Python Training

Python TrainingLycasoft Technologies is the NO.1 Python training institute offering the best Python training in Coimbatore, expert guidance and 100% placement assistance.

Are you seeking a data analyst job? Are you an IT professional longing for a career change in Machine Learning? Are you a data analyst looking to acquire the best Machine Learning project support? Are you a team looking for the best Machine Learning using Python classroom training and real-time hands-on training on Python? Are you looking for a fast track Machine Learning course? Are you willing to do one on one Machine Learning training in Coimbatore? Are you keen to undergo live online training on Machine Learning with Python? Are you a college student interested to learn data analytics? Do you need Machine Learning project support on the job?

If any of these above questions is hitting your mind, Don’t worry…We are here to help you with the Machine Learning course. From day 1 until the course completion, Lycasoft management, and are well-qualified Machine Learning tutors will provide you the unique, supportive and convenient learning environment? So, you can make use of this golden opportunity to learn a technology from scratch until advanced programming.

Python plays an important role in Artificial Intelligence by providing it with good frameworks like sci-kit-learn: machine learning in Python, which fulfills almost every need in this field and D3.js – Data-Driven Documents in JS, which is one of the most powerful and easy-to-use tools for visualization. Other than frameworks, it’s fast prototyping makes it an important language not to be ignored. AI needs a lot of research and hence it is necessary not to require a 500 KB boilerplate code in Java to test a new hypothesis, which will never finish the project. In Python, almost every idea can be quickly validated through 20-30 lines of code (same for JS with libs). Therefore, it is a pretty useful language for the sake of AI.

SECTION 1: INTRODUCTION

  • What’s python?
  • Why do people use python?
  • Some quotable quotes
  • A python history lesson
  • Advocacy news
  • What’s python good for?
  • What’s python not good for?
  • The compulsory features list
  • Python portability
  • On apples and oranges
  • Summary: why python?

SECTION 2: USING THE INTERPRETER

  • How Python Runs Programs
  • How You Run Programs
  • Configuration Details
  • Module Files: A First Look
  • The Idle Interface
  • Other Python Ides
  • Time To Start Coding
  • Lab Session 1

SECTION 3: TYPES AND OPERATORS

  • A First Pass
  • The ‘Big Picture’
  • Numbers
  • Dynamic Typing Interlude
  • Strings
  • Lists
  • Dictionaries
  • Tuples
  • General Object Properties
  • Summary: Python’s Type Hierarchies
  • Built-In Type Gotchas
  • Lab Session 2

SECTION 4: BASIC STATEMENTS

  • General Syntax Concepts
  • Expressions
  • Print
  • If Selections
  • Python Syntax Rules
  • Documentation Sources Interlude
  • Truth Tests
  • While Loops
  • Break, Continue, Pass, And The Loop Else
  • For Loops
  • Comprehensions And Iterations
  • Loop Coding Techniques
  • Comprehensive Loop Examples
  • Basic Coding Gotchas
  • Preview: Program Unit Statements
  • Lab Session 3

SECTION 5: FUNCTIONS

  • Function Basics
  • Scope Rules In Functions
  • More On “Global” (And “Nonlocal”)
  • More On “Return”
  • More On Argument Passing
  • Special Argument Matching Modes
  • Odds And Ends
  • Generator Expressions And Functions
  • Function Design Concepts
  • Functions Are Objects: Indirect Calls
  • Function Gotchas
  • Optional Case Study: Set Functions
  • Lab Session 4

SECTION 6: MODULES

  • Module Basics
  • Module Files Are A Namespace
  • Name Qualification
  • Import Variants
  • Reloading Modules
  • Package Imports
  • Odds And Ends
  • Module Design Concepts
  • Modules Are Objects: Metaprograms
  • Module Gotchas
  • Optional Case Study: A Shared Stack Module
  • Lab Session 5

SECTION 7: CLASSES

  • Oop: The Big Picture
  • Class Basics
  • A More Realistic Example
  • Using The Class Statement
  • Using Class Methods
  • Customization Via Inheritance
  • Specializing Inherited Methods
  • Operator Overloading In Classes
  • Namespace Rules: The Whole Story
  • Oop Examples: Inheritance And Composition
  • Classes And Methods Are Objects
  • Odds And Ends
  • New Style Classes
  • Class Gotchas
  • Optional Case Study: A Set Class
  • Summary: Oop In Python
  • Lab Session 6

SECTION 8: EXCEPTIONS

  • Exception Basics
  • First Examples
  • Exception Idioms
  • Exception Catching Modes
  • Class Exceptions
  • Exception Gotchas
  • Lab Session 7

SECTION 9: BUILT-IN TOOLS OVERVIEW

  • The Secret Handshake
  • Debugging Options
  • Inspecting Name-Spaces
  • Dynamic Coding Tools
  • Timing And Profiling Python Programs
  • File Types And Packaging Options
  • Development Tools For Larger Projects
  • Summary: Python Tool-Set Layers
  • Lab Session 7 Continued

