Python for Data Science and Machine Learning Bootcamp

Learn how Python treats variables as memory containers with dynamic typing. A variable holds a single value after assignment, defined without explicit types, with no size limit.

Explore Python’s number types: integers, floats, and complex numbers with real and imaginary parts. Learn how Python handles big integers, division yielding floats, and dynamic typecasting without predefined types.

Learn how to define strings using single or double quotes, understand boolean values with True and False, and recognize reserved keywords that cannot be used as variable names in Python.

Explore implicit and explicit type conversion in Python, including how user input as strings affects arithmetic, and how to cast to int or float to avoid concatenation errors.

Explore arithmetic operators in Python, including addition, subtraction, multiplication, division, floor division, modulo, and exponentiation, with practical examples in Spyder.

Explore Python comparison operators in the Python for data science and machine learning bootcamp, including ==, !=, >, >=, <, and <=, and learn how they return true or false.

Master Python logical operators in data science, using and, or, and not within conditional statements to evaluate true or false outcomes.

Master operator precedence in python by applying brackets, understanding the priority of division, exponent, and other operators, and using a precedence chart to evaluate expressions correctly.

Master control flow with if statements in Python, using user input, comparisons, and explicit type conversion to perform actions, as in a simple calculator project.

Explore the else statement as a control flow tool that evaluates conditions and directs execution to the true or false branch, with examples like if x > 3.

Master the elif (else + if) statement to chain multiple conditions, using else if blocks to handle alternative cases and guide calculator logic.

Create a calculator project that uses control flow statements to perform plus, minus, multiply, and divide operations with user input and integer typecasting, extendable to modulo and floor division.

Explore the for loop in Python, learn how to repeat a block of statements using looping statements, control iterations with range, and execute code multiple times.

Explore while loops by comparing them to for loops, showing how a loop runs until a condition is satisfied, with examples of initializing a counter, incrementing, and printing outputs.

Explore the fundamentals of for loop and while loop in Python, mastering iteration, list-based and external-variable approaches, and controlling flow with break and infinite loop concepts.

Master the break statement to exit a loop when a condition is met, using it in a for loop to stop searching a list of numbers. Explore how break shortens iterations by printing the found value and exiting the loop, illustrating practical use in data science and machine learning workflows.

Learn how to define strings in Python, view their type, and print them; explore single quotes vs double quotes, handling apostrophes, and choosing quotes to avoid syntax errors.

Explore how Python handles strings, index-based access from zero, and locating substrings with find to identify first occurrences.

Learn how to split a string in Python using the split function, with whitespace as the default delimiter, and handle custom delimiters like commas to produce a list of substrings.

Master the strip function in Python to remove extra spaces and trim strings from the extreme left and right, enhancing string handling for data science tasks and competitive programming.

Master essential Python string functions, including strip, substring, isalpha, and concatenation. Learn how upper, lower, and title transforms handle spaces and mixed alphabetic and numeric content.

Explore how Python functions work, including embedded and user defined functions defined with def, calling with parameters, and returning results, with examples like string length and simple interest.

Learn how parameters and arguments define a function signature, and how to pass any number of values using the star operator, with a practical example of summing numbers.

Explore how to define default arguments in Python, learn why optional parameters sit on the right side of a function signature, and see examples using defaults to compute results.

Explore the core data structures in Python, focusing on lists, dictionaries, and heterogeneous data, and learn how to manipulate, define, and store elements efficiently.

Merge two lists using Python's extend method to create a single combined list, showcasing basic list manipulation for data science and machine learning applications.

Explore how Python lists use positive and negative indexing to access elements, including the last and second last items, and compare approaches to retrieving them.

Master Python list manipulations, including append, insert, remove, change, and clear, and learn to use in-built functions for efficient list handling.

Explore enumeration in python? Wait, the instruction says to start with action verbs and keep proper casing. The correct response should be:

Explore merging lists in Python through concatenation and extend, comparing implicit and explicit methods, then sort lists with the sort function in ascending or descending order.

Explore Python list slicing by selecting sublists with start and end indices, starting from zero, to extract specific elements or exclude the last items.

Explore dictionaries in Python, a data structure that maps keys to values like a table. Define dictionaries, assign keys and values, access entries, and iterate with items.

Learn how to add elements to a Python dictionary by assigning a new key with its value using dictionary[key] = value, and view the updated dictionary with three values.

Explore dictionary functions in Python: use keys, values, and items to retrieve keys, values, and key-value pairs from a dictionary; learn how each returns data.

Compare get and index in dictionaries to see how both retrieve a key’s value and how missing keys trigger errors with index.

Update dictionaries in Python using the update method to modify existing key values and extend dictionaries with new pairs, by supplying another dictionary or a set of key-value pairs.

This lecture explains deletion in dictionaries in Python, showing how to delete by key, use the pop item method, and apply clear to remove all entries.

Explore tuples as an immutable, heterogeneous data structure in Python, unlike lists, enabling access without modification and supporting static memory allocation considerations.

Learn how to use the index and count functions on tuples to locate positions, count occurrences, and access values by index for data retrieval in Python.

Learn how to use predefined tuple functions in Python, including length, max, min, delete, and filter, and define a test function to filter values greater than three.

Explore Python sets, a data structure like lists but with no duplicates; learn how to define, initialize, and identify an empty set, and understand unique elements.

Explore how sets are unordered and do not duplicate elements, then access elements through traversal with for loops and use the in keyword for membership checks.

learn how to add elements to a set in python, showing that duplicates are ignored and new values like 7 and 9 are added, while indexing elements is not supported.

Discover how to delete elements in a collection using pop to remove a random element, remove to delete by value, clear to wipe all items, and del for permanent deletion.

Explore set operations on two sequences A and B, including union, intersection, difference, and symmetric difference, highlighting how duplicates are handled and how common values are identified.