Machine Learning with Python and Statistics

Explore variables as memory locations that store data and track changing values, contrast with constants, and learn naming rules and types like categorical, numerical, and mixed.

Explore categorical variables, distinguishing nominal and ordinal types, with examples such as gender, marital status, loan purpose, home ownership, and loan status, and learn how 0/1 encoding affects analysis.

Explore numerical variables by distinguishing discrete and continuous values, with LendingClub data examples such as number of accounts and interest rates; learn about binary variables and histograms.

Identify and analyze mixed variables that combine numeric and categorical values, including handling missing data, using examples from credit accounts and the Titanic dataset.

Learn how Python lists work as mutable data structures. Master indexing, slicing, and core operations—append, insert, extend, remove, and sort— including nested lists and memory location.

Learn set basics in Python: unique elements, mutable, with add, update, remove, and union, intersection, and minus operations. Then study tuples, which are immutable, index-accessed, and defined by parentheses.

Learn how conditional statements control code execution by evaluating true or false conditions, using simple if constructs and multi-condition logic with examples.

this lecture demonstrates using a python for loop with range to iterate from zero to stop, showing stop is exclusive and how to use step values like 0,2,4.

Learn Python functions: define with def, pass parameters, call with indentation, and understand return behavior, optional returns, and the difference between global and local variables.

Explore Python built-ins like isinstance to check an object's class, and apply map, reduce, and filter to transform lists such as squaring numbers and multiplying sequences.

Explore how to define user defined functions in Python, pass parameters and arguments, set default values, and use keyword arguments to control function behavior.

Explore date, last payment date, and payment date variables, preprocess to extract information, and visualize loans disbursed over time using Lending Club data.

Explore pandas and numpy basics by creating series and dataframes from lists, assigning indexes, and selecting, adding, and dropping columns with standard normal random data.

Learn numpy, the fast python library for scientific computing, creating arrays, ones and zeros, identity matrices, reshaping, linspace, random numbers (uniform and normal), and max, min indices.

Master numpy indexing and selection, including single and multi-element retrieval, slicing, broadcasting, and creating zero-filled matrices, with emphasis on views vs copies and boolean selection.

Explore classes and objects in Python, defining classes with methods and attributes, creating objects, and calling methods either via the class or an instance, while noting indentation.

Explore object oriented programming in python by defining classes as blueprints and creating objects as class instances, with encapsulation, inheritance, and polymorphism guiding this approach.

Understand abstraction in OOPs by hiding implementation details and exposing only essential information through classes and objects, and explore encapsulation with axis specifiers public, private, and protected.

Learn how abstract classes declare abstract methods and cannot be instantiated; implement them in concrete subclasses using inheritance, with the ABC base class and abstractmethod decorators.

Explore how inheritance transfers attributes and functions from a parent class to a child class, and how polymorphism applies the same method in different contexts.

Learn encapsulation by bundling data and methods into a single class, with public and private access, using underscores to denote private members, and applying this in a student example.

Learn how to handle runtime errors with try and catch, manage zero division and type conversion exceptions, and build stable programs by handling multiple and specific exceptions.

Explore how to raise standard and user-defined exceptions, handle them with try-except blocks, and ensure cleanup with finally, including file operations and division-by-zero scenarios.

Explore how web scraping automates data extraction from public data sources while respecting access rules, fueling retail, manufacturing, and equity analysis, enabling real-time analytics and data-driven strategy.

Explore the fundamentals of html, including structure with head and body, tags, attributes, and common elements like hyperlinks, images, and paragraphs, to support data scraping.

Explore web crawling and web scraping with Python, using requests and BeautifulSoup to fetch pages, navigate hyperlinks, and extract data from Hacker News.

Explore multitasking in Python by distinguishing process-based and thread-based multitasking, and learn how to create and manage threads with the threading module, including when to use multithreading.

Explore Python multithreading by creating threads through subclassing or the threading module, synchronize with join, and manage self parameters and run methods to run tasks in parallel.

Connect Python with MySQL by installing the Python MySQL package, setting localhost user credentials, and testing the connection to obtain a connection object.