🎓 EASYTUTORGUIDE · Complete Python Course

Very beginner → professional Python · 60 chapters · 900 topics

EasyTutorGuide

Chapter 56: Applied Machine Learning

Complete Python lesson for very beginners. Technical words are explained in simple language, and every outline topic includes a practical example, expected output, steps, and practice.

Python application focus: This is a Python application chapter. It shows how Python can be used in applied machine-learning workflows; the main course remains a Python tutorial.

Python CourseVery Beginner Friendly15 TopicsExamples + Output20 Q&A
Chapter 56 · 15 topics
100%

Chapter Overview

This Python tutorial chapter covers Applied Machine Learning through 15 connected topics. Work through the examples in order, check the expected output, and complete the practice after each topic.

  • 56.1 Data Preparation
  • 56.2 Feature Selection
  • 56.3 Feature Engineering
  • 56.4 Train/Test Splitting
  • 56.5 Cross-Validation
  • Plus 10 additional Python topics in this chapter.

56.1 Data Preparation

Data Preparation is part of Applied Machine Learning. In simple language, it means a Python idea used while learning applied machine learning.

It helps Python turn data into calculations, summaries, visual explanations, or predictions. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Data Preparation (a Python idea used while learning applied machine learning).

Python / Practical Example

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
print(len(X_train), len(X_test))

Expected Output

3 1

Step-by-Step Explanation

  1. Separate features from labels.
  2. Split data so evaluation can use examples not used for fitting.
  3. A fixed random_state makes this learning example repeatable.
Practice: Re-type the example for “Data Preparation”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.2 Feature Selection

Feature Selection is part of Applied Machine Learning. In simple language, it means a Python idea used while learning applied machine learning.

It is one building block of applied machine learning and helps you write clearer, more predictable Python programs. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Feature Selection (a Python idea used while learning applied machine learning).

Python / Practical Example

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
print(len(X_train), len(X_test))

Expected Output

3 1

Step-by-Step Explanation

  1. Separate features from labels.
  2. Split data so evaluation can use examples not used for fitting.
  3. A fixed random_state makes this learning example repeatable.
Practice: Re-type the example for “Feature Selection”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.3 Feature Engineering

Feature Engineering is part of Applied Machine Learning. In simple language, it means a Python idea used while learning applied machine learning.

It is one building block of applied machine learning and helps you write clearer, more predictable Python programs. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Feature Engineering (a Python idea used while learning applied machine learning).

Python / Practical Example

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
print(len(X_train), len(X_test))

Expected Output

3 1

Step-by-Step Explanation

  1. Separate features from labels.
  2. Split data so evaluation can use examples not used for fitting.
  3. A fixed random_state makes this learning example repeatable.
Practice: Re-type the example for “Feature Engineering”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.4 Train/Test Splitting

Train/Test Splitting is part of Applied Machine Learning. In simple language, it means a Python idea used while learning applied machine learning.

It makes programs easier to trust, diagnose, change, and maintain as they become larger. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Train/Test Splitting (a Python idea used while learning applied machine learning).

Python / Practical Example

text = "red,blue,green"
print(text.split(","))

Expected Output

['red', 'blue', 'green']

Step-by-Step Explanation

  1. Choose a delimiter.
  2. split() breaks the string at that delimiter.
  3. The result is a list of strings.
Practice: Re-type the example for “Train/Test Splitting”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.5 Cross-Validation

Cross-Validation is part of Applied Machine Learning. In simple language, it means a Python idea used while learning applied machine learning.

It is one building block of applied machine learning and helps you write clearer, more predictable Python programs. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Cross-Validation (a Python idea used while learning applied machine learning).

Python / Practical Example

text = "123"
if text.isdigit():
    value = int(text)
    print(value)

Expected Output

123

Step-by-Step Explanation

  1. Check the input before converting it.
  2. isdigit() confirms these characters are digits.
  3. Convert only after the check passes.
Practice: Re-type the example for “Cross-Validation”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.6 Regression Models

Regression Models is part of Applied Machine Learning. In simple language, it means a Python idea used while learning applied machine learning.

