Python + Machine Learning Foundations / Python နှင့် ML အခြေခံ — 30-Video Learning Plan

မြန်မာဘာသာ Python ကို အခုမှ စလေ့လာသူများအတွက် programming အခြေခံကနေ tabular data နဲ့ machine learning အခြေခံအထိ အဆင့်ဆင့် လေ့ကျင့်မည့် အဆိုပြုသင်တန်း ဖြစ်ပါတယ်။ 30 ဗီဒီယိုနဲ့ အဆင့်မြင့် ML အားလုံး မပြီးနိုင်သောကြောင့် foundation course အဖြစ် သတ်မှတ်ထားပါတယ်။ ဤသည်မှာ ကြိုတင်လေ့လာနိုင်သော သင်ရိုးအဆိုပြုအစီအစဉ် ဖြစ်ပါတယ်။ ဗီဒီယိုများ အားလုံးတင်ပြီးပြီဟု မဆိုလိုပါ။ သင်တန်းစမည့်ရက်၊ အချိန်နှင့် သင်တန်းကြေးကို နောက်မှ ကြေညာပါမယ်။ English technical terms များကို မြန်မာလို ရှင်းပြသင်ကြားရန် ရည်ရွယ်ထားပါတယ်။ English — Proposed learning plan Python + Machine Learning Foundations / Python နှင့် ML အခြေခံ 30 planned videos; suggested pace: 6–8 weeks. Each video: approximately 18–25 minutes, plus 30–60 minutes of practice. These are planning estimates, not recorded runtimes. Who this is for / prerequisites • Computer Basics skills: files, folders, typing and installing with help • Comfort with basic arithmetic; percentages and graphs introduced in class • A computer with an instructor-tested Python/data environment or an approved hosted notebook What you will practise • Write and debug small Python programs • Load, clean and describe a small tabular dataset • Train and evaluate introductory classification/regression models • Explain held-out testing, leakage and model limitations Final project Build a reproducible, non-sensitive tabular-data project: question, data dictionary, cleaning, train/test split, baseline, one or two models, appropriate metrics, error analysis and a plain-language model card. Lesson plan Python စတင်ခြင်း / Python foundations 01. Course goals and reproducible setup Learn: Explain programs, interpreter, editor/notebook and a controlled course environment; do not promise every newest package works together. Demonstration: Run the instructor-tested environment and a first print statement. Practice: Open, run, save and reopen a hello-world file/notebook. 02. Values, variables and types Learn: Introduce numbers, strings, booleans, assignment and readable names. Demonstration: Build a fictional shop receipt with named values. Practice: Change inputs and predict output before running. 03. Input, conversion and formatting Learn: Teach input, int/float conversion, f-strings and meaningful units. Demonstration: Create a simple unit converter with sample input. Practice: Build a minutes-to-hours converter with labelled output. 04. Operators and comparisons Learn: Teach arithmetic, precedence, equality, inequality and logical expressions. Demonstration: Calculate a fictional basket total and evaluate conditions. Practice: Predict ten short expressions and check results. 05. Conditional decisions Learn: Teach if/elif/else, indentation and edge cases without using real eligibility decisions. Demonstration: Classify toy temperature readings into descriptive bands. Practice: Build a harmless discount example and test boundary values. Logic နှင့် Data structures / Logic and collections 06. Loops with for and range Learn: Teach repeated work, loop variables, range boundaries and accumulators. Demonstration: Sum a short list of synthetic daily amounts. Practice: Print a multiplication table and calculate a total. 07. While loops and termination Learn: Explain condition-controlled loops, progress and controlled stopping. Demonstration: Build a short menu with a clear exit option. Practice: Create a retry loop with a bounded attempt counter. 08. Strings and text processing Learn: Teach indexing, slicing, strip, split and case handling; preserve Unicode. Demonstration: Clean fictional names with extra whitespace. Practice: Parse a short comma-separated example and retain Burmese text. 09. Lists and tuples Learn: Teach ordered collections, iteration, indexing, mutability and common methods. Demonstration: Manage a fictional shopping list and compare a fixed coordinate tuple. Practice: Add, remove and summarise list items without changing the wrong object. 10. Dictionaries, sets and choosing structures Learn: Teach key/value lookup, uniqueness and choosing structures by task. Demonstration: Count word occurrences in harmless sample text. Practice: Create a small inventory dictionary and a unique-item set. Function၊ File နှင့် Project / Programs and files 11. Functions and return values Learn: Teach parameters, return, local scope and small single-purpose functions. Demonstration: Refactor a repeated calculation into a tested function. Practice: Write two conversion functions and call them with edge inputs. 12. Errors, debugging and exceptions Learn: Read tracebacks, distinguish syntax/runtime/logic errors and handle expected failures narrowly. Demonstration: Debug a small broken converter, then handle invalid numeric input. Practice: Repair three prepared errors and explain their causes. 13. Files, CSV and context managers Learn: Teach paths, with open, encoding, CSV headers and preserving original data. Demonstration: Read a small instructor-created UTF-8 CSV into a program. Practice: Summarise the CSV and write a separate output file. 14. Modules, packages and environments Learn: Distinguish import from installation; teach virtual environments and recording dependencies. Demonstration: Use a standard-library module and inspect the supplied pinned environment file. Practice: Recreate the exercise environment from documented instructions. 