Complete Data Science,Machine Learning,DL,NLP Bootcamp

Begin using the VS Code IDE to run Python and Jupyter Notebook files, create and activate a conda environment with Python 3.12, and install ipykernel.

Create a dedicated environment for your project using three approaches—python command, virtualenv, and conda—with activation, library installation (pandas, numpy), and optional requirements.txt.

Explore python syntax and semantics through practical examples, covering comments, indentation, and common syntax errors. Understand variable assignment and type inference, line continuation, and single versus multi-line comments.

Learn how to declare and assign variables in Python, apply naming conventions and type checking, perform type conversion, and use input and a simple calculator for practical examples.

Explore Python operators by performing arithmetic, floor division, modulus, and exponentiation, along with comparison and logical operations, through practical examples and a calculator project.

Explore Python control flow with if, elif, and else, learn nested conditionals, and solve real-world examples like voting eligibility, leap year, and even/odd checks.

Master loops in python with for and while using range, break, continue, and pass, explore nested loops, and apply practical examples.

Explore tuples in Python: create, access, and manipulate immutable ordered collections through indexing, slicing, concatenation, and repetition. Learn packing and unpacking, count and index methods, and handling nested tuples.

Explore Python sets, a built-in data type for storing unique items. Learn creation, membership tests, and core operations including union, intersection, difference, symmetric difference, and update.

Explore dictionaries in Python by creating and accessing key-value pairs, updating, deleting, and iterating over entries. Learn dictionary methods, nested dictionaries, dictionary comprehension, shallow copy, and merging with unpacking.

Explore real world use cases of lists in Python, including a to-do list manager, inventory tracking, student grade analysis, and collecting user feedback.

Master Python functions by defining and calling them, learning syntax and parameters. Explore default parameters, variable length arguments, positional and keyword arguments, and return statements with docstrings.

Explore practical Python function examples, including temperature conversion, password strength checks, shopping cart total cost, palindrome checks, factorial recursion, word frequency from files, and email validation.

Learn how to define and use lambda functions in python, creating anonymous one-expression functions, and apply them with map and filter for practical examples.

Explore the Python map function, which applies a function to every item in an iterable and returns a map object. See lambda use, multiple iterables, and data transformation.

Use the Python filter function to create an iterator from an iterable that selects items based on a condition, with lambda for even, greater than five, and dictionary data.

Explore how to import modules and packages in Python, use built-in math functions and NumPy, install packages with pip, and create custom packages with __init__.py.

Explore Python’s standard library essentials, from array, math, random, and os to csv, json, and regex, and learn practical file, time, and data serialization techniques.

Explore file operations in Python by reading and writing text and binary files, using read, write, append, and seek to manage content across multiple scenarios.

Learn to create and manage directories with os and os.path, list files, join paths, and determine absolute and relative paths, enabling robust file handling in projects.

Learn Python exception handling in the complete data science bootcamp, featuring try, except, else, and finally blocks to handle errors gracefully and write robust programs.