🟣 ML + AI · Lesson 48

Machine Learning Workflow

What is Machine Learning Workflow?

Machine Learning Workflow means the ML workflow includes data collection, cleaning, feature selection, splitting, model training, evaluation and saving.

In real programs, this topic helps in following correct ML steps. Learn the idea first, then type the program yourself and compare the output.

💡 At a Glance

Point	Details
Course Area	Machine Learning + AI Concepts used for prediction, classification, clustering and AI-based projects.
Main Use	following correct ML steps
Example File	`ml-workflow.py`
Practice Focus	Run, change values, and explain the output line by line.

Why should you learn this?

It is useful for following correct ML steps.
It connects with avoiding data leakage.
It improves your ability to read, write and debug Python programs.

Important Terms

These terms are used directly in this lesson. Understand them before memorising the code.

Term	Meaning
dataset	Collection of data used for analysis or model training.
preprocessing	Preparing data before model training.
training	Process where a model learns from data.
evaluation	Checking model performance after training.
deployment	Putting a trained model into practical use.

Syntax / Basic Pattern

The simple pattern is: prepare data, apply the concept, then show the result.

Basic Pattern

steps = [
    "Collect data",
    "Clean data",
    "Split data",
    "Train model",
    "Evaluate model",
    "Save and deploy"
]

Complete Example Program

Python – ml-workflow.py

steps = [
    "Collect data",
    "Clean data",
    "Split data",
    "Train model",
    "Evaluate model",
    "Save and deploy"
]

for step in steps:
    print("ML Step:", step)

Expected Output

ML Step: Collect data ML Step: Clean data ML Step: Split data ML Step: Train model ML Step: Evaluate model ML Step: Save and deploy

Program Explanation

steps = [ stores a value in steps.
"Collect data", performs the next step of the program logic.
"Clean data", performs the next step of the program logic.
"Split data", performs the next step of the program logic.
"Train model", performs the next step of the program logic.
"Evaluate model", performs the next step of the program logic.
"Save and deploy" performs the next step of the program logic.

Where will you use it?

Following correct ml steps.
Avoiding data leakage.
Moving from data to model to evaluation.

Common Mistakes

Training and testing the model on the same data.
Using an algorithm without understanding the input features.
Reporting only accuracy without checking actual mistakes and limitations.

Practice Tasks

Type the program in ml-workflow.py and run it.
Change input values or sample data and observe the new output.
Create one example related to following correct ML steps.
Write 5 lines explaining the logic in your own words.

Summary

Machine Learning Workflow is not a theory-only topic. You should be able to explain the meaning, write the example, run it successfully, and use it in a small practical program.