Python is one of the popular programming language in the United States that can be used for various applications, including web development, data analysis, artificial intelligence, machine learning, automation, and scientific computing. Its vast ecosystem of libraries and frameworks helps developers easily extend the language’s capabilities and streamline their work.
If you want to learn Python programming from scratch, then you are at the right place. We will guide you from here, how to start Python programming for beginners and to the advanced level.
We will start Python programming from the basics and then will go on learning various useful libraries that are used in Machine learning.
Ready to learn Python programming, let us start from the basics.
What is Python programming language in simple words?
Python is a user-friendly computer programming language that helps people create software, websites, and apps. It’s popular because it’s easy to learn and understand, even for beginners. People use Python to build all kinds of things, from simple tasks to advanced projects like artificial intelligence and data analysis.
Python is a programming language that’s loved by many because it’s simple and efficient. Even if you’re new to coding, you can learn Python quickly and start creating cool projects. From designing websites to analyzing data, Python is useful for all kinds of tasks. Plus, there’s a large community of people who share knowledge and help each other, making it even more enjoyable to work with Python.
Python is an excellent choice for machine learning (ML) and artificial intelligence (AI) projects due to its powerful libraries and frameworks specifically designed for these domains. With Python, you can easily develop and implement ML and AI algorithms for tasks such as image recognition, natural language processing, and predictive analytics.
Popular Python libraries like TensorFlow, PyTorch, and scikit-learn simplify the development process and provide extensive tools for building, training, and deploying ML models. The ease of use, flexibility, and strong community support make Python a go-to language for beginners and experts alike in the exciting world of ML and AI.
What is Python programming language used for?
Here are a few applications where we are using Python programming language.
Python’s broad applicability and strong community support make it a popular choice across various industries and applications. Its accessibility and ease of use, combined with its powerful capabilities, continue to attract both beginners and seasoned developers to explore and create using this dynamic programming language.
- Web Development: Python is frequently employed in web development, with frameworks such as Django, Flask, and Pyramid simplifying the process of building web applications. These frameworks provide tools and reusable components, allowing developers to create websites and web applications quickly and efficiently.
- Data Analysis and Visualization: Python is a popular choice for data analysis, with libraries like Pandas, NumPy, and Matplotlib offering powerful tools to manipulate, analyze, and visualize data. Data scientists and analysts often use Python to clean, preprocess, and explore datasets, as well as to perform statistical analysis and create data visualizations to gain insights and inform decision-making.
- Machine Learning and Artificial Intelligence: Python’s extensive library support makes it ideal for machine learning (ML) and artificial intelligence (AI) projects. Libraries like TensorFlow, PyTorch, and scikit-learn enable developers to create, train, and deploy ML models for various tasks, such as image recognition, natural language processing, and predictive analytics.
- Automation and Scripting: Python excels in automation and scripting tasks, as its easy-to-understand syntax enables developers to create scripts for automating repetitive tasks, parsing files, or interacting with APIs. Python’s extensive standard library and third-party packages make it simple to connect to various services, perform data manipulation, and streamline workflows.
- Game Development: Although not as widely known for game development as other languages, Python has libraries and frameworks like Pygame and Panda3D, which facilitate the creation of 2D and 3D games. Python’s ease of use and rapid development capabilities make it a suitable choice for indie developers and hobbyists looking to experiment with game development.
- Scientific Computing and Research: Python has become a staple in the scientific community due to its simplicity and powerful libraries tailored to scientific computing. Libraries such as SciPy, SymPy, and BioPython provide tools for numerical computing, symbolic mathematics, and computational biology, respectively, assisting researchers in various disciplines to perform simulations, data analysis, and modeling.
Python basic tutorials
Let us now check a few basic tutorials on Python programming language.
The first step to learning Python is to install Python and create your first hello world python program.
- Download and Install Python: This is a complete tutorial on how to install Python on Windows, Mac, Ubuntu, CentOS, etc.
- Python hello world program step by step: This is a complete tutorial on how to create your first hello world program using Pycharm and visual studio code.
- OOPS concepts in Python: Oops concepts are very important in Python and this article explains everything about oops concepts in Python.
- Comment Lines in Python: This tutorial explains, how to comment lines in Python.
- Python Keywords: In this tutorial, get to know all python keywords with examples.
- Python Naming Conventions: This is a complete guide that explains Python naming conventions.
- Python String: This Python tutorial explains everything about Python string, how to create a string in Python, various string operations, Python string methods, etc.