SECTION 10: SYSTEM INTERFACES

  • System Modules Overview
  • Running Shell Commands
  • Arguments, Streams, Shell Variables
  • File Tools
  • Directory Tools
  • Forking Processes
  • Thread Modules And Queues
  • The Subprocess And Multiprocessing Modules
  • IPC Tools: Pipes, Sockets, Signals
  • Fork Versis Spawn
  • Larger Examples
  • Lab Session 8

SECTION 11: GUI PROGRAMMING

  • Python Gui Options
  • The Tkinter ‘Hello World’ Program
  • Adding Buttons, Frames, And Callbacks
  • Getting Input From A User
  • Assorted Tkinter Details
  • Building GUIs By Subclassing Frames
  • Reusing GUIs By Subclassing And Attaching
  • Advanced Widgets: Images, Grids, And More
  • Larger Examples
  • Tkinter Odds And Ends
  • Lab Session 8 Continued

 SECTION 12: DATABASES AND PERSISTENCE

  • Databases and Persistence
  • Object Persistence: Shelves
  • Storing Class Instances
  • Pickling Objects Without Shelves
  •  Using Simple Dbm Files
  • Shelve Gotchas
  • Zodb Object-Oriented Database
  • Python SQL Database API
  • Persistence Odds And Ends
  •  Lab Session 9

 SECTION 13: TEXT PROCESSING

  • String Objects: Review
  • Splitting And Joining Strings
  • Regular Expressions
  • Parsing Languages
  • Regular Expressions
  • Lab Session 10

SECTION 14: INTERNET SCRIPTING

  • Using Sockets In Python
  • The Ftp Module
  • Email Processing
  • Other Client-Side Tools
  • Building Web Sites With Python
  • Writing Server-Side Cgi Scripts
  • Jython: Python For Java Systems
  • Active Scripting And Com
  • Other Internet-Related Tools
  • Lab Session 10

SECTION 15: PYTHON FOR DATA SCIENCE

  • NumPy
  • NumPy Arrays
  • Basic statistics with NumPy
  • Graphical data analysis with Python
  • Cumulative distribution function
  • Plotting data with Python
  • Plotting histogram with Python
  • Data Analysis and Statistical thinking in Python
  • Scrapping the web
  • HTTP Request for importing files and flat files from the web
  • Importing data in python
  • Importing flat files using pandas
  • Importing flat files using NumPy
  • Importance of flat files in data science
  • Python Data Science ToolBox
  • Customizing plots with Matplotlib
  • Histogram with Matplotlib
  • Basics plots with Matplotlib

SECTION 16: PYTHON FOR MACHINE LEARNING

  • Introduction to Machine learning
  • Setting up Scikitlearn and Ipython notebook
  • Getting started in Scikit-learn with famous iris dataset
  • Machine learning model with Scikit-learn
  • Comparing the machine learning model with Scikit-learn
  • Pandas vs Seaborn vs Scikitlearn
  • Choosing the best model in Scikitlearn using cross-validation
  • Evaluate a classifier in Scikitlearn
  • Text in sci-kit learn

SECTION 17: ADVANCED TOPICS

  • Unicode Text And Binary Data
  • Managed Attributes
  • Decorators
  • Metaclasses
  • Context Managers
  • Python 3.X Changes
  • Lab Session 13

LABORATORY EXERCISES

  • Lab 1: Using The Interpreter
  • Lab 2: Types And Operators
  • Lab 3: Basic Statements
  • Lab 4: Functions
  • Lab 5: Modules
  • Lab 6: Classes
  • Lab 7: Exceptions And Built-In Tools
  • Lab 8: System Interfaces And GUIs
  • Lab 9: Persistence
  • Lab 10: Text Processing And The Internet
  • Lab 11: Decorators And Metaclasses

Python Course Features

  • Backup Python Classes
  • Experienced Python Trainers
  • Python Online Training
  • Python Classroom Training
  • Python Corporate Training
  • Affordable Python Training Cost
  • Python Course Completion Certificate
  • Personality Development Training
  • Hands-on Training
  • Resume Preparation
  • Career Counselling
  • Placement Assistance
  • Live Project Support
  • Free Wi-Fi
  • Free Parking Facility

Though there is no official certification available for Python right now, Python Software Foundation (PSF) is planning to introduce certifications as early as possible. And this certification will apply globally and become an important qualification for Python-related jobs. However, you don’t have to worry about getting that certification, our Python course syllabus covers all the important information on Python and you will be able to clear the exam easily with our guidance. The names of proposed certifications (based on the level of expertise) in Python are given below:

Python Certified
Python Certified Professional
Python Certified Expert

Without these certifications also, you can prove your skills and knowledge on Python with the self-developed programs using Python. We have made it mandatory to develop your own programs on Python to pass the final assessment of our training. These programs will make a strong case for you and hugely increase your chances of selection at recruitment drives. Since you had joined and studied at the best Python training institute in Coimbatore, you don’t have to worry about getting a job in the Python domain. We will help you get a job in the Python domain as soon as you complete our training. So, all you need to do is complete our Python training which is the best Python training in Coimbatore. Studying at the best Python training institute in Coimbatore has a lot of benefits even if there is no official certification available for Python right now.

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