
Discover why Python is versatile and popular, with open source access and simple syntax. Leverage cross-platform compatibility, extensive libraries for data analysis, machine learning, AI, and frameworks.
Install Jupyter notebook via the Anaconda distribution to support data science projects, machine learning and artificial intelligence, choosing between the distribution installer or miniconda, with libraries like TensorFlow and PyTorch.
Launch and explore the Jupyter Notebook from Anaconda, start the local server, navigate the interface, and create and run code cells to perform simple Python calculations.
Learn to use the Jupyter notebook, run cells with Ctrl+Enter, Shift+Enter, or Alt+Enter, manage files and cells, and switch between markdown and code for quick Python practice.
Explore various Python integrated development environments, including IDLE, Jupyter, Spyder, VS Code, PyCharm Community, Atom, Sublime, and Eclipse, and learn to write, test, and debug code across interfaces.
Download and install PyCharm Community Edition, a free Python IDE from JetBrains, set up by creating a project, selecting a Python version, and using the terminal.
Learn Python variable rules: variables are case sensitive, may include alphanumeric characters and underscores, must not start with a number, include spaces or special symbols, and avoid keywords.
Explore Python comparison operators, including greater than, less than, and equal checks, distinguish between assignment and equality, and practice booleans in the Jupyter notebook to build conditional logic.
Convert Fahrenheit to centigrade using the standard formula, read user input, print the centigrade result, and apply if-else logic to suggest a doctor for temperatures above 37 or rest otherwise.
Learn assignment operators, including pre- and post-increment and decrement, and compound forms like plus-equal and multiply-equal, with practical examples.
Explore a real-life bank balance update using assignment operators to handle deposit and withdrawal from an initial 500 USD, with a conditional check for sufficient funds and an updated balance.
Discover Python's operator precedence and how parentheses control evaluation. Learn the left-to-right order for division, multiplication, addition, and subtraction with practical examples.
Explore Python data types and data structures, from integers, floats, strings, and booleans to lists, tuples, sets, and dictionaries, with numpy for arrays.
Explore Python strings, covering properties, indexing and slicing, built-in functions like len, max, min, dot methods, and string definitions with single, double, or triple quotes, noting immutability and duplicates.
Learn string concatenation and repetition in Python, using single or double quotes, spaces, and prefixes. Understand converting numbers to strings for mixed concatenation and building full names.
Learn indexing and slicing in Python by accessing elements with position numbers and slicing with start:end:step on data like strings. Use forward and reverse indexing, negative indices, to extract data.
Explore python string methods through the string class and dot notation. Learn key methods such as capitalize, upper, lower, title, and swap case with practical examples.
Write a program that converts the search input to lowercase to enable caseless comparison, matching queries to stored python course names in a database and reporting when found.
Explore Python's inbuilt string methods, including zfill for left padding with zeros, center, rjust, and ljust for alignment, and the count method to tally occurrences.
Explore string methods such as index, replace, and split to locate first occurrences, replace Python with Java, and split text into word tokens based on patterns.
Explore indexing and slicing in Python lists through forward and reverse access using zero and negative indices. Modify list elements without loops with practical examples in a Jupyter notebook.
Explore list and string operations in python by printing course names, capitalizing the first letters of words, replacing artificial intelligence with AI, and squaring the number 30.
Learn how Python lists use inbuilt methods like append, extend, and insert to modify ordered, mutable lists, including end-of-list behavior with practical car examples.
Explore Python list methods, including pop, remove, and clear, to delete elements by index or value. Learn how list ordering and first occurrence affect removals and practice with examples.
Explore Python list methods by copying elements into a new list, counting occurrences, finding index positions, and sorting or reversing the list to see ascending and descending orders.
Compare lists and tuples in Python: lists are mutable, tuples immutable; both support indexing, slicing, and concatenation, so use lists for changing data and tuples for fixed data.
Explore Python sets that store unique objects, disallow duplicates, and remain unordered, enabling add or remove operations and union-based concatenation.
Explore set methods in Python, learn how to add single elements with add, update multiple elements with update, and understand that sets are unordered and enforce uniqueness.
Explore set methods in Python: discard vs remove, including when to use each to avoid errors, and learn how clear and pop affect the set.
Learn to create unique email ids in python using sets and the add method, check before adding to avoid duplicates, and show if an email id exists or is created.
Explore set operations to compare document contents, find intersections and differences between set one and set two, and observe common points, unique elements, and symmetric differences.
Remove duplicates from a list by converting to a set, then reconverting, noting that sets are unordered; or preserve order with a for loop that appends unseen items.
Plot Venn diagrams to visualize common and unique elements across datasets using Python libraries like matplotlib, seaborn, and plotly, with practical steps for installation, labels, and changing colors.
Explore Python dictionaries by defining key-value pairs with curly braces, learn that keys must be unique while values can be any type, and index by keys since dictionaries are mutable.
Explore how a duplicate key behaves in a Python dictionary: the latest value overwrites the existing key, updating the dictionary to show NLP for key 11 0 1.
Learn how to merge dictionaries with the double asterisk syntax, handle duplicate keys by the latest dictionary, and access elements via key indexing.
Explore the built-in dictionary methods in Python, including pop, pop item, clear, and copy, with practical examples.
Explore dictionary methods in Python by printing keys, values, and items, and use fromkeys to assign a common value to multiple keys, illustrated with student Python course examples.
Explore nested dictionaries in Python by building a bank customer dataset, accessing and updating nested values, and printing specific details like age and salary.
Map dictionary data to a multi-dimensional data frame using Python and pandas, then export the results to Excel with index set to false.
Master Python conditional logic by learning if statements, else and elif, using boolean expressions, and applying indentation rules for nested conditions to shape program logic.
