What is Machine Learning With Python: From Zero to a Working Model?

Build, test and explain a model that predicts home sale prices, in plain words and with no heavy maths. You build, test and explain real models without drowning in maths. By the end you have trained a model on a real table of home sales, tested it honestly, saved it, and written a plain-English report that shows what it can and cannot do.

Open the Machine Learning With Python: From Zero to a Working Model course page

People who know basic Python and want to build, test and explain their first machine learning models. You do not need a maths background. You do need to be comfortable running a Python script and reading short code.

These are the prerequisites the course lists:

• Basic Python: variables, lists, dictionaries, functions, and running a .py file from a terminal.
• Being able to install a Python package with pip and open a terminal or command prompt.
• Comfort reading a table of rows and columns, like a spreadsheet.
• School-level maths only: averages and percentages. No calculus or linear algebra is needed.

These are the tools and materials the course lists:

• A computer running Windows, macOS or Linux. Any recent laptop is enough. There is no need for a graphics card.
• Python 3.12 or newer, free from python.org. Python 3.11 also works, but it installs older library versions, so some numbers may differ slightly.
• Free libraries installed with pip: scikit-learn, pandas and their dependencies. No paid tool or subscription is needed for any lesson.
• A text editor. VS Code is a free choice. Notepad also works.
• An internet connection for the one-time install and for a one-time download of the project table (about half a megabyte).
• Roughly nine hours in total for the reading, the exercises and the capstone. Spread it over whatever schedule suits you.

A trained model on a real dataset, with a plain-English report. You finish with a working model that predicts the sale price of a home in Ames, Iowa from nine facts about it. It is built with a leak-proof pipeline, compared against a baseline, scored with cross-validation, tuned without peeking, and tested once on rows it never saw. You save it to a file and write a short report for a reader who does not code. The report says how far off the model is, what it leans on, and where it is weak. The finished deliverables are:

• data_card.md: the question, the label, the nine features, the blanks and the limits of the table.
• prep.py and model.py: the preparation code and the pipeline, with every learned step fitted on training rows only.
• results.md: the baseline, line and forest comparison, the cross-validation averages and spreads, the chosen settings, and the single final test score.
• ames_model.joblib and model_notes.txt: the saved pipeline and a note of the library version and training details.
• report.md: a plain-English report with seven parts: summary, question and data, what we did, results, what the model leans on, limits, and how to use it.
• requirements.txt: your library versions so the work can be rebuilt.

Machine Learning With Python: From Zero to a Working Model has 24 lessons in 6 modules, a workbook of 10 items and a capstone project. It is planned at about 9 hours in total. The lesson text runs to roughly 16,700 words.

Machine Learning With Python: From Zero to a Working Model has 6 modules:

• Module 1: What a model is, and your first working one. Learn what a machine learning model is in plain words, set up Python, run your first model, and meet the home-sales table you will use for the whole course.
• Module 2: Prepare the data so a model can learn. Hold back an honest test set, fill blanks, turn words into numbers and put numbers on one scale, all without letting the test rows leak in.
• Module 3: Predict a category: classification. Learn to ask a yes-or-no question of your data, set a baseline, and try three classic classifiers: logistic regression, a decision tree and a random forest.
• Module 4: Predict a number: regression. Fit a line and a forest to predict sale prices, measure the size of the misses in plain units, and learn to spot underfitting and overfitting with a validation set.
• Module 5: Judge your model honestly. Bundle your preparation into a pipeline, score with cross-validation, read precision and recall, and tune settings without ever peeking at the test rows.
• Module 6: Explain it, save it, and write the report. Find out what your model leans on, save it and use it on a new row, learn where it is weak, and finish with a plain-English report that someone who does not code can trust.

