Python Course &
Workshop

Detailed course with timings & practicals.

python-learning

Core Level Python

GETTING STARTED
History & need of
python
Advantages of python
Disadvantages of python
Features
Setting up Path
Working with Python
Basic Syntax
Variable and Data
Types
Operator

DATA TYPES
Numbers
Strings
Lists
Tuples
Dictionary
Set
Frozenset
Bool
Mutable and immutable

LISTS MANIPULATION
Accessing list
Operations
Working with lists
Function and Methods

TUPLES
Introduction
Creating tuples
Accessing tuples
Joining tuples
Replicating tuples
Tuples Slicing

DICTIONARIES
Arithmetic Operator
Relational Operator
Logical Operator
Membership Operator
Identity Operator
Bitwise Operator
Assignment Operator
Type Casting


CONDITIONAL STATEMENT
If
If-Else
Elif(Nested if-else)

LOOPING
For
While
Nested loops

CONTROL STATEMENT
Break
Continue
Pass

FUNCTIONS
Defining a function
Calling a function
Types of functions
Structure of python Functions
Anonymous functions
Global and local variables
Lambda Functions

MODULES
Importing module
Math module
Random module
Packages
Composition

EXCEPTION HANDLING
Default Exception and Errors
Catching Exceptions
Raise an Exception
User defined Exception

INPUT - OUTPUT
Printing on screen
Reading data from keywords
Opening and Closing file
Reading and Writing file 

This part of course includes multiple programs and projects. 

Advanced Level Python

OOPS CONCEPT
Class and objects
Attributes
Inheritance
Overloading
Overriding
Polymorphism

GUI PROGRAMMING
Introduction
Tkinter Programming
Tkinter Widgets
Frame
Button
Label
Entry
Messagebox
Labelframe

REGULAR EXPRESSIONS
Match
Function
Search
Function
Grouping
Matching at
Beginning or End
Match Object
Flags

MULTI THREADING
Thread and Process
Starting a Thread
Threading
Modules
Synchronizing
Threads
Multi Threaded
Priority Queue

CGI
Architecture
CGI
Environment variables
GET and POST methods
Cookies, FileUpload

DATABASE
MYSQL/MONGODB
PYMYSQL Connections
Executing
Queries
Transactions
Handling
Error

Libraries in Python

MULTI THREADING
Thread and Process
Starting a Thread
Threading
Modules
Synchronizing
Threads
Multi Threaded
Priority Queue

NUMPY
Setup
Numpy Array
Numby Append
Numpy
Reshape
Numpy SUM
Numpy Random
Numpy Log
Numpy Degree

PANDAS
Environment
Setup
Series
Data Frame
Sorting
Basic
Functionality
Working with Text Data

 This part of course includes multiple programs and projects.

Expert Level Python

As a Data Scientist, you are required to understand the business problem, design a data analysis strategy, collect and format the required data, apply algorithms or techniques using the correct tools, and make recommendations backed by data.

The program concludes with a capstone project designed to reinforce the learning by building a real industry product encompassing all the key aspects learned throughout the program. The skills focused on in this program will help prepare you for the role of a Data Scientist.

Tools Covered..
Flume, NumPy, pandas, SciPy, Spark, IBM Watson, Apache HBASE, hive, Pig, Sqoop,Hadoop Hdfs, Hadoop Map Reduce, Python, R, Scala


Detailed Program

1. Data science overview
2. Data Analytics Overview
3. Statistical Analysis & Business Application
4. Python Environment Setup & Essentials
5. Mathematical Computing with Python (NumPy)
6. Scientific Computing with Python (Scipy)
7. Data Manipulation with Pandas
8. Machine Learning with Scikit-Learn
9. Data Visulation in Python using matplotlib
10. Web Scraping with BeautifulSoup
11. Python integration with Hadoop MapReduce & Spark

An exciting branch of Artificial Intelligence, this Machine Learning certification online course will provide the skills you need to become a Machine Learning Engineer and unlock the power of this emerging field.
In-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised & unsupervised learning, regression, classification, and time series modeling.

Eligibility
Course is well-suited for participants at the intermediate level including, analytics managers, business analysts, information architects, developers looking to become data scientists, and graduates seeking a career in Data Science and Machine Learning.

Requires an understanding of basic statistics and mathematics at the college level, Familiarity with Python programming is also beneficial.
You should understand these fundamental courses including Python for Data Science, Math Refresher, and Statistics Essential for Data Science


Detailed Program

1. Introduction to AL & Machine Learning
2. Data Pre-processing
3. Supervised Learning
4. Feature Engineering
5. Supervised Learning Classification
6. Unsupervised Learning
7. Time Series Modeling
8. Ensemble Learning
9. Recommender Systems
10. Text Mining

This program focuses on the fundamental building blocks you will need to learn in order to become an AI practitioner. Specifically, you will learn programming skills, and essential math for building an AI architecture. You’ll even dive into neural networks and deep learning.

Course 1: Introduction to Python
Why Python Programming
Data Types and Operators
Control Flow
Functions
Scripting
Classes

Course 2: Anaconda, Jupyter Notebook, NumPy, Pandas, and Matplotlib
Anaconda
Jupyter Notebooks
NumPy Basics
Pandas Basics
Matplotlib Basics

Course 3: Linear Algebra Essentials
Vectors
Linear Combination
Linear Transformation and Matrices
Linear Algebra in Neural Networks
Labs

Course 4: Calculus Essentials
Derivatives Through Geometry
Chain Rule and Dot Product
More on Derivatives
Limits
Integration
Calculus in Neural Networks

Course 5: Neural Networks
Introduction to Neural Networks
Training Neural Networks
Deep Learning with PyTorch

Address

A-100, 2nd floor, Mohan Garden, Uttam Nagar, New Delhi-110059


Contacts

Email: support@pythonext.com
Phone: 9999038803
Phone: 9540822331