PYTHON TRAINING IN CHENNAI


Data Analysis and Machine Learning using Python training in Chennai. Best Institute for Python training in Chennai with real time expert guidance and fully hands-on classes in Python for Data Science.

Greens Technologys no 1 Python Training Institute in Chennai offering training on python by experts!


About The Trainer

- Karthik work as an Data Scientist & Instructor, He has over 11 years of analytics experience working with companies like Capital One, Walmart, ICICI Lombard etc and recognized expert in analytics using R & Python.

Mr. Karthik specializes in SAS, R, Python, Machine leaning and algorithms, MATLAB, Big Data: Hadoop (Hive,Pig,casandra,Hbase), SPSS and Advanced SAS (SQL and Macros).

Qualification: M.S. in Statistics

Membership American Statistical Association


Flexible Timings / Weekend classes Available.

Talk to the Trainer @ +91-8939915577

Python Training Course Objective


  • Hands-on experience setting up an integrated analysis environment for doing data science with Python.
  • An understanding of how to use the Python standard library to write programs, access the various data science tools, and document and automate analytic processes.
  • Orientation to some of the most powerful and popular Python libraries for data science including Pandas (data preparation, analysis, and modeling; time series analysis), scikit-learn (machine learning), and Matplotlib and Seaborn (data visualization).
  • Working knowledge of the Python tools ideally suited for data science tasks, including:
    • Accessing data (e.g., text files, databases)
    • Cleansing and normalizing data
    • Exploring data (e.g., simple statistics, correlation matrices, visualization)
    • Modeling data (e.g., machine learning)


Python Training Course Syllabus


I. Introduction and Setting Up Your Integrated Analysis Environment

Setting Up Your Integrated Analysis Environment & Tools Overview

  • IPython Shell
  • Custom environment settings
  • Jupyter Notebooks
  • Script editor
  • Packages: NumPy, SciPy, scikit-learn, Pandas, Matplotlib, Seaborn, etc.

Once you complete this module, you will understand some of the unique benefits of using Python for data science / what features make Python particularly well-suited for data science, you will be able to set up a fully functioning Python-based analysis environment, and you will know what each tool is used for in the data science workflow.

II. Using Python to Control and Document Your Data Science Processes

Python Essentials

  • Data types and objects
  • Loading packages, namespaces
  • Reading and writing data
  • Simple plotting
  • Control flow
  • Debugging
  • Code profiling

Once you complete this module, you will be able to use the Python standard library plus Canopy tools to write, run, debug, and profile programs that control your data science processes (which draw on the scientific packages).

III. Accessing and Preparing Data

Data, Data, Everywhere...

Acquiring Data with Python

  • Loading from CSV files
  • Accessing SQL databases

Cleansing Data with Python

  • Stripping out extraneous information
  • Normalizing data
  • Formatting data

Once you complete this module, you will know how to load data from common types of data sources, including structured text files and SQL databases. and you will know some of the common tools used in Python to cleanse and prepare your data for analysis.

IV. Numerical Analysis, Data Exploration, and Data Visualization with NumPy Arrays,
Matplotlib, and Seaborn

NumPy Essentials

  • The NumPy array
  • N-dimensional array operations and manipulations
  • Memory mapped files

Data Visualization

  • 2D plotting with Matplotlib
  • Advanced data visualization with Seaborn

Once you complete this module, you will understand how to use NumPy arrays for efficient numerical processing and how to use NumPy methods such as slicing to write code that is both compact and easy to read and understand. You will know how to use Matplotlib, Seaborn, and NumPy together to explore and visualize your data.

V. Exploring Data with Pandas

Searching for Gold in a Pile of Pyrite

  • Data manipulation with Pandas
  • Statistical analysis with Pandas
  • Time series analysis with Pandas

At the end of this module, you will know how to access some of the core tools used for statistical analysis and data exploration in Python.

VI. Machine Learning with scikit-learn

Predicting the Future Can Be Good for Business

  • Input: 2D, samples, and features
  • Estimator, predictor, transformer interfaces
  • Pre-processing data
  • Regression
  • Classification
  • Model selection

At the end of this module you will have a working understanding of what machine learning tools are available in scikit-learn and how to use them.

Data Analysis And Machine Learning Using Python Training in Chennai


Use your data to predict future events with the help of machine learning. This Data Analysis And Machine Learning Using Python Training in Chennai will walk you through creating a machine learning prediction solution and will introduce Python, the scikit-learn library, and the Jupyter Notebook environment.


