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Python Basic to Advance

About this course

Python training for AI, ML, and data science focuses on equipping participants with essential skills to manipulate data, build predictive models, and deploy machine learning algorithms. Participants learn Python programming fundamentals, data analysis libraries like Pandas, and visualization tools such as Matplotlib and Seaborn. Training covers machine learning concepts including supervised and unsupervised learning, regression, classification, and clustering algorithms. Advanced topics include deep learning frameworks like TensorFlow and PyTorch for neural network development. Practical projects and exercises provide hands-on experience in data preprocessing, model training, evaluation, and deployment. Python training for AI, ML, and data science prepares participants for roles as data scientists, machine learning engineers, or AI developers, enabling them to solve complex problems and derive actionable insights from data using Python’s versatile capabilities.

Course Outline

Module 1:

Overview of Python
  • What is Python?
  • Interpreted Language
  • Downloading and Installing
  • Which version of Python
  • Where to find documentation

 

Module 2:

Python Environment
  • Structure of Python script
  • Using the interpreter interactively 
  • Running standalone scripts under Linux and Windows

 

Module 3:

Python Basics
  • Data types
  • Sequences
  • Mapping types
  • Program structure
  • Files and console I/O
  • Conditionals
  • Loops
  • Built-ins

 

Module 4:

Operating System Access
  • The OS module
  • Environment variables
  • Launching external commands
  • Walking directory trees
  • Paths, directories, and filenames
  • Working with file systems
  • Dates and times
  • Accessing Windows DLL’s

 

Module 5:

Pythonic Programming
  • The Zen of Python
  • Common idioms
  • Lambda functions
  • List comprehensions
  • Generator expressions
  • String formatting

 

Module 6:

Modules and packages
  • Initialization code
  • Namespaces
  • Executing modules as scripts
  • Documentation
  • Packages and name resolution
  • Naming conventions
  • Using imports

 

Module 7:

Classes
  • Defining classes
  • Instance methods and data
  • Properties
  • Initializers
  • Class and static methods/data
  • Inheritance

 

Module 8:

Metaprogramming
  • Implicit properties
  • globals() and locals()
  • Working with attributes
  • The inspect module
  • Decorators
  • Monkey patching

 

Module 9:

Programmer tools
  • Analyzing programs
  • Using pylint
  • Testing code
  • Using unittest
  • Debugging
  • Profiling and benchmarking

 

Module 10:

Database access
  • The DB API
  • Available Interfaces
  • Connecting to a server
  • Creating and executing a cursor
  • Fetching data
  • Parameterized statements
  • Metadata
  • Transaction control
  • Other DBMS modules

 

Module 11:

Network Programming
  • Sockets
  • Clients
  • Servers
  • Application protocols
  • Forking servers
  • Binary data

 

Module 12:

Multiprogramming
  • When to use threads?
  • The Global Interpreter Lock
  • The threading module
  • Simple threading
  • Sharing variables
  • The queue module
  • Debugging threaded programs
  • Multiprocessing
  • Other alternatives

 

Module 13:

XML and JSON
  • Working with XML
  • DOM and Sax
  • Introducing Element Tree and xml
  • Parsing XML
  • Navigating the document
  • Creating a new XML document
  • JSON
  • Parsing JSON into Python
  • Converting Python into JSON

 

Module 14:

Extending Python
  • About non-Python modules
  • Overview of a C extension
  • Writing C by hand
  • Using SWIG
  • Loading modules with C types

 

$ 150

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Duration

30hrs

Module

14

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$ 150

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8

Module

6