🟣 ML + AI · Lesson 48

Machine Learning Workflow

Machine Learning Workflow क्या है?

Machine Learning Workflow ka matlab hai: The ML workflow includes data collection, cleaning, feature selection, splitting, model training, evaluation and saving. Simple words me, ye topic practical Python programs likhne me direct use hota hai.

Is topic ko sirf definition ke liye nahi, balki following correct ML steps jaise real examples ke liye practice karein.

यह क्यों सीखना जरूरी है?

Ye following correct ML steps me kaam aata hai.
Ye avoiding data leakage se bhi connected hai.
Isse aap code ka output aur errors better samajh paate hain.

Important Terms

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

Basic idea: pehle data तैयार करें, phir Python logic apply करें, aur finally result display करें.

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.

Practical Uses

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

Program ko ml-workflow.py file me type karke run karein.
Values change karke output compare karein.
following correct ML steps par ek छोटा example banayen.
Logic ko apne words me 5 lines me likhein.

सारांश

Machine Learning Workflow ko tab complete maanenge jab aap iska meaning, example, output aur practical use clearly explain kar saken.

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