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- What is Python?
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- What is Python bytecode?
- Python Regular Expressions
Python Panda Tutorial
- Python Pandas Tutorial
- Python Pandas Features
- Advantages and Disadvantages of Python Pandas
- Pandas Library In Python
- Pandas Series To Frame
- Python Dataframeaggregate and Assign
- Pandas Dataframe Describe
- Pandas Dataframe Mean
- Pandas Hist
- Pandas Dataframe Sum
- How to convert Pandas DataFrame to Numpy array
- Pandas Concatenation
- Pandas Shift
- Pandas Rolling
- Data Analysis With Pandas and Python
- How Python Panda Are Helpful For With Data Science
Python Selenium
- Selenium Basics
- Selenium with Python Introduction and Installation
- Navigating links using get method Selenium Python
- Locating Single Elements in Selenium Python
- Locating Multiple elements in Selenium Python
Python Flask Tutorial
Python Django
- How to Install Django and Set Up a Virtual Environment in 6 Steps
- Django MTV Architecture
- Django Models
- Django Views
- Django Templates
- Django Template Language
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- Django Admin Interface
- Django Database
- Django URLs and URLConf
- Django Redirects
- Django Cookies and Cookies Handling
- Django Caching
- Types of Caching in Django
- Django Sessions
- Django Forms Handling & Validation
- Django Exceptions & Error-handling
- Django Forms Validation
- Django Redirects
- Django Admin Interface
- Django Bootstrap
- Ajax in Django
- Django Migrations and Database Connectivity
- Django Web Hosting and IDE
- Django Admin Customization
- What is CRUD?
- Django ORM
- Django Request-Response Cycle
- Django ORM
- Making a basic_api with DRF
- Django Logging
- Django Applications
- Difference between Flask vs Django
- Difference between Django vs PHP
Numpy
- Numpy Introduction
- NumPy– Environment Setup
- NumPy - Data Types
- NumPy–Functions
- NumPy Histogram
- numPy.where
- numpy.sort
- NumPyfloor
- Matrix in NumPy
- NumPy Arrays
- NumPy Array Functions
- Matrix Multiplication in NumPy
- NumPy Matrix Transpose
- NumPy Array Append
- NumPy empty array
- NumPy Linear Algebra
- numpy.diff()
- numpy.unique()
- numpy.dot()
- numpy.mean()
- Numpy.argsort()
- numpy.pad()
- NumPyvstack
- NumPy sum
- NumPy Normal Distribution
- NumPylogspace()
- NumPy correlation
- Why we learn and use Numpy?
Tensorflow
- Introduction To Tensorflow
- INTRODUCTION TO DEEP LEARNING
- EXPLAIN NEURAL NETWORK?
- CONVOLUTIONAL AND RECURRENT NEURAL NETWORK
- INTRODUCTION TO TENSORFLOW
- INSTALLATION OF TENSORFLOW
- TENSORBOARD VISUALIZATION
- Linear regression in tensorflow
- Word Embedding
- Difference between CNN And RNN
- Explain Keras
- Program elements in tensorflow
- Recurrent Neural Network
- Tensorflow Object Detection
- EXPLAIN MULTILAYER PERCEPTRON
- GRADIENT DESCENT OPTIMIZATION
Interview Questions & Answers
Numpy Introduction
NumPy is a Python package. It stands for ‘Numerical Python’. It is a library consisting of multidimensional array objects and a collection of routines for processing of array.
Numeric, the ancestor of NumPy, was developed by Jim Hugunin. Another package Numarray was also developed, having some additional functionalities. In 2005, Travis Oliphant created NumPy package by incorporating the features of Numarray into Numeric package. There are many contributors to this open source project.
Operations using NumPy
Using NumPy, a developer can perform the following operations −
- Mathematical and logical operations on arrays.
- Fourier transforms and routines for shape manipulation.
- Operations related to linear algebra. NumPy has in-built functions for linear algebra and random number generation.
NumPy – A Replacement for MatLab
NumPy is often used along with packages like SciPy (Scientific Python) and Mat−plotlib (plotting library). This combination is widely used as a replacement for MatLab, a popular platform for technical computing. However, Python alternative to MatLab is now seen as a more modern and complete programming language.
It is open source, which is an added advantage of NumPy.