Learn Python & OpenCV for Computer Vision Deep Learning, OCR

Explore a high level overview of Python, its evolution from launch through the journey of Python, and the reasons it remains the most popular language across industries and technical areas.

Explore what Python is as an interpreted, object-oriented, high-level language with dynamic semantics, open-source and easy to use, featuring a standard library for artificial intelligence, deep learning, and data science.

Trace Python's journey from its 1990s origins in the Netherlands to the 3.x era, highlighting 2.7 stability, list comprehension, garbage collection, and Unicode and performance improvements.

Discover why Python remains powerful, flexible, and user-friendly as machine learning and big data drive its popularity, evidenced by rising Stack Overflow and TIOBE trends.

Take a step-by-step Python setup on Windows and Ubuntu, install libraries and packages, and use Google Colab for notebooks, building a foundation for basic and advanced concepts.

Master Python setup on Windows by installing Python 3.6, Jupyter Notebook, and PyCharm, then create and activate a virtual environment with virtualenv, and install pandas, numpy, matplotlib, and openpyxl.

Set up Python and essential tools on Ubuntu, including Python 3.6, Jupyter Notebook, PyCharm, and virtual environments with virtualenv and venv, then install Pandas, Numpy, Matplotlib, and OpenPyXL.

Use Google Colab to create, upload, and run notebooks with code and shell commands. Mount Google Drive, switch runtimes (CPU, GPU, TPU), and manage data for efficient deep learning workflows.

Explore Python data types and variables, demonstrated in a Jupyter notebook, with hands-on examples of strings, lists, tuples, sets, and dictionaries, followed by arithmetic operators.

Explore variables and data types in Python, including memory location, first-use assignment, and case-sensitive naming rules. Grasp data type classifications and how operations relate to datatypes.

Learn numeric and boolean data types in Python, including integers, floats, and complex numbers, along with variable assignment, print formatting, and basic operations.

Explore how Python strings are defined with single or double quotes, assigned to variables, sliced by indices, and manipulated with methods like upper, lower, strip, replace, split, and format.

Explore the Python list data type: an ordered, changeable collection that supports duplicates, created with square brackets, with indexing, membership testing, and essential methods like append, insert, and sort.

Explain the tuple datatype, its immutability, ordered storage and indexing, how to use tuples as dictionary keys and set elements, and compare them to lists.

Explore data type sets in Python: understand unordered, unindexed collections with no duplicates, learn hash table performance, and apply add, remove, update, union, intersection, and symmetric_difference_update operations.

Learn python dictionaries as ordered, mutable key-value stores with unique keys, including creation, accessing items, nested dictionaries, and core methods such as get, items, keys, values, update, and pop.

Explore Python operators, including arithmetic, comparison, logical, membership, bitwise, and identity operators, plus assignment and ternary patterns, with practical examples.

Explore Python control flow tools by learning if-else, elif, for and while loops, and apply them to lists, tuples, and dictionaries in practical Python scenarios.

Master python if-else logic with boolean expressions and operators like ==, !=, <, and >=, explore elif and nested if, and learn indentation or single-line forms that produce clear outputs.

Master for and while loops in Python to iterate over lists, tuples, dictionaries, sets, and strings, using range, else, nested loops, and break, continue, and pass.

Explore modular programming in Python by learning functions with arbitrary arguments (*args) and keyword arguments (**kwargs), and mastering modules, packages, and file input/output.

Learn how functions organize code by accepting arguments and returning values. It introduces *args and **kwargs for flexible inputs, plus keyword and default parameters, scope, lists, and recursion.

Explore Python modules and packages, create and import modules, use aliases and from-imports, and structure packages with __init__.py and folder conventions to organize code.

Learn Python file I/O fundamentals, including buffered and unbuffered operations, open modes (r, w, a, x), encoding, and basic read, write, seek, and tell usage.

Learn python's industry coding practices, including _main and _name to control execution, master exception handling with try catch blocks, and explore built-in exception types to manage errors.

Learn how Python uses the __name__ and __main__ variables to define a starting point, and how direct runs vs imports alter code flow with first.py and second.py.

Explore how python handles errors with try-except blocks, multiple exceptions, finally and raise, and learn common built-in exception types like TypeError, IndexError, and ModuleNotFoundError.

Explore advanced Python functions, including lambda expressions, map, filter, and reduce, and compare lambda with def while transforming and filtering iterables and reducing to a single value.

Explore advanced Python functions, including lambda expressions, map, filter, and reduce, and learn how to apply anonymous functions as arguments to higher-order functions.

Explore object-oriented methodology in Python, from conceptual foundations to code-level implementation, then master decorators to add functionality and generators to create custom iterators.

Learn object oriented programming in Python by understanding class and object concepts, class and instance variables, and methods, plus inheritance and polymorphism.

Explore object oriented programming in Python by building classes and objects, using __init__ to initialize attributes, and applying inheritance, overriding, and polymorphism with practical examples.

Learn how Python decorators extend function behavior by wrapping functions, using the @ symbol, and building generic decorators with *args and **kwargs.

Explain how Python generators use yield to produce iterators, pause and resume execution, and save memory by generating values on demand.

Discover Python's built-in modules written in C, from DateTime for date and time zones to Math, Random, and Statistics, then OS and Sys for OS interaction and runtime tools.

Explore the Python datetime module to manipulate dates and times, using now, strptime, strftime, ISO week number, timestamp, epoch conversions, and sleep for timed delays.

Explore maths, random, and statistics modules in Python, using built-in functions like min, max, abs, and power, plus pi, sqrt, ceil, floor, and mean, median, mode, stdev, variance.

Explore the sys and os modules in Python, handling command line arguments with sys.argv, accessing sys.maxsize and sys.path, and managing directories with os.getcwd, os.mkdir, os.chdir, listdir, and os.rmdir.