It is one building block of applied machine learning and helps you write clearer, more predictable Python programs. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Regression Models (a Python idea used while learning applied machine learning).

Python / Practical Example

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
print(len(X_train), len(X_test))

Expected Output

3 1

Step-by-Step Explanation

  1. Separate features from labels.
  2. Split data so evaluation can use examples not used for fitting.
  3. A fixed random_state makes this learning example repeatable.
Practice: Re-type the example for “Regression Models”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.7 Classification Models

Classification Models is part of Applied Machine Learning. In simple language, it means a blueprint for creating objects.

It is one building block of applied machine learning and helps you write clearer, more predictable Python programs. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Classification Models (a blueprint for creating objects).

Python / Practical Example

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
print(len(X_train), len(X_test))

Expected Output

3 1

Step-by-Step Explanation

  1. Separate features from labels.
  2. Split data so evaluation can use examples not used for fitting.
  3. A fixed random_state makes this learning example repeatable.
Practice: Re-type the example for “Classification Models”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.8 Tree-Based Models

Tree-Based Models is part of Applied Machine Learning. In simple language, it means a Python idea used while learning applied machine learning.

It is one building block of applied machine learning and helps you write clearer, more predictable Python programs. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Tree-Based Models (a Python idea used while learning applied machine learning).

Python / Practical Example

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
print(len(X_train), len(X_test))

Expected Output

3 1

Step-by-Step Explanation

  1. Separate features from labels.
  2. Split data so evaluation can use examples not used for fitting.
  3. A fixed random_state makes this learning example repeatable.
Practice: Re-type the example for “Tree-Based Models”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.9 Clustering Models

Clustering Models is part of Applied Machine Learning. In simple language, it means a Python idea used while learning applied machine learning.

It is one building block of applied machine learning and helps you write clearer, more predictable Python programs. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Clustering Models (a Python idea used while learning applied machine learning).

Python / Practical Example

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
print(len(X_train), len(X_test))

Expected Output

3 1

Step-by-Step Explanation

  1. Separate features from labels.
  2. Split data so evaluation can use examples not used for fitting.
  3. A fixed random_state makes this learning example repeatable.
Practice: Re-type the example for “Clustering Models”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.10 Model Metrics

Model Metrics is part of Applied Machine Learning. In simple language, it means a Python idea used while learning applied machine learning.

It is one building block of applied machine learning and helps you write clearer, more predictable Python programs. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Model Metrics (a Python idea used while learning applied machine learning).

Python / Practical Example

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
print(len(X_train), len(X_test))

Expected Output

3 1

Step-by-Step Explanation

  1. Separate features from labels.
  2. Split data so evaluation can use examples not used for fitting.
  3. A fixed random_state makes this learning example repeatable.
Practice: Re-type the example for “Model Metrics”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.11 Overfitting

Overfitting is part of Applied Machine Learning. In simple language, it means a Python idea used while learning applied machine learning.

It is one building block of applied machine learning and helps you write clearer, more predictable Python programs. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Overfitting (a Python idea used while learning applied machine learning).

Python / Practical Example

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
print(len(X_train), len(X_test))

Expected Output

3 1

Step-by-Step Explanation

  1. Separate features from labels.
  2. Split data so evaluation can use examples not used for fitting.
  3. A fixed random_state makes this learning example repeatable.
Practice: Re-type the example for “Overfitting”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.12 Underfitting

Underfitting is part of Applied Machine Learning. In simple language, it means a Python idea used while learning applied machine learning.

It is one building block of applied machine learning and helps you write clearer, more predictable Python programs. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Underfitting (a Python idea used while learning applied machine learning).

Python / Practical Example

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
print(len(X_train), len(X_test))

Expected Output

3 1

Step-by-Step Explanation

  1. Separate features from labels.
  2. Split data so evaluation can use examples not used for fitting.
  3. A fixed random_state makes this learning example repeatable.
Practice: Re-type the example for “Underfitting”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.13 Hyperparameters

Hyperparameters is part of Applied Machine Learning. In simple language, it means a name in a function definition that receives a value.

It is one building block of applied machine learning and helps you write clearer, more predictable Python programs. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Hyperparameters (a name in a function definition that receives a value).

