Adv. Python with Machine Learning

AnExpertise | It Takes You Where You Want To Be Adv. Python with Machine Learning

Advanced Python with ML                                                   

________________________________________________________________________________________________________

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.
  • Djangoframework 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 indeed.com, 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

Contents:-

(Assignment and Live Examples)

Real time examples with live project for Google finance data extractions

Sample resumes helping you to create your resume 

 

Additional Benefits: 

  • We provide real time scenarios examples, how to work in real time projects
  • We guide for resume preparation by giving sample resume
  • Will give you 2 POC (proof Of Concept) with Data set so that you can practice before going for interview
  • We provide hands –on in class room itself so that you can understand concepts 100%
  • We give assignments for weekdays practice

 

 

ADV PYTHON FOR MACHINE LEARNING

 

Module 1: Python Essentials

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

 

Machine learning (ML)

  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. 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 DataFrames
  • Pandas Series
  • Data aggregation with Pandas DataFrames
  • Concatenating and appending DataFrames
  • Joining DataFrames
  • Handling missing data

 

  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
  • CREATE, INSERT, READ, UPDATE and DELETE Operation
  • DML and DDL Oepration with Databases
  • Performing Transactions
  • Handling Database Errors
  • Disconnecting Database

 

  1. Web Scraping in Python

 

  1. Regression based learning
  • Simple regression
  • Multiple regression
  • Logistic regression
  • Predicting house prices with regression
  1. Clustering based learning
  • Defnition
  • Types of clustering
  • The k-means clustering algorithm
  1. Data mining
  • Introducing data mining
  • Decision Tree
  • Affiity Analysis
  • Clustering

 

  1. Classifiation – Sentiment Analysis

 

 

 

 

 

 

 

 

  1. Natural Language Processing
  • Install nltk
  • Tokenize words
  • Tokenizing sentences
  • Stop words with NLTK
  • Stemming words with NLTK
  • Speech tagging
  • Sentiment analysis with NLTK

 

  1. Making Sense of data through Visualization
  • Introducing matplotlib
  • Bar Charts
  • Line Charts
  • Scatter plots
  • Bubble charts

 

  1. Working with openCV
  • Setting up openCV
  • Loading and displaying images
  • Applying image filters
  • Tracking faces
  • Face recognition

 

  1. Performing predictions with Linear Regression
  • Simple linear regression
  • Multiple regression
  • Training and testing model