Machine Learning Using Python Training Objective


When working with data, machine learning can be used to do incredible things, including predicting future events. Its ease of use combined with the power of scikit-learn is causing Python to become the preferred development language for many machine learning practitioners. In this course, Understanding Machine Learning with Python, you will learn how Python developers and data scientists use machine learning to predict the likelihood of events based on data. Throughout this Python training in chennai, you will use Python and the scikit-learn library. Specifically, you will learn how to format your problem to be solvable, how to prepare your data for use in a prediction, and finally how to combine that data with algorithms to create models that can predict the future, all performed in the Jupyter Notebook environment. By the end of this Python training in chennai, you will have a better understanding of how machine learning can help you put your data to good use in predicting future events, and you'll also know how to use Python to make it happen.


  • Introduce you to Machine Learning: Introduce you to Machine Learning and the technology behind it. You will see why companies are in such a rush to use Machine Learning to grow their business and increase profits.
  • Teach you to build predictive models: You will learn how developers and Data Scientists use Machine Learning to predict events based on data. Specifically, you will learn how to format a problem to be solvable, where to get data, and how to combine that data with algorithms to create models that can predict the future.
  • Utilize the Python language: Throughout this course we will utilize Python and a number of its libraries that make creating machine learning solutions easy. However, you do not need prior experience with Python. In this course we learn by doing, and the code we use will be explained as we create our solution.

By the end of this course you will know the how, when, and why of building a machine learning solution with Python. You will have the skills you need to transform a one line problem statement into a tested prediction model that solves the problem.

I look forward to you joining me on this journey of═×Understanding Machine Learning with Python from Best Python training institute in chennai.


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Introduction

  • Why do we need Python?
  • Program structure

Execution steps

  • Interactive Shell
  • Executable or script files
  • User Interface or IDE

Memory management and Garbage collections

  • Object creation and deletion
  • Object properties

Data Types and Operations

  • Numbers
  • Strings
  • List
  • Tuple
  • Dictionary
  • Other Core Types

Statements and Syntax

  • Assignments, Expressions and prints
  • If tests and Syntax Rules
  • While and For Loops
  • Iterations and Comprehensions

File Operations

  • Opening a file
  • Using Files
  • Other File tools

Functions

  • Function definition and call
  • Function Scope
  • Arguments
  • Function Objects
  • Anonymous Functions

Modules and Packages

  • Module Creations and Usage
  • Module Search Path
  • Module Vs. Script
  • Package Creation and Importing

Classes

  • Classes and instances
  • Classes method calls
  • Inheritance and Compositions
  • Static and Class Methods
  • Bound and Unbound Methods
  • Operator Overloading
  • Polymorphism

Exception Handling

  • Default Exception Handler
  • Catching Exceptions
  • Raise an exception
  • User defined exception

Advanced Concepts

  • Decorators
  • Generators
  • Iterators
  • Co-routines
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Standard Library Modules

References

Exercises

Roadmap with Python



Python Training - Advanced Syllabus

Functions and Program Flow

  • More with Functions
  • Variable Scope
  • Introducing Lambdas
  • Exception Handling
  • try-except-else
  • try-finally
  • Custom Exceptions
  • Advanced Looping Techniques
  • Introducing Iterators and Magic Methods
  • Generators
  • Coroutines

Classes in Python

  • Modules Revisited
  • Creating Classes in Python
  • Classes are Namespaces
  • Working with Instances
  • __dict__, __setitem__(), __getitem__()
  • __getattr__ and __setattr__
  • Constructors
  • Where's public and private?
  • Self and Instances
  • Class Variables
  • Class Attributes in Instance Methods
  • Classic vs "New Style" Classes
  • Inheritance
  • Using super()
  • Multiple Inheritance
  • Determining Method Resolution Order
  • Search Order in Instances and Hierarchies
  • Abstract Classes
  • Lack of Interfaces
  • Operator Overloading
  • Static and Class Methods
  • Properties
  • __slots__
  • List Comprehensions

Introducing System Administration and the Python Standard Library

  • System Administration with Python
  • Using the Python Standard Library
  • Introducing Jython Scripting

Advanced Programming with Functions and Metaclasses

  • Introduction to Functional Programming
  • Closures
  • Decorators
  • Metaclass Programming

Classes in Python

  • Modules Revisited

Regular Expressions, Searching, Pattern Matching

  • Regular Expressions

Working with XML

  • Overview of Python's XML Offerings
  • Parsing XML
  • Using LXML

Data Persistence

  • Pickle, shelve, dbm, anydbm Modules
  • ORM in Python: SQLAlchemy
  • Incorporating Transactions
  • Database Account Example

Sys Admin Scripting Revisited

  • Tuning Tips and Command-line Options
  • Subprocesses
  • Linking Subprocesses
  • Comparing files

Multi-threading

  • Threading Basics
  • Locking
  • Semaphores
  • Multiprocessing Module

Appendices:

  • Python Overview Python Data Types
  • Working with HTML Network Protocols Intro to GUIs

Interested in registering for a free demo session? please call us +91-9840496320

Testimonials
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