project spanning baseline vectorizers, static embeddings, and automated model comparison reports.

a_35_1_mnist

MNIST training plus an interactive digit-drawing recognizer with interpretability visualizations.

a_36_iris_classification

TensorFlow-based iris pipeline with configurable hyperparameters, early stopping, and reproducibility settings.

a_37_cnn_fashion

Fashion-MNIST CNN workflow including training, evaluation graphics, and error analysis visuals.

a_39_40_yolo_on_feed

Live-stream YOLO detection app with PySide6 UI controls, model selection, and periodic detection snapshots.

Assignment Scope Map

Module	Scope	Primary Files
a_03_loops	loop/control-flow exercises, PIN lockout logic, ASCII/emoji geometry drawing, and plotting helpers	tema3.py, tema_3.ipynb
a_04_collections	collections-heavy tasks: playlist/budget/activity assistants plus hobby similarity datasets and analysis	tema_4_0_anunt.md, tema_4_*.ipynb
a_05_functions	functional refactors of earlier tasks plus helper package (painting_on_water)	tema_05_*.py, painting_on_water/*.py
a_06_oop	OOP-oriented modeling and exploration (mro, class behavior), with supporting data assets	senses.py, explore/*.py
a_07_recap	quiz generation and GUI playthrough backed by JSON question banks	quiz_maker.py, quiz_gui.py, quizes.json
a_08_9_data_struct	data structures and algorithms: sorting, binary search, and complexity comparisons	tema_8_9.py, tema_8_9.ipynb
a_10_fleet_game	package-based fleet routing game with CLI, pygame UI, and pytest suite	main.py, main_pygame.py, tests/*
a_11_hashing	hashing techniques: duplicate detection, counters, hashmap, autocomplete	hashing.ipynb
a_12_caching_lru	manual cache, LRU patterns, decorators, and cache performance comparisons	caching_lru.ipynb
a_13_14_numpy	beginner and advanced NumPy exercises including broadcasting/vectorization	13_task_beginner.ipynb, 14_task_advanced.ipynb
a_15_numpy	NumPy bridge toward neural-network concepts	numpy_advanced_intro_to_neural_nets.ipynb
a_16_pandas	DataFrame fundamentals, preparation, transformation, and export pipeline	16_task_prepare.ipynb
a_17_pandas	cleaning pipeline on temporal data, pivoting, missing value strategy, reusable cleaning funcs	17_task_clean.ipynb
a_18_matplotlib	beginner plotting tasks and exported chart/dashboard artifacts	18_task_beginner.ipynb, export/*
a_19_matplotlib	advanced matplotlib dashboards, heatmaps, confidence visuals, and style variants	19_task_advanced.ipynb, export/*
a_20_21_seaborn	structured seaborn curriculum and implementation notebook	seaborn_task.MD, seaborn_task.ipynb
a_22_plotly	interactive/animated Plotly charts and HTML export workflows	22_plotly.ipynb, 22_plotly_1.py, export/*
a_23_basic_dashboard	Streamlit dashboard basics: filters, salary range controls, and KPI cards	streamlit_app.py, app_utils.py
a_24_advanced_analytics	interactive analytics dashboard: scatter, correlation matrix, grouped summaries	streamlit_app.py, app_utils.py
a_25_professional_dashboard	professionalized Streamlit layout: sidebar controls, payroll views, distribution charts	streamlit_app.py, app_utils.py
a_26_db_integration	Streamlit + SQLite pipeline with SQL querying and import/export flow	streamlit_app.py, employees.db
a_27_leet_code	curated LeetCode notebook set (Two Sum, Binary Search, DP, arrays/strings basics)	leet_code.ipynb
a_28_oos_computer_vision	experimental CV stack: fire/smoke detection, DeepFace verification, YOLO person/face detection	fire_detect/*.py, match_face/*.py, person_detect/*.py
a_28_opencv	OpenCV surveillance + smoke detection system with logging, snapshots, and warmup exercises	opencv_surveillance_project/surveillance_system.py
a_29_face_recognition	modular surveillance platform (CLI + PySide6 control center) with YOLO integration and registry	surveillance_core.py, surveillance_app.py, control_center_pyside6.py
a_30_facial_expression_pipeline	facial-expression pipeline: synthetic image generation, landmarks extraction, model training, realtime inference	README.md, extract_landmarks.py, train_model*.py, realtime_emotion_detection.py
a_31_iris_classification	classical ML iris benchmark (SVM + Random Forest), evaluation, model serialization	train_iris.py, evaluate_and_visualize.py
a_32_advanced_regression	real-estate regression benchmark with preprocessing pipelines, model comparison, diagnostics	train_models.py, run_full_analysis.py, IMPROVING_TRAINING.md
a_33_algorithmic	rule-based medical classifier versus Random Forest comparison on breast cancer data	breast_cancer_algorithmic_vs_ml.py
a_34_p1	word-frequency analysis and stylized wordcloud generation from text corpus	word_freq.py, wordcloud_output.png
a_35_0_spam_ham	spam/ham NLP project: NB baselines, static-embedding classifier, automated comparison reporting	README.md, spam_detect.py, train_spam_ham_static_v2.py
a_35_1_mnist	MNIST dense/CNN training, interactive PySide digit recognizer, activation/focus visualizations	train_tensorflow.py, train_cnn.py, digit_recognizer_app.py
a_36_iris_classification	TensorFlow iris classifier with config-driven finetuning and reproducible training settings	iris_classification.py, finetune_config.py
a_37_cnn_fashion	Fashion-MNIST CNN training, test visualizations, confusion/misclassification analysis	fashion_cnn_train.py, fashion_cnn_test_visuals.py
a_39_40_yolo_on_feed	PySide6 + YOLO live feed application (YouTube/stream source), detection overlays, snapshot saves	youtube_feed_pyside6_yolo.py, yolo_feed_config.py

Notes

• Assignment folders are intentionally heterogeneous: some are notebook-first, others are full Python projects with tests and app UIs.
• For CV/ML-heavy modules (a_28+ and a_30+), prefer per-folder requirements.txt and local READMEs where present.