Advanced Python Tutorials
In this section, we will go through a list of Python advanced tutorials. We will check out a few libraries of Python that we can use for different purposes.
Python tkinter, short for “Tk interface,” is a built-in library in Python used for creating graphical user interfaces (GUIs) for desktop applications. It is a standard Python interface to the Tk GUI toolkit, which is a popular cross-platform library for creating graphical interfaces.
Tkinter provides a simple and intuitive way to design and develop GUI applications using a wide range of widgets, such as buttons, labels, text boxes, checkboxes, and menus. These widgets allow users to interact with the program through various input and output mechanisms.
Tkinter is an attractive choice for developers looking to create simple to moderately complex GUI applications, as it is easy to learn and requires no additional installations, being part of the Python standard library.
SciPy, which stands for Scientific Python, is an open-source library used in Python for scientific computing and technical applications. It builds upon the foundational capabilities of NumPy, another widely-used library for numerical computations in Python, and extends its functionality with additional modules for a wide range of scientific domains.
SciPy is commonly used by researchers, engineers, and data scientists to perform various tasks that require advanced mathematical functions and algorithms.
Matplotlib is a popular, open-source library in Python used for creating static, interactive, and animated visualizations. It provides a comprehensive and versatile set of tools for generating high-quality plots and graphs with a wide range of chart types, such as line plots, scatter plots, bar plots, histograms, and more. The library is highly customizable, allowing users to control various aspects of the visual appearance and layout of their plots.
Django is a high-level, open-source web framework used in Python for developing robust, scalable, and maintainable web applications. It follows the Model-View-Controller (MVC) architectural pattern and promotes rapid development and clean, pragmatic design. Django is widely used by developers for creating web applications ranging from simple websites to complex, data-driven platforms.
Turtle is a built-in Python library used for teaching programming concepts, especially to beginners, through drawing and controlling on-screen graphics called “turtles.” It is inspired by the Logo programming language, which was designed to introduce programming concepts to children in a visual and interactive manner.
The Turtle library provides a simple way to create shapes and graphics by giving commands to the turtle, which acts as a virtual pen moving across the screen.
Machine Learning Tutorials
Python has emerged as a popular programming language, especially for machine learning and data science applications. It is easy to learn and offers a variety of libraries and frameworks that facilitate the rapid development of machine learning models.
This tutorial series will delve into advanced Python concepts and introduce some of the powerful machine learning libraries, such as TensorFlow, Keras, and PyTorch. You will learn how to preprocess data, build and optimize machine learning models, and visualize results to gain insights.
Part 1: Advanced Python Concepts To begin with, we will explore advanced Python concepts that are essential for machine learning development. Topics covered will include list comprehensions, lambda functions, map, and filter functions. We will also dive into the world of generators, decorators, and context managers, which will provide you with the necessary foundation for efficient Python programming.
Python Pandas, Numpy & Scikit-Learn
Part 2: Data Preprocessing and Feature Engineering In this section, we will discuss the importance of preprocessing data and feature engineering for successful machine learning models.
We will introduce powerful Python libraries like Pandas, NumPy, and Scikit-learn to handle and manipulate data effectively. Additionally, you will learn various techniques for handling missing data, scaling, and normalization, encoding categorical features, and dimensionality reduction.
Part 3: Supervised Learning Algorithms Here, we will explore the most common supervised learning algorithms, such as linear regression, logistic regression, support vector machines, decision trees, and random forests.
We will explain the theory behind these algorithms and demonstrate how to implement them using Scikit-learn. Furthermore, you will learn how to fine-tune model parameters and evaluate performance metrics to ensure the best possible results.
Part 4: Deep Learning with TensorFlow and Keras In this section, we will introduce deep learning and the TensorFlow library, which is widely used for building complex neural networks. We will explore the Keras API, a high-level neural network API built on top of TensorFlow, to build, train, and deploy deep learning models.
Topics covered will include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs). We will also discuss techniques for regularizing and optimizing neural networks, such as dropout and batch normalization.
Part 5: PyTorch for Deep Learning and Computer Vision Lastly, we will delve into PyTorch, another popular deep learning framework. We will explore its dynamic computation graph, autograd system, and the use of GPU acceleration for fast training.
You will learn how to create custom neural networks and leverage pre-trained models for transfer learning. We will also discuss advanced computer vision techniques, such as object detection and semantic segmentation, using popular architectures like YOLO and Mask R-CNN.