Python / Practical Example

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
print(len(X_train), len(X_test))

Expected Output

3 1

Step-by-Step Explanation

  1. Separate features from labels.
  2. Split data so evaluation can use examples not used for fitting.
  3. A fixed random_state makes this learning example repeatable.
Practice: Re-type the example for “Hyperparameters”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.14 Model Pipelines

Model Pipelines is part of Applied Machine Learning. In simple language, it means a Python idea used while learning applied machine learning.

It is one building block of applied machine learning and helps you write clearer, more predictable Python programs. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: Model Pipelines (a Python idea used while learning applied machine learning).

Python / Practical Example

python -m pip install package-name

Expected Output

# Terminal command; output depends on the selected package and environment.

Step-by-Step Explanation

  1. Run package-management commands in a terminal.
  2. python -m pip uses pip associated with that Python interpreter.
  3. Replace package-name with the package you actually intend to install.
Practice: Re-type the example for “Model Pipelines”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

56.15 End-to-End ML Project

End-to-End ML Project is part of Applied Machine Learning. In simple language, it means a Python idea used while learning applied machine learning.

It is one building block of applied machine learning and helps you write clearer, more predictable Python programs. For a very beginner, focus first on what goes in, what Python does, and what comes out; details become easier after you run a small example.

Technical words in simple language: End-to-End ML Project (a Python idea used while learning applied machine learning).

Python / Practical Example

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
print(len(X_train), len(X_test))

Expected Output

3 1

Step-by-Step Explanation

  1. Separate features from labels.
  2. Split data so evaluation can use examples not used for fitting.
  3. A fixed random_state makes this learning example repeatable.
Practice: Re-type the example for “End-to-End ML Project”, change one safe input, predict the result before running it, and explain in one sentence why the result changed.

Common Beginner Mistakes

  • Copying code without predicting what each line does.
  • Ignoring the first useful error message or traceback location.
  • Mixing tabs/spaces or changing indentation accidentally.
  • Using data of the wrong type for an operation.
  • Trying to learn many advanced variations before mastering one small working example.

Chapter Practice

  1. Choose three topics from this chapter and re-type their examples without copying and pasting.
  2. For each example, change one input and predict the output first.
  3. Explain five technical terms from this chapter in your own beginner-friendly words.
  4. Create one small program that combines at least two chapter topics.
  5. Keep notes about errors you made and what fixed them.

Mini Project / Challenge

Create a small Python exercise that combines at least three ideas from Applied Machine Learning. Start with a tiny working version, test it, then improve it one step at a time.

  1. Choose three topics from this chapter.
  2. Write or adapt a small Python example using those topics.
  3. Predict the output before running the code.
  4. Test at least one different input.
  5. Write two sentences explaining what the program does and what you learned.

20 Questions & Answers

1. What is the main goal of Chapter 56?

The goal is to understand applied machine learning through small explanations, examples, and practice.

2. Should I memorize every command or method?

No. Understand the pattern, practice the common form, and learn how to read documentation when you need exact details.

3. Why are the examples small?

Small examples isolate one idea at a time, which makes errors easier to understand and fix.

4. What should I do when an example gives an error?

Read the last part of the traceback, check spelling and indentation, confirm the required package or file exists, and compare the input types with what the operation expects.

5. Why should I predict output before running code?

Prediction forces you to reason about the program instead of only copying it.

6. What should I remember about Data Preparation?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

7. What should I remember about Feature Selection?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

8. What should I remember about Feature Engineering?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

9. What should I remember about Train/Test Splitting?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

10. What should I remember about Cross-Validation?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

11. What should I remember about Regression Models?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

12. What should I remember about Classification Models?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

13. What should I remember about Tree-Based Models?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

14. What should I remember about Clustering Models?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

15. What should I remember about Model Metrics?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

16. What should I remember about Overfitting?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

17. What should I remember about Underfitting?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

18. What should I remember about Hyperparameters?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

19. What should I remember about Model Pipelines?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.

20. What should I remember about End-to-End ML Project?

Remember its beginner meaning, the problem it helps solve, the shape of a small example, and one common situation where it is appropriate.