Python Basic With Machine Learning

AnExpertise | It Takes You Where You Want To Be Python Basic With Machine Learning

Basic Python with Machine Learning                                                          


Best Python Training with Real-time Project 

Python is a widely used general-purpose, high-level programming language. Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard library.

Audience: Application programmers, automation engineer, testers, system administrators,
web-crawlers and UNIX/NT power users.

Prerequisites: Basic of UNIX or Windows.

For whom Python is?

IT folks who want to excel or change their profile in a most demanding language which is in demand by almost all clients in all domains because of below mentioned reasons-

  • Python is open source (Cost saving)
  • Python has relatively few keywords, simple structure, and a clearly defined syntax. This allows the student to pick up the language in a relatively short period of time.
  • Django framework might be the most famous Python web framework, there is also a host of successful small and micro-frameworks. 

Who use Python?

  • Google makes extensive use of Python in its web search system, and employs Python’s creator Guido van Rossum.
  • The YouTube video sharing service is largely written in Python.
  • Intel, Cisco, Hewlett-Packard, Seagate, Qualcomm, and IBM use Python for hardware testing.
  • JPMorgan Chase, UBS, Getco, and Citadel apply Python for financial market forecasting.
  • NASA, Los Alamos, JPL, use Python for scientific programming tasks.
  • iRobot uses Python to develop commercial robotic vacuum cleaners.
  • The NSA uses Python for cryptography and intelligence analysis.
  • And Many More J

What is the job trend in Python?

As Per the, percentage growth of Python is 700 times more than its peer Languages. 

Python is part of the winning formula for productivity, software quality, and maintainability at many companies around the world.

Who can learn Python?

In short anyone.

  • Automation Engineers | Data analysts and scientist | Web Developers | Networking Professionals | Software Developers | Hadoop programmers | Desktop Applications |Robotics Engineers |Hardware level developers


Module – I PYTHON

1: Introduction

  • What is Python..?
  • A Brief history of Python
  • Why Should I learn Python..?
  • Installing Python
  • How to execute Python program
  • Write your first program

2: Variables & Data Types

  • Variables, Numbers, String
  • Lists ,Tuples & Dictionary

3: Conditional Statements & Loops

  • if…statement, if…else statement
  • elif…statement, The while…Loop
  • The for….Loop

4: Control Statements

  • continue statement
  • break statement
  • pass statement

5: Functions

  • Define function
  • Calling a function
  • Function arguments
  • Built-in functions

6: Modules & Packages

  • Modules
  • How to import a module…?
  • Packages
  • How to create packages

7: Classes & Objects

  • Introduction about classes & objects
  • Creating a class & object
  • Inheritance
  • Methods Overriding
  • Data hiding

8: Files & Exception Handling

  • Writing data to a file
  • Reading data from a file
  • Read and Write data from csv file
  • try…except
  • try…except…else
  • finally
  • os module

Module 2: Machine Learning

  1. Introduction to Machine learning (ML)
  • What is Machine learning?
  • Overview about sci-kit learn and tensor flow
  • Types of ML
  • Some complementing fields of ML
  • ML algorithms
  • Machine learning examples


  1. Getting started with Python Libraries
  • what is data analysis ?
  • why python for data analysis ?
  • Essential Python Libraries
  • Installation and setup
  • Jupyter Notebook
  • 7 VS 3.5
  1. OS
  • Command Line
  • argv
  • argparse module
  1. NumPy Arrays
  • Creating multidimensional array
  • NumPy-Data types
  • Array attributes
  • Indexing and Slicing
  • Creating array views and copies
  • Manipulating array shapes
  • I/O with NumPy
  1. Working with Pandas
  • Installing pandas
  • Pandas data Frames
  • Pandas Series
  • Data aggregation with Pandas DataFrames
  • Concatenating and appending DataFrames
  • Joining DataFrames
  • Handling missing data
  1. Data Loading, Storage and file format
  • Writing CSV files with numpy and pandas
  • HDF5 format
  • Reading and Writing to Excel with pandas
  • JSON data
  • Parsing HTML with Beautiful Soup
  • PyTables
  1. Python Regular Expressions
  • What are regular expressions?
  • The match Function
  • The search Function
  • Matching vs searching
  • Search and Replace
  • Extended Regular Expressions
  • Wildcard
  1. Python Oracle Database Access
  • Install the cx Oracle and other Packages
  • Create Database Connection
  • DML and DDL Operation with Databases
  • Performing Transactions
  • Handling Database Errors
  • Disconnecting Database
  1. Python Multithreaded Programming
  • What is multithreading?
  • Starting a New Thread
  • The Threading Module
  • Synchronizing Threads
  • Multithreaded Priority Queue