15. Python mini-project and checkpoint Learn: Combine input, collections, functions, file I/O and simple tests in a small program. Demonstration: Plan a fictional stock-summary tool without completing the assignment. Practice: Build the tool and pass normal, empty and invalid-input tests. ဒေတာနားလည်ခြင်း / Working with data 16. Arrays and vectorised calculations Learn: Introduce NumPy arrays, shape, indexing and elementwise operations; connect to lists. Demonstration: Compute simple measurements from a synthetic numeric array. Practice: Check shape and calculate row/column summaries. 17. Tables with pandas Learn: Teach DataFrame, Series, reading CSV, head, info and column selection. Demonstration: Load a small non-sensitive dataset and create a data dictionary. Practice: Identify rows, columns, types and missing values. 18. Cleaning and missing data Learn: Teach missingness, duplicates, ranges and explicit cleaning rules; avoid arbitrary deletion. Demonstration: Flag invalid synthetic values and document a cleaning choice. Practice: Produce a cleaning log and compare counts before/after. 19. Summaries, percentages and charts Learn: Teach mean/median, counts, proportions, distributions and honest axes. Demonstration: Compare two skewed toy distributions and choose a readable plot. Practice: Make two charts and explain what they do and do not show. 20. ML questions, features and targets Learn: Distinguish supervised/unsupervised learning and classification/regression; avoid sensitive real-world decisions. Demonstration: Frame a toy flower-classification question and identify X and y. Practice: Write a task statement and feature/target table. ML နှင့် စမ်းသပ်တိုင်းတာခြင်း / Modelling and evaluation 21. Train/test split before learning Learn: Teach held-out evaluation, appropriate random versus grouped/time splits and reproducible seeds. Demonstration: Split an approved toy dataset and inspect the separation. Practice: Save split choices and explain why the test set is held out. 22. Baseline and first classifier Learn: Teach fit/predict workflow and a simple baseline; use only the training subset for fitting. Demonstration: Compare DummyClassifier with one simple classifier on a toy dataset. Practice: Report both results with split details. 23. Classification evaluation Learn: Explain confusion matrix, precision, recall and class imbalance with small counts. Demonstration: Calculate metrics for synthetic predictions by hand, then with a library. Practice: Choose a metric for a harmless toy sorting task and justify it. 24. First regression model Learn: Explain continuous targets, simple linear regression, residuals and MAE with units. Demonstration: Fit a small synthetic numeric example and compare to a mean baseline. Practice: Calculate held-out MAE and explain its unit. 25. Preprocessing pipelines and leakage Learn: Teach train-only learned transformations, imputation/scaling and Pipeline; leave final test data untouched. Demonstration: Put preprocessing and a model into one pipeline. Practice: Identify leakage in three prepared flawed examples. Project နှင့် ရှင်းပြခြင်း / Responsible project work 26. Overfitting and model complexity Learn: Contrast training/validation results; introduce a small decision tree and controlled complexity. Demonstration: Compare shallow and deep trees using training-only validation. Practice: Explain which model generalises better on the supplied evidence. 27. Cross-validation and careful tuning Learn: Introduce training-set cross-validation, a small parameter grid and an untouched final test. Demonstration: Compare a few settings within cross-validation. Practice: Record a chosen setting, selection metric and final one-time test result. 28. Capstone: question, dataset and plan Learn: Teach dataset licence, provenance, row meaning, consent/privacy and non-sensitive project scope. Demonstration: Review a model project plan and data dictionary. Practice: Submit a feasible question, approved dataset, split plan and baseline. 29. Capstone: model, errors and limitations Learn: Combine documented cleaning, pipeline, baseline, model, evaluation and error inspection. Demonstration: Demonstrate an honest model card including poor cases. Practice: Finish a reproducible notebook and describe at least three limitations. 30. Project presentation and next learning path Learn: Assess code, reproducibility, evaluation and explanation; distinguish this foundation from advanced ML/deployment. Demonstration: Show a clear five-minute project walkthrough and questions. Practice: Submit notebook, environment instructions, report and model card; answer review questions. Scope: Python and tabular machine-learning foundations. Deep learning, production MLOps and advanced mathematics are later courses. Complete the Python checkpoint before starting ML; use only approved, non-sensitive datasets. Study approach Watch one lesson, pause to repeat the demonstration, finish the practice task, and explain the idea in your own words. Computer Basics can follow one video per day for 30 days. The other tracks need flexible lab and revision time; 30 videos do not mean 30 days. Source references for instructor verification https://docs.python.org/3/tutorial/ https://scikit-learn.org/stable/getting_started.html https://scikit-learn.org/stable/common_pitfalls.html Prepared for 21CT, October 21, 2026. This is a proposed syllabus; enrollment details will be announced separately.