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Python Programming for Beginners: Learn Python in 100 Minute
Rating: 4.4 out of 5(4 ratings)
256 students

Python Programming for Beginners: Learn Python in 100 Minute

Master Python fundamentals from scratch using Jupyter . build your first project to prepare for Data Science, ML and DL
Created byShayan Janati
Last updated 6/2026
English
English [Auto],

What you'll learn

  • Understand Python syntax and write basic programs
  • Use Jupyter Notebook efficiently
  • Create and modify variables
  • Work with integers, floats, strings, and Boolean values
  • Accept user input and process data
  • Use arithmetic, comparison, and logical operators
  • Prepare for more advanced topics such as Data Science, Machine Learning, and Deep Learning

Course content

1 section • 10 lectures • 1h 38m total length

  • Introduction7:00

    Beginner-friendly Python basics in about 100 minutes using Jupyter Notebook; learn what Python is, why it's popular, and how to run code with print, code and markdown cells.

  • lecture 214:23

    Learn how variables store information and are named with descriptive identifiers, using assignment to set values; explore Python types, including integers, floats, strings, booleans, the type function, and type conversion.

  • lecture 38:30

    Learn how user input powers interactive Python programs with the input function. Master arithmetic and boolean operators, modulus for even checks, and comparisons for true/false decisions.

  • lecture 48:41

    Explore how Python stores text as strings, uses zero-based and negative indexing, slices substrings, and employs methods like upper, lower, strip, and f-strings for data preprocessing.

  • lecture 59:34
  • lecture 610:01
  • lecture 710:00

    Learn to write reusable Python functions using def, parameters, and return, follow the DRY principle, and explore modules like random and libraries such as NumPy, Pandas, and TensorFlow.

  • lecture 810:00

    Learn to read and write files in Python using open, with mode choices (r, w, a), and handle errors with try, except, and finally for reliable programs.

  • lecture 97:28

    Learn Python by building a simple expense tracker using functions, file I/O, user inputs, loops, and exception handling.

  • lecture 1013:06

Requirements

  • No prior programming experience required

Description

Python Programming for Beginners: Learn Python in 100 Minutes with Jupyter Notebook

Want to learn Python but don't know where to start?

This course was designed specifically for complete beginners who want a fast, practical, and beginner-friendly introduction to Python programming.

Unlike many Python courses that require installing multiple tools and writing code in traditional IDEs, this course uses Jupyter Notebook exclusively. Lessons are presented using Markdown cells that act as interactive slides, making learning easier, cleaner, and more engaging.

In less than two hours, you'll learn the essential Python concepts used by developers, data scientists, and machine learning engineers.

We start from the very beginning, assuming no prior programming experience.

Throughout the course, you'll learn how to:

• Work with variables and data types

• Receive input from users

• Perform mathematical operations

• Manipulate strings

• Use lists, tuples, dictionaries, and sets

• Write conditional statements and loops

• Create reusable functions

• Read from and write to files

• Handle errors gracefully

• Build a simple Personal Expense Tracker project

Every lecture includes explanations, examples, exercises, and solutions inside Jupyter Notebook.

By the end of this course, you'll have a solid understanding of Python fundamentals and be ready to continue your journey into Data Science, Machine Learning, and Deep Learning.

Whether your goal is becoming a Python developer, analyzing data, training AI models, or simply learning a valuable new skill, this course provides a strong foundation to help you move forward.

Who this course is for:

  • Complete beginners with no programming experience
  • Students interested in Data Science and Artificial Intelligence
  • Future Machine Learning Engineers
  • Learners who prefer interactive Jupyter Notebooks over traditional IDEs

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