Python is the open source
high-level programming language which provides constructs intended to enable
clear programs. It support multiple programming paradigms like object-oriented
and functional programming and features a dynamic type system and automatic
memory management. Python acts as a shell which is highly extensible and
implementations can function as a command line interpreter. It is used by data
scientists for building and using machine learning applications and also used
by youtube ad dropbox. Python online training help students gain expertise in
machine learning and master the concepts of python.
MindMajix is the globally
interactive professional Python programming training institute enhance skills
to fetch web content using python. The organized curriculum covers the
fundamentals to advanced concepts like writing scripts, sequence and file operations,
Web Scraping, Map Reduce and Python UDF for Pig and Hive. Students learn about
packages like pydoop, scikit, scipy etc. with project-based examples by 10+
years experienced skilled professional
Course Curriculum
Unit 1: Introduction of Python
Topics - Overview of Python, Advantages/Disadvantages of Python, pydoc.Starting Python, Interpreter PATH, Using the Interpreter, Running a Python Script, Python Scripts on UNIX/Windows, Python Editors and IDEs. Using Variables, Keywords, Built-in Functions, Strings, Different Literals, Math Operators and Expressions, Writing to the Screen, String Formatting, Command Line Parameters and Flow Control.
Unit 2: Sequences and File Operations
Topics - Lists, Tuples, Indexing and Slicing, Iterating through a Sequence, Functions for all Sequences, Using Enumerate(), Operators and Keywords for Sequences, The xrange()function, List Comprehensions, Generator Expressions, Dictionaries and Sets.
Unit 3: Deep Dive - Functions, Sorting, Errors and Exception Handling
Topics - Functions, Function Parameters, Global Variables, Variable Scope and Returning Values. Sorting, Alternate Keys, Lambda Functions, Sorting Collections of Collections, Sorting Dictionaries, Sorting Lists in Place. Errors and Exception Handling, Handling Multiple Exceptions, The Standard Exception Hierarchy, Using Modules, The Import Statement, Module Search Path, Package Installation Ways
Unit 4: Regular Expressions, it's Packages and Object Oriented Programming in Python
Topics - The Sys Module, Interpreter Information, STDIO, Launching External Programs, Paths, Directories and Filenames, Walking Directory Trees, Math Function, Random Numbers, Dates and Times, Zipped Archives, Introduction to Python Classes, Defining Classes, Initializers, Instance Methods, Properties, Class Methods and Data, Static Methods, Private Methods and Inheritance, Module Aliases and Regular Expressions
Unit 5: Debugging, Databases and Project Skeletons
Topics - Debugging, Dealing with Errors, Using Unit Tests. Project Skeleton, Required Packages, Creating the Skeleton, Project Directory, Final Directory Structure, Testing your Setup, Using the Skeleton, Creating a Database with SQLite 3, CRUD Operations, Creating a Database Object.
Unit 6: Machine Learning Using Python - I
Topics - Introduction to Machine Learning, Areas of Implementation of Machine Learning, Why Python, Major Classes of Learning Algorithms, Supervised vs Unsupervised Learning, Learning NumPy, Learning Scipy, Basic plotting using Matplotlib. In this module we will also build a small Machine Learning application and discuss the different steps involved while building an application.
Unit 7: Machine Learning Using Python - II
Topics - Classification Problem, Classifying with k-Nearest Neighbours (kNN) Algorithm, General Approach to kNN, Building the Classifier from Scratch, Testing the Classifier, Measuring the Performance of the Classifier. Clustering Problem, What is K-Means Clustering, Clustering with k-Means in Python and an Application Example. Introduction to Pandas, Creating Data Frames, Grouping, Sorting, Plotting Data, Creating Functions, Converting Different Formats, Combining Data from Various Formats, Slicing/Dicing Operations.
Unit 8: Scikit and Introduction to Hadoop
Topics - Introduction to Scikit-Learn, Inbuilt Algorithms for Use, What is Hadoop and why it is popular, Distributed Computation and Functional Programming, Understanding MapReduce Framework, Sample Map Reduce Job Run.
Unit 9: Hadoop and Python
Topics - PIG and HIVE Basics, Streaming Feature in Hadoop, Map Reduce Job Run using Python, Writing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and MRjob Basics.
Unit 10: Web Scraping in Python and Project Work
Topics - Web Scraping, Introduction to Beautifulsoup Package, How to Scrape Webpages. A real world project showing scrapping data from Google finance and IMDB.
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