Spécialisation Applied Python: Web Dev, Machine Learning & Cryptography

This course is designed to guide learners through the complete pipeline of Python-based web application development, from structured document parsing to cross-platform deployment and performance tuning. Through three progressively advanced modules, learners will apply, construct, and evaluate server-side scripting techniques, Python-based integration tools, and distribution mechanisms.

In Module 1, learners will explore the foundations of web content parsing, using libraries such as Tidy and Beautiful Soup, and will demonstrate the creation and debugging of CGI-based web applications. In Module 2, learners will construct dynamic server-side components using Python Server Pages (PSP), implement XML-RPC communication, and validate their applications with testing tools like doctest and unittest, followed by evaluating performance using profiling tools. In Module 3, learners will demonstrate integration with external platforms like Java and .NET through Jython and IronPython, analyze memory management using reference counting, and construct Python packages using distutils for scalable application deployment. This hands-on, project-based course enables learners to not only build web solutions but also optimize and package them efficiently for diverse environments.

This hands-on course empowers learners to apply and evaluate linear regression techniques in Python through a structured, project-driven approach to supervised machine learning. Designed for beginners and aspiring data professionals, the course walks through each step of the regression modeling pipeline—from understanding the use case and importing key libraries to analyzing variable relationships and predicting outcomes.

In Module 1, learners will identify, describe, and prepare the foundational elements of a machine learning project. Through univariate and graphical analysis, they will recognize distribution patterns, outliers, and data characteristics critical to model readiness. In Module 2, learