Python Training Company In Udaipur Rajasthan India
WHY TO CHOOSE PYTHON PROGAMING DEVELOPMENT TRAINING COURSES AT KEEN INFOTECH
- We provide the python programming with live class
- Well Equipped Computer Python Development Developers
- 100% Practical Oriented Sessions
- Our python developer trainers have minimum 7 years python application development experience
- 100% Job Placement Assistance.
- Learn How to make your python Project Live on Domain and IP Address
- Product Based Training
- Extensive Support python Libraries
- Real-Time Case Studies
- Use of Latest Tools & Technology
- Daily Handouts & Lab Exercise
Python Development Training Full Stack Development Machine Learning Data Analysis AI Kivy
DESCRIPTION
The course is designed to provide Basic and Advance knowledge of Python. Python programming is intended for software engineers, Machine Learning , Data analysis, Kivy App Development system analysis, program managers and user support personnel who wish to learn the Python programming language. Ease of use for beginners.
- Python is one of the easiest programming languages to learn.
- Quick application development time.
- Extensive data visualization support.
- Open-source larges libraries.
- Leading companies are using Python.
WHY ONE SHOULD CHOOSE PYTHON DEVELOPMENT AS CAREER?
Python is found to one of the most popular programming frameworks for data science data analysis artificial intelligence App kivy across the world. It is also one of the best programming languages when it comes to adaptability
Why do you want to build your career as Python development ?
Python is easy to pick up / easy Syntax and a versatile programming language. Due to its fast growing popularity and the universal dependence on web or computer-based applications, Python jobs are also on the rise
Python developer a good career?
No wonder Python has been constantly in demand since its existence. Python developer is surely one of the most recommended career options for any professional.
Why you should choose Python?
Why Python is so popular?
First and foremost reason why Python is much popular because it is highly productive as compared to other programming languages like C++ and Java. Python is also very famous for its simple programming syntax, code readability and English like commands that make coding in Python lot easier and efficient.
How python different from other languages
Python is an interpreted and dynamically typed language, whereas Java is a compiled and statically typed language. Python code doesnt need to be compiled before being run. Java code, on the other hand, needs to be compiled from code readable by humans to code readable by the machine
Python is used by hundreds of thousands of programmers and is used in many places. Sometimes only Python code is used for a program, but most of the time it is used to do simple jobs while another programming language is used to do more complicated tasks.
Its standard library is made up of many functions that come with Python when it is installed. On the Internet there are many other libraries available that make it possible for the Python language to do more things. These libraries make it a powerful language; it can do many different things.
Some things that Python is often used for are
WHO CAN LEARN Pytohn ?
Over the entire course you will learn:
- Python
- HTML & HTML 5
- CSS & CSS 3
- Responsive Design with Bootstrap
- JavaScript
- jQuery
- AJAX
- MongoDB
- APIs (both creating them and interacting with them)
WHAT IS SCOPE OF CAREER GROWTH IN ANDROID AFTER TRAINING?
• One can make his/her own app and upload it on play store and earn money.• As Python is new technology there will plenty of growth points.
PREREQUISITES
• Base company programming KnowledgePython Programming Training Outline
An Overview of Python
- What is Python?
- Interpreted languages
- Advantages and disadvantages
- Downloading and installing
- Which version of Python
- Where to find documentation
Running Python Scripts
- Structure of a Python script
- Using the interpreter interactively
- Running standalone scripts under Unix and Windows
Getting Started
- Using variables
- String types: normal, raw and Unicode
- String operators and expressions
- Methods print(), str(), and int()
- The format() method
- Getting user input with the input() method
- Asking users for input
- Command line parameters
Flow Control
- About flow control
- if Statements
- for Statements
- while Statements
- The range() Function
- break and continue Statements, and else Clauses on Loops
- pass Statements
Defining Functions
- Syntax of function definition
- Keyword Arguments
- Default Argument Values
- Keyword Arguments
- Arbitrary Argument Lists
- Unpacking Argument Lists
- Lambda Expressions
- Documentation Strings
- Function Annotations
Data Structures
- Using Lists as Stacks
- Using Lists as Queues
- List Comprehensions
- Nested List Comprehensions
- The del statement
- Tuples and Sequences
- Sets
- Dictionaries
- Looping Techniques
Modules
- Executing modules as scripts
- The Module Search Path
- “Compiled” Python files
- Standard Modules
- The dir() Function
- Packages
- Importing * From a Package
- Intra-package References
- Packages in Multiple Directories
Working with Files
- Text file I/O overview
- Opening a text file
- Reading text files
- Raw (binary) data
- Using the pickle module
- Writing to a text file
- Methods of File Objects
- Saving structured data with json
Errors and Exceptions
- Syntax Errors
- Exceptions
- Handling Exceptions
- Raising Exceptions
- User-defined Exceptions
- Defining Clean-up Actions
- Predefined Clean-up Actions
Classes
- Class Definition Syntax
- Class Objects
- Instance Objects
- Method Objects
- Class and Instance Variables
- Inheritance
- Multiple Inheritance
- Private Variables
- Odds and Ends
- Iterators
- Generators
- Python Scopes and Namespaces
Standard Library
- Operating System Interface
- File Wildcards
- Command Line Arguments
- String Pattern Matching
- Mathematics
- Internet Access
- Dates and Times
- Data Compression
- Performance Measurement
- Quality Control
- Templating
- Working with Binary Data Record Layouts
- Multi-threading
- Logging
Machine Learning
Pre Program Preparation
-
Python for Data Analysis: Get acquainted with Data Strtuctures, Object Oriented Programming, Data Manipulation and Data Visualization in Python
- Introduction to SQL: Learn SQL for querying information from databases
- Math for Data Analysis: Brush up your knowledge of Linear Algebra, Matrices, Eigen Vectors and their application for Data Analysis
Statistics Essentails
MachineLearning
- Linear Regression: Learn to implement linear regression and predict continuous data values
- Supervised Learning: Understand and implement algorithms like Naive Bayes and Logistic Regression
- Unsupervised Learning: Learn how to create segments based on similarities using K-Means and Hierarchical clustering
- Support Vector Machines: Learn how to classify data points using support vectors
- Decision Trees: Tree-based model that is simple and easy to use. Learn the fundamentals on how to implement them
Natural Languege Processing
- Basics of text processing: Get started with the Natural language toolkit, learn the basics of text processing in python
- Lexical processing: Learn how to extract features from unstructured text and build machine learning models on text data
- Syntax and Semantics: Conduct sentiment analysis, learn to parse English sentences and extract meaning from them
- Other problems in text analytics: Explore the applications of text analytics in new areas and various business domains
Keen Infotech Python Latest News
New PyPI launched, legacy PyPI shutting down April 30 New PyPI launched, legacy PyPI shutting down April 30. Starting today, the canonical Python Package Index is at https://pypi.org and uses the new Warehouse codebase. We announced the https://pypi.org beta on March 26 and your feedback and test usage have helped us get it production-ready. Monday April 16 (2018-04-16): We launched the new PyPI, redirecting browser traffic and API calls (including "pip install") from pypi.python.org to the new site. The old codebase is still available at https://legacy.pypi.org for now. Monday April 30 (2018-04-30): We plan to shut down legacy PyPI https://legacy.pypi.org . The address pypi.python.org will continue to redirect to Warehouse