MEPX
Get started
Chapters
Python114 chapters

Basics

  1. Introduction
  2. Get Started
  3. Syntax
  4. Comments
  5. Variables
  6. Variable Names
  7. Multiple Assignment
  8. Global Variables
  9. Data Types
  10. Numbers
  11. Casting
  12. Booleans
  13. Operators
  14. User Input

Strings

  1. Strings
  2. Slicing
  3. Modify Strings
  4. Concatenation
  5. f-Strings
  6. String Formatting
  7. Escape Characters
  8. String Methods
  9. Text and Unicode

Collections

  1. Lists
  2. List Access
  3. Change Items
  4. Add Items
  5. Remove Items
  6. Loop Lists
  7. List Comprehension
  8. Sort Lists
  9. Copy Lists
  10. Join Lists
  11. List Methods
  12. Tuples
  13. Sets
  14. Dictionaries
  15. Nested Dictionaries
  16. Dictionary Methods
  17. Set Methods
  18. Dict and Set Comprehensions
  19. The collections Module

Control flow

  1. If...Else
  2. Match
  3. While Loops
  4. For Loops
  5. Break and Continue

Functions

  1. Functions
  2. Arguments
  3. args and kwargs
  4. Default Values
  5. Return Values
  6. Scope
  7. Recursion
  8. Lambda
  9. Decorators
  10. Generators
  11. Closures

Classes and objects

  1. Classes and Objects
  2. The __init__ Method
  3. The __str__ Method
  4. Methods
  5. self
  6. Inheritance
  7. Polymorphism
  8. Iterators
  9. Dataclasses
  10. Enums
  11. Magic Methods

Modules and the standard library

  1. Modules
  2. PIP
  3. Dates
  4. Math
  5. JSON
  6. RegEx
  7. itertools
  8. os and sys
  9. Random Numbers

Files and errors

  1. File Handling
  2. Read Files
  3. Write Files
  4. Delete Files
  5. Try...Except
  6. Raising Exceptions
  7. CSV Files
  8. Context Managers
  9. Paths with pathlib

Beyond the basics

  1. Virtual Environments
  2. Type Hints
  3. Async Basics
  4. NumPy Intro
  5. Pandas Intro
  6. Matplotlib Intro
  7. Working with APIs
  8. Testing
  9. Logging
  10. Command-line Arguments
  11. Debugging
  12. SQLite
  13. Packaging

Machine learning

  1. What Machine Learning Is
  2. Your First Model
  3. Features and Labels
  4. Train and Test Split
  5. Classification
  6. Regression
  7. Evaluating a Model
  8. Overfitting and Underfitting
  9. Preprocessing
  10. Pipelines
  11. Clustering
  12. Neural Networks
  13. Saving and Using a Model
  14. Where It Goes Wrong

Exercises

Saving and Using a Model

3 tasks. Write the code, press Check, and the page runs it against a real Python interpreter.

Back to the saving and using a model chapter
Exercise 1

Save the trained pipeline, load it back, and predict with the loaded one.

Python needs scikit-learn, downloaded on first run
import joblib
from sklearn.datasets import load_iris
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

X, y = load_iris(return_X_y=True)
model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000)).fit(X, y)
# save it, load it, and print a prediction
Exercise 2

Only the estimator was saved, so the scaler is lost. Save the whole pipeline instead.

Python needs scikit-learn, downloaded on first run
import joblib
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

X, y = load_iris(return_X_y=True)

scaler = StandardScaler().fit(X)
bare = LogisticRegression(max_iter=1000).fit(scaler.transform(X), y)
joblib.dump(bare, "model.joblib")

print(joblib.load("model.joblib").predict([[5.1, 3.5, 1.4, 0.2]])[0])
Exercise 3

Save the model together with its feature names and class names, then print the classes from the loaded file.

Python needs scikit-learn, downloaded on first run
import joblib
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

data = load_iris()
model = LogisticRegression(max_iter=1000).fit(data.data, data.target)
# dump a dict holding the model, features and classes, then load and print the classes