Python & ML Online Course

OVERVIEW & PURPOSE

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis

OBJECTIVES

1. To understand the concepts and constructs of Python
2. To create own Python programs, know the machine learning algorithms in Python and work on a real-time project running on Python

Python Environment Setup and Essentials:

Introduction to Python Language, features, the advantages of Python over other programming languages, Python installation, Windows, Mac & Linux distribution for Anaconda Python, deploying Python IDE, basic Python commands, data types, variables, keywords and more.

Python language Basic Constructs

Built-in data types in Python, tabs and spaces indentation, code comment Pound # character, variables and names, Python built-in data types, Numeric, int, float, complex, list tuple, set dict, containers, text sequence, exceptions, instances,  classes, modules, Str(String), Ellipsis Object, Null Object, Ellipsis, Debug, basic operators, comparison, arithmetic, slicing and slice operator, logical, bitwise, loop and control statements, while, for, if, break, else, continue.

OOP concepts in Python

How to write OOP concepts program in Python, connecting to a database, classes and objects in Python, OOPs paradigm, important concepts in OOP like polymorphism, inheritance, encapsulation, Python functions, return types, and parameters, Lambda expressions, connecting to database and pulling the data.

Database connection

Understanding the Database, need of database, Installing MySQL on windows, showing databases available in MySQL Database Server, creating a Database in MySQl Workbench and showing it, understanding a MySQL Connector, understanding Database connection using Python.

NumPy for mathematical computing

Introduction to arrays and matrices, indexing of array, datatypes, broadcasting of array math, standard deviation, conditional probability, correlation and covariance.

SciPy for scientific computing

Introduction to SciPy and its functions, building on top of NumPy, cluster, linalg, signal, optimize, integrate, subpackages, SciPy with Bayes Theorem.

Matplotlib for data visualization

How to plot graph and chart with Python, various aspects of line, scatter, bar, histogram, 3D, the API of MatPlotLib, subplots.

Pandas for data analysis and machine learning

Introduction to Python dataframes, importing data from JSON, CSV, Excel, SQL database, NumPy array to dataframe, various data operations like selecting, filtering, sorting, viewing, joining, combining, how to handle missing values, time series analysis, linear regression.

Exception Handling

Introduction to Exception Handling, scenarios in Exception Handling with its execution, Arithmetic exception, RAISE of Exception, what is Random List, running a Random list on Jupyter Notebook, Value Error in Exception Handling.

Multi Threading & Race Condition

Introduction to Thread, need of threads, what are thread functions, performing various operations on thread like joining a thread, starting a thread, enumeration in a thread, creating a Multithread, finishing the multithreads. Understanding Race Condition, lock and Synchronization with lock.

Packages and Functions

Intro to modules in Python, need of modules, how to import modules in python, the import statement, locating a module, namespace and scoping, arithmetic operations on Modules using a function, Intro to Search path, Global and local functions, filter functions, Packages, Python Packages, import in packages, various ways of accessing the packages, Decorators, Pointer assignments, and Xldr.

Web scraping with Python

Introduction to web scraping in Python, the various web scraping libraries, beautifulsoup, Scrapy Python packages, installing of beautifulsoup, installing Python parser lxml, creating soup object with input HTML, searching of tree, full or partial parsing, output print, searching the tree.