Applied Python: Web Dev, Machine Learning & Cryptography

EDUCBA via Coursera Specialization

Go to class Write review

Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

Unlock All Certificates

This hands-on specialization equips learners to build and deploy dynamic web applications, apply supervised machine learning techniques, and implement real-world cryptographic systems using Python. Across five project-based courses, learners will gain expertise in server-side scripting, sentiment analysis, linear regression, and secure communication technologies. By the end, students will be proficient in integrating Python across modern web systems, machine learning workflows, and encryption frameworks. Ideal for aspiring web developers, data analysts, and cybersecurity professionals.

Syllabus

Course 1: Developing and Deploying Web Applications with Python
Course 2: Integrating Python for Web Systems, Testing, and Packaging
Course 3: Linear Regression & Supervised Learning in Python
Course 4: Python Case Study - Sentiment Analysis
Course 5: Python Case Study - Cryptography

Courses

0 reviews

View details

Build practical skills in Python web development by learning how to create, connect, parse, publish, and deploy web applications. This course is designed for learners with a foundational understanding of Python who want to expand into GUI development, networking, web data processing, and web application deployment using industry-standard Python technologies. You will begin by building interactive desktop applications with wxPython, creating GUI-based programs, enhancing text editors, and integrating database support. Next, you will develop network-aware applications using Python networking modules, implement asynchronous programming with asyncio, and build event-driven applications with the Twisted framework. You will then learn to parse and clean HTML using Tidy, Python parsing libraries, and Beautiful Soup, before extracting web data and implementing CGI programming for server-side web applications. Finally, you will configure Apache for dynamic content deployment, publish applications using Python Server Pages, generate RSS feeds, and implement XML-RPC for remote communication. Whether you want to strengthen your Python web programming skills or gain hands-on experience with web deployment and communication technologies, this course provides a structured, practice-focused path from GUI development to deploying Python-powered web services.
0 reviews

View details

Build practical skills in Python web development, server-side scripting, testing, integration, and application packaging through a structured, hands-on learning experience. In this course, you will learn how to clean and parse web content, create dynamic CGI applications, and configure Apache for server-side execution. As you progress, you will develop dynamic web components using Python Server Pages (PSP), implement XML-RPC communication, validate applications with doctest and unittest, and analyze application performance using profiling tools. The course also explores advanced Python integration with Java and .NET through Jython and IronPython, memory management using reference counting, and packaging applications with distutils for distribution. Designed for learners who want to strengthen their Python web development skills, this course emphasizes practical implementation across the complete development pipeline—from web content preparation and application testing to cross-platform integration and deployment. By the end of the course, you will be able to parse and prepare web content, build and debug server-side Python applications, test and optimize application performance, integrate Python with multiple runtime environments, and package Python applications for scalable distribution. If you want to develop practical experience in building, testing, integrating, and deploying Python-based web systems, this course provides a comprehensive, project-based learning path.
0 reviews

View details

Learn how to apply and evaluate linear regression models in Python through a structured, hands-on introduction to supervised machine learning. This course guides you through the complete regression workflow, from identifying a machine learning use case and preparing your environment to analyzing data, building a model, and evaluating prediction accuracy. Designed for beginners and aspiring data professionals, the course introduces the essential Python libraries for regression, exploratory data analysis (EDA), and graphical techniques for understanding data distributions, variable relationships, and outliers. You will then construct a simple linear regression model, generate predictions, and evaluate model performance using standard metrics and prediction comparisons to determine how well the model fits real-world data. What makes this course unique is its project-driven learning approach that combines practical demonstrations, clear conceptual explanations, and structured assessments. Practice and graded quizzes aligned with Bloom's Taxonomy reinforce key concepts and help you build confidence as you progress. By the end of the course, you will be able to prepare data for regression, analyze relationships between variables, build and evaluate a linear regression model in Python, and interpret results to validate predictive performance. If you want to develop a strong foundation in Python-based supervised learning and regression analysis, this course provides a practical path to achieving that goal.
0 reviews

View details

Discover how cryptographic systems work by implementing and analyzing them with Python through practical case studies. This course introduces both classical and modern cryptography, helping you build a strong foundation in encryption techniques while exploring how secure communication systems are designed and evaluated. You will begin with core cryptography concepts and simple ciphers such as reverse and Caesar ciphers before progressing to brute force attacks, transposition, multiplicative, affine, substitution, Vernam, and Vigenère ciphers. As you advance, you will implement encryption and decryption programs, analyze cipher vulnerabilities, and evaluate cryptographic strength using Python. The course concludes with modern cryptography topics, including encoding, hashing, cryptographic libraries, and RSA public-key cryptography, where you will construct and validate RSA key pairs using modular arithmetic. Designed for learners interested in Python programming and cryptography, this course emphasizes hands-on implementation and analysis through real-world case studies. By the end of the course, you will be able to build encryption and decryption tools, compare classical and modern cryptographic techniques, analyze cryptographic weaknesses, and implement secure communication methods using Python.
0 reviews

View details

Learn how to build and evaluate a sentiment analysis model using Python through a practical, hands-on approach to natural language processing (NLP). In this course, you will explore the core concepts of sentiment analysis, understand its real-world applications, and identify the development environment, Python libraries, and machine learning algorithms commonly used for text classification. As you progress, you will construct a complete sentiment analysis pipeline by cleaning and processing text data, implementing code, training machine learning models, and evaluating their performance using standard evaluation metrics. Each lesson builds on the previous one, helping you develop practical skills through a project-based learning experience. This course is designed for learners with basic Python knowledge who want to expand their understanding of NLP and machine learning by building a working sentiment analysis application. Rather than focusing only on theory, the course emphasizes hands-on implementation, allowing you to apply concepts throughout the development process. By the end of the course, you will be able to identify key sentiment analysis concepts, select appropriate Python tools and libraries, implement data preprocessing and feature extraction techniques, train sentiment classification models, and assess model performance using standard evaluation methods. If you want to strengthen your Python and NLP skills through a practical sentiment analysis project, this course provides a structured path from foundational concepts to model evaluation.