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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

Regression

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

Back to the regression chapter
Exercise 1

Fit a line to the study hours and print the slope, rounded to one place.

Python needs scikit-learn, downloaded on first run
from sklearn.linear_model import LinearRegression

X = [[1], [2], [3], [4], [5]]
y = [52, 55, 61, 64, 68]
# print the slope
Exercise 2

Print the mean absolute error on the test set, rounded to one place.

Python needs scikit-learn, downloaded on first run
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=0
)
model = LinearRegression().fit(X_train, y_train)
# print the MAE
Exercise 3

Show that R2 is 0 for a model that always predicts the mean.

Python needs scikit-learn, downloaded on first run
from sklearn.metrics import r2_score

actual = [10, 20, 30, 40]
# print the R2 of always predicting the mean