All 24 lesson titles, module by module:

• Module 1: What a model is, and your first working one. 1.1 A model is a pattern learned from examples; 1.2 Set up Python and your libraries; 1.3 Your first model: fit, then predict; 1.4 Meet your project table.
• Module 2: Prepare the data so a model can learn. 2.1 Hold back an exam: the train-test split; 2.2 Fill the blanks using only the training rows; 2.3 Turn words into numbers with one-hot columns; 2.4 Put numbers on the same scale.
• Module 3: Predict a category: classification. 3.1 Beat the baseline first; 3.2 Logistic regression: a weighted sum turned into a probability; 3.3 Decision trees: rules a person can read; 3.4 Random forests: many trees vote.
• Module 4: Predict a number: regression. 4.1 Linear regression: the best straight line; 4.2 Measure the size of the misses; 4.3 Forests for numbers, and what they cannot do; 4.4 Underfitting, overfitting and the validation set.
• Module 5: Judge your model honestly. 5.1 Pipelines: prepare and predict in one object; 5.2 Cross-validation: five exams instead of one; 5.3 Precision and recall: the two kinds of mistake; 5.4 Tune settings without peeking.
• Module 6: Explain it, save it, and write the report. 6.1 Which columns does the model lean on?; 6.2 Save your model and use it on a new row; 6.3 Know where the model is weak; 6.4 Capstone: write the plain-English report.

The course is listed at beginner level and planned at about 9 hours in total. It has 24 lessons, each with objectives you can check yourself against.

Lesson 1 is "A model is a pattern learned from examples" (Module 1, What a model is, and your first working one). By the end of it the course says you will be able to:

• Explain in one plain sentence what a machine learning model is.
• Point to the features and the label in any table.
• Tell training apart from predicting.

Machine Learning With Python: From Zero to a Working Model defines 30 terms in plain words: Model, Feature, Label (target), Row, Training (fitting), Predicting, Regression, Classification, Baseline, Accuracy, Training set, Test set, Validation set, Data leakage, Impute, Median, One-hot encoding, Standardising (scaling), Overfitting, Underfitting, Pipeline, Cross-validation, Hyperparameter, Grid search, MAE, R-squared (R2), Precision, Recall, Permutation importance and Virtual environment.

ModelFeatureLabel (target)RowTraining (fitting)PredictingRegressionClassificationBaselineAccuracyTraining setTest setValidation setData leakageImputeMedianOne-hot encodingStandardising (scaling)OverfittingUnderfittingPipelineCross-validationHyperparameterGrid searchMAER-squared (R2)PrecisionRecallPermutation importanceVirtual environment

Machine Learning With Python: From Zero to a Working Model lists 9 official sources that were opened and checked while it was written, from 3 websites.

• scikit-learn.org
• openml.org
• docs.python.org

Machine Learning With Python: From Zero to a Working Model costs $39 (USD) as The Codex, the full written course with its workbook and templates. Studio (narrated video lessons) and the Full Operator path are not on sale yet.

How much do courses cost?

Machine Learning With Python: From Zero to a Working Model was prepared by Apex Flow Labs with AI assistance and checked before it was published. Every code sample, formula and command the course includes was run before it went in, the course records 27 verification checks, and it lists 9 official sources it checked. Do not take the label on trust: open the sources and run the examples yourself.

How Apex Flow Academy checks its courses

It is listed at beginner level, and it asks for this before you begin:

• Basic Python: variables, lists, dictionaries, functions, and running a .py file from a terminal.
• Being able to install a Python package with pip and open a terminal or command prompt.
• Comfort reading a table of rows and columns, like a spreadsheet.
• School-level maths only: averages and percentages. No calculus or linear algebra is needed.

No. Machine Learning With Python: From Zero to a Working Model teaches and checks a skill, but it cannot promise an outcome. The course's own disclosure reads: "Created by Apex Flow Academy with AI assistance. For education only; not legal, tax, financial or medical advice. Results depend on your effort and circumstances."

Yes. Machine Learning With Python: From Zero to a Working Model includes a verifiable Certificate of Completion, issued by Apex Flow Academy when you finish every lesson, resolve every quiz question with at least 50% right on the first try, and tick the capstone checklist. It has a unique ID and a public verification page. It is a completion certificate, not a degree, licence, accreditation or exam result. The Full Operator path, with a final exam and capstone review, opens later with its own separate, stronger credential.

Is there a certificate?