How to Set Up Python for AI & ML Assignments – A Complete Guide by Tutors India
                     HOW TO SET UP 
YOUR PYTHON 
ENVIRONMENT FOR AI & 
A BeginnMer’s LGui deA to SGetStingI SGtartNed wMith AEI/ML Using Python NTS
Presented by:
Tutors India –  Your Research Writing 
Partner
INTRODUCTION
Artificial Intelligence (AI) Machin
Learning and e 
from healt(hMcLa)re atore robtroatnicssfo. rming industrie
s
This guide helps you:
Create a clean and structured 
Python environment
Work with real 
datasets Train your 
first models
Complete 
assignments 
confidently
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WHY ENVIRONMENT 
SETUP IS IMPORTANT
1 3
Avoids Compatibility Facilitates Reproducibility
Problems Get consistent results across 
Ensures your code runs smoothly systems
2 4
Enhances Professional Practice
Concentration Mirrors real-world industry workflows
Focus on learning, not 
debugging
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WHY PYTHON?
Simple syntax—easy to learn
Rich libraries: NumPy, Pandas, 
Scikit- learn, TensorFlow, PyTorch
Excellent documentation & 
community support 
Integrates well with data 
and visualization tools
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INSTALL PYTHON
To begin working on AI and ML assignments, 
you need to install Python programming 
language that will be used throughout the 
work.
Go to: https://www.python.org 
Download Python 3.13.3 
Check: "Add Python to PATH" 
Test installation with:
python --version
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CHOOSE A CODE 
EDITOR (IDE)
IDE Best For Highlights
Jupyter Notebook Beginners Interactive, ideal for 
VS Code Intermediate users learning Lightweight, 
PyCharm Professional projects customizable Full-featured 
Python IDE
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CREATE A A virtual environment helps you keep your project dependencies isolated from the 
VIRTUAL system Python, ensuring that your project uses only the packages you install.
ENVIRONMENT
1 2 3
Create a virtual Activate the virtual Deactivate the virtual 
environment: environme
In  your  terminal  or  To   start  
n tu:sing   this   isolated environment:
command prompt,  navigate  to  environment, you need to When you’re done working in 
your  project directory  and  run  activate it: the environment, you can 
the  following command: deactivate it by simply running:
On Windows: deactivate
python -m venv ai_env\Scripts\activate
ai_env This you to the
This creates a new folder On macOS/Linux: returns system
named ai_env,  which  contains  source ai_env/bin/activate Python.
a  self- contained Python 
environment. Once activated, your  terminal  
will show the name of the 
environment, like this:(ai_env).
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INSTALL CORE 
LIBRAOncRe yoIuEr virStual environment is activated, you 
can install the essential libraries required for AI 
and ML.
1.Install core libraries:
These libraries will help you manipulate data, visualize it, and build machine learning models. Open 
your terminal and run the following command:
pip install numpy pandas  matplotlib seaborn scikit-learn jupyterlab
2.For deep learning projects:
you’ll need additional libraries like TensorFlow and Keras (or PyTorch if you prefer). To install them, use:
For TensorFlow + Keras: Or for PyTorch:
pip install tensorflow pip install torch torchvision
keras
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TEST YOUR 
To verifSy thEat TeverUythinPg is set up correctly, run a basic Python script 
to check if the core libraries were installed successfully.
Checking input: Expected Output:
Open a Python script or Jupyter Notebook If all libraries are installed correctly, you should 
and enter the following code: see this printed on the screen:
import numpy as Environment is ready for AI and 
np import pandas ML assignments!
as pd
from sklearn.linear_model 
import LinearRegression
print("Environment is ready 
for AI and ML assignments!")
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FOLDER STRUCTURE & 
REQUIREMENTS FILE
A good folder structure helps you 
stay organized and manage your 
projects efficiently.
Here’s a  recommended folder structure for your AI and ML 
assignments:
ML_Assignment/
├── data/ #  Store your datasets here
├── notebooks/ #  Store Jupyter Notebooks for 
├── models/ analysis #  Store your trained models
├── results/ #  Store results like graphs, evaluation 
├── requirements.txt scores #  List of installed libraries for 
└── README.md reproducibility
#  Project description, usage, and any 
dependencies
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REAL-WORLD EXAMPLE 
[STUDENT PERFORMANCE]
Export results 
Visualize using Train a Evaluate 
Load Preprocess as PDF 
Matplotlib/ model (e.g., using 
dataset with Scikit- notebook aset 
Seaborn Decision accuracy or 
using learn using Pandas
Tree) F1 Score
Pandas
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COMMON ERRORS AND 
FIXES
Error Cause Fix
ModuleNotFoundError Missing pip install 
Reinstall Python with "Add 
Python not library PATH to PATH"
recognized not set pip install notebook
Check TensorFlow 
Notebook won’t Jupyter not CUDA version
launch CUDA not installed 
found Mismatched 
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drivers
BEST 
PRACTICES & 
CONCLUSION
• Use Markdown for 
documentation
• Use consistent naming
• Track experiments clearly
• Clear outputs before submission
• Backup your work
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