Python is an interpreted and high-level programming language which was originated in the year of late 1980s but it was implemented in December 1989 by Guido Van Rossum. The word Python came when Guido Van Rossum began implementing Python, Guido van Rossum was also reading the published scripts from “Monty Python’s Flying Circus”, a BBC comedy series from the 1970s. Van Rossum thought he needed a name that was short, unique, and slightly mysterious, so he decided to call the language Python(source). According to a recent survey by StackOverflow, Python has overtaken java in popularity.
Python is dynamically typed, and garbage-collected language which aims to help a programmer to write clear and logical code for both small and large-scale projects.
Learning Python is good because Python is a famous programming language because it is easy to learn and use. Moreover, there is a large number of libraries available that can be used in your projects today! Some developers even consider it to be a must-known programming language.
Python is opensource which was developed under OSI-approved license, making it freely usable and distributable, even for commercial use. It means any of them can contribute to the python software.
An interpreter is a kind of program that executes other programs when you write python, programs, it converts source code written by the developer into an intermediate language. Which again translated into the native language/machine language that is executed.
The python code written by you is computed into python byte code, which creates a file with the extension .Py. The byte code compilation happens internally and almost completely hidden from the developer. The compilation is simply a translation step, and byte code is a lower-level and platform-independent representation of your source code. Roughly, each of your source statement is translated into a group of byte code instructions, this byte code translation is performed to speed execution of byte code.
The .py file is created during the compilation step in order to execute the data present in the file. Hence interpreter is mostly used for compiling each and every line in python code.
The best environment to learn and execute your code in python is Anaconda. Anaconda is an open-source platform with an inbuilt package of different software required for a developer to execute his skill. Anaconda is popular as it contains many of the tools used in data science and Machine learning with just one install, so it is better to have a short and simple setup.
To Setup Python Environment, follow the below steps–
Step1: Go to Google and type Anaconda.
Step2: Click on the first link.
Step3: Click “Get Started” button.
Step4: Click on “See all Anaconda products” button.
Step5: Go to “Individual Edition” and click on “Learn More“.
Step6: Now click on “Download” button.
Step7: You will automatically be redirected to down of the page where you can select your system config: Windows- 32/64 bit, Mac, and Linux click download it.
Step8: Install it in your PC/laptop/Mac.
You are ready to become a developer!!
- Easy to learn
- Easy to write code
- Big Community support
- Used to make GUI applications
- In demand
- Used for scripting and automation
- Web development
- Data Science
- Machine learning
- Artificial Intelligence
- Easy image processing
- Rich in libraries
What you will learn?
We have designed this course for both i.e. beginners and professionals. We will explain everything in detail while avoiding any confusion. We will cover most of the topics in python-like syntax, statements, comments, lists, dictionaries, functions, classes and objects, inheritance, date-time, modules, libraries, exceptions, file I/O, etc. So, we are very excited to see you in further lectures.
- Machine Learning: A Gentle Introduction
- Machine Learning Course Description
- ML Environment Setup and Overview
- Jupyter Notebook: The Ultimate Guide
- Numpy For Machine Learning: A Complete Guide
- Python Pandas Tutorial: A Complete Introduction for Beginners
- Matplotlib Python: A Beginner’s Walkthrough
- Seaborn: Create Elegant Plots
- Set up Python Environment
- Linear Regression: Your 1st Step in Machine Learning
- Gradient Descent: Another Approach to Linear Regression
- Logistic Regression: Regression Model for Classification
- Decision Tree: Foundation of Powerful ML Algorithms
- K Nearest Neighbours|KNN: One of the Earliest ML Algorithm
- Random Forest in ML: Wisdom of the Crowd
- Titanic Survival Prediction – Machine Learning Project (Part-1)
- Titanic Survival Prediction – Machine Learning Project (Part-2)
- Customer Segmentation using Python in Machine Learning
- Detect Eyes and Detect Faces in Python
- 8 Steps to Build a Machine Learning Model