** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on Python Projects will help you establish a foothold on Python by helping you assess and obtain skills which are used to design, develop and analyze projects built in Python.
1. Introduction to Python
2. Installation and Working with Python
3. Python Projects- 3levels
4. Practical approach - Code
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Agenda
Introduction 01
Introduction
to Python
Getting Started 02
Concepts 03
Practical Approach 04
Installing and working
with Python
Looking at code to
understand theory
Python Projects – 3 levels
5. Python Certification Training https://www.edureka.co/python
Introduction To Python
Python is an interpreted, high-level, general-purpose
programming language.
What is Python?
Open Source
Largest community for
Learners and Collaborators
Let’s get started then!
I created
Python!
No. It wasn't named after a
dangerous snake.
Rossum was fan of a comedy
series from late seventies.
The name "Python" was
adopted from the same
series "Monty Python's
Flying Circus".
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Popularity Of Python
Learning other
languages
Learning
Python
Python is very
beginner-friendly!
Hello Python!
Syntax is extremely
simple to read and
follow!
Millions of happy learners!
I love Python!
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Why Should You Learn Python?
Length of the code is relatively short
Python is a general-purpose language
Wide range of applications
Fun to work with!
Web Development Mathematical Computations Graphical User Interface
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Installation & Development Environments
There are a lot of environments you can use!
Komodo IDE
Installing Python is very straightforward!
Google Colab
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Progression With Python
Introduction to Python
Learning the fundamentals of
Python
Getting started With Code
Python Usage
Important to know why you want to learn
Python.
Top uses of Python in the real world.
Python Projects
Learning to develop using Python
Solving problems using Python
Python Mastery
Doing all that is needed to master
Python and implement it in
multiple situations.
Working on concepts involving
Artificial Intelligence or any other
trendy technologies.
1
3
2
4
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Introduction to Python Projects
It is best to divide the level of complexity and work from the ground up
Projects
Easy Projects
Intermediate Projects
Advanced Projects
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Introduction to Python Projects
1
2
3
4
Easy Projects
Starting out with simple projects which do not
need any dependencies
Use existing packages to build something
Intermediate Projects
Using external libraries to work with
Python
Simple libraries at this point of time
Fundamentals & Basics
Important to understand all/most of the
fundamentals before starting with code
Basics of Python is easy to understand
Advanced Projects
Making use of Python for specialized requirements.
Achieving Artificial Intelligence is one good use case
of an advanced project.
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Easy Python Projects
Problem Statement Analyzing and providing solution
Python ImplementationSolution Achieved
Conversions5
String Manipulation6
Hangman7
Hello World 1
Calculations 2
Basic Programs 3
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Easy Python Project - Hangman
Have you played Hangman before?
‘n’ lettered word! Check if letter exists
Word Guess!!Number of attempts
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Intermediate Python Projects
There are many Python concepts to be considered as intermediate!
NumPy
Pandas
Scipy
Matplotlib
Use an external library
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Intermediate Python Project – Data Visualization
Data Visualization is vital for analyzing and displaying data graphically!
Input Data Analyze Data
Visualize using GraphOutput Data
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Intermediate Python Project – Data Visualization
3D Visualization using Matplotlib
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Advanced Python Projects
Achieving Artificial Intelligence is advanced in my opinion!
TensorFlow
Keras
PyTorch
Theano
Deep Learning
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Advanced Python Project – Image Classifier
Generating an Image Classifier which predicts data based on an Image-Set by constructing a
Neural Network which is used by companies like Google to create Image-to-Text Applications
such as Translation etc.
Problem Statement
Using this we optimise accuracy of data obtained!
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Image Classifier – Dataset
What about data?
Standard Python Packages can be used to load data into numpy array.
Then can be converted into a torch.*Tensor.
torchvision package helps to avoid writing boilerplate code
Image
• Pillow
• OpenCV
Audio
• Scipy
• Librosa
Text
• SpaCy
• Cython
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Image Classifier – CIFAR10
• Let’s use the CIFAR10 dataset.
• The images in CIFAR-10 are of size 3x32x32, i.e. 3-channel colour images of 32x32 pixels in size.
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Image Classifier – Flow Diagram
Start
Load the
Dataset
Read the
Dataset
Normalize test Dataset using
torchvision
Define Convolution Neural
Network (CNN)
Define Loss Function
Train the
Network
Test the Network Based on
Trained Data
End
Repeat the process to
Decrease the Loss
Pre-processing of dataset
Make Prediction on the Test
Data