Learn to write an if statement in Python, using DateTime to get the current year, compute age from year of birth, and accept or reject registration accordingly.
Apply if-else logic to validate a password against guidelines: length at least eight, starts with p, second letter uppercase, and ends with a digit, accepting valid input.
Implement if-elif-else logic to compute a car's average speed from distance and time. Enforce warnings and penalties across defined speed ranges, including fines and license cancellation.
Learn how to write Python for loops to iterate over strings, using an iterator, colon and indentation, printing elements and applying string methods or conditions to selectively process items.
Learn to separate even and odd numbers in a Python list by using modulus, for loops, and list append to two distinct lists.
Explore how for loops work with sets, noting unordered order and removal of duplicates, then convert even numbers to squares and odd numbers to cubes using two sets.
Learn to iterate dictionary items with for loops, extract keys and values using dict.items, and print keys, values, or both while applying filters, such as values starting with s.
Swap dictionary keys with values by iterating over dictionary.items(), creating a new empty dictionary, and printing the final swapped dictionary.
Explore while loops in Python, contrasting with for loops, showing how the condition is checked before each iteration and how statements execute while the condition remains true, repeating until false.
Explain a while loop login system that prompts for username and password, validates credentials, reports success or failure, and repeats until the user chooses to re login or exit.
Learn how to implement nested loops in Python by combining colors and items to produce red apple, red tomato, green apple, and green tomato, using for and while constructs.
Master nested loops by printing tables from 1 to 10 using a while loop with an inner for loop and range, including adjustable start and end table values.
Define a customer data set in Python and build a procedural banking simulator with user ID, password, and mobile OTP authentication. Display demographics and transactions, and support deposits and withdrawals.
Define customer data structures with IDs, passwords, mobile numbers, plus demographic and transactional data, then import random as R, DateTime as dt, and time for login timing and OTP.
Authenticate users by validating a case-insensitive user id, verifying passwords, generating a random six-digit otp for login, then load and display customer details.
Define test data and libraries, then run validation to catch bugs while developing. Simulate a banking workflow with user authentication, otp verification, account access, and balance updates.
Explore Python’s inbuilt range, type, sum, round, and divmod functions. Learn to generate sequences with start, stop, and step, compute sums and remainders, and inspect types.
Explore how the isinstance function checks types and directs operations on lists, sets, and tuples, including append and add methods and immutability.
Map multiple iterables into a zipped structure with the zip function and unzip it back into separate lists using the asterisk to unpack names, ages, and weights.
Create and call user defined functions in Python using def, specify arguments, and return statements, while recognizing inbuilt functions and the standard function syntax.
Explore bounded and unbounded arguments in Python functions, showing how bounded arguments must follow code logic and types, while unbounded ones remain independent of calculations.
Develop a custom function to compute list length, sum, maximum, and minimum without built-in functions, practicing iteration and aggregation logic.
Explore the four main types of Python functions by argument: required and positional, keyword, default, and variable length argument. Learn how combinations and call rules affect values and execution.
function for calculate the tax amount.
if customer income is upto 800000 - no tax ,
800000 above and below 1200000 - 10% tax,
1200000 above and below 1600000 - 15% tax
# 1600000 above and below 2000000 - 20% tax
# 2000000 above and below 2400000 - 25% tax
# above 2500000 then 30% tax
Learn how keyword arguments let you pass named parameters in any order, providing values like name, age, city, postal code, and account type to a bank account function.
Explore variable length arguments in Python, learning why they are needed beyond default and keyword arguments. Define a function that greets any number of people and prints greetings.
Learn to implement a Python factorial function using an iterative loop, range, and a starting initializer, validating with five and ten factorials.
Create a Python function to generate the fibonacci sequence up to a given number by iterating with a and b starting at 0 and 1, appending values to a list.
Master the Python os and math modules: learn to get current working directory, create and change directories, list files, and perform pi, sqrt, power, trig, log, and factorial operations.
Explore the Python random module: import it, inspect its methods, generate floats and integers, use the choice method, create one-time passwords, pick random items from a list, and shuffle data.
Explore the Python date and time module, import datetime as dt, print current date and time, and manipulate dates with timedelta for future or past calculations.
Learn to create and import a .py module in a Python project, run it in a Jupyter notebook, and explore aggregation and tax calculation functions plus offer price logic.
Learn to install and use Python's third-party libraries with pip, leveraging packages for web frameworks, data visualization, machine learning, and NLP to accelerate development.
Explore a third-party module such as pywhatkit for automating tasks with Python, including sending scheduled WhatsApp messages, emails, Google searches, playing YouTube videos, taking screenshots, and more.
Dear Python Learners,
Welcome to Python Bootcamp 2025–2026: From Beginner to Master!
This course is built on a problem-based learning model, helping you grow from a complete beginner to a confident Python programmer. With over 12.5 hours of on-demand video, 130 engaging lectures, and 100+ hands-on coding exercises and projects, you'll gain both the knowledge and experience you need to succeed.
Each lecture offers:
Clear explanations of Python theory
In-depth coding practice
Guided walkthroughs of real-world problems
As a bonus, we’ll explore advanced Python concepts, machine learning algorithms, and AI applications, preparing you for more cutting-edge paths in tech.
Key Takeaways:
Build a solid foundation in Python and gain hands-on experience
Master programming logic, syntax, and key components
Control program flow and structure code effectively
Execute real-time projects with confidence
Be ready for data analysis, machine learning, and AI development
You’ll find this course engaging, structured, and concept-driven—each topic flows logically into the next to support deep, connected learning.
The curriculum is divided into 12 sections, combining theoretical lessons with applied programming challenges. You'll explore both procedural and functional programming paradigms, gaining a well-rounded understanding of Python’s flexibility.
Wishing you an exciting and rewarding learning journey.
Happy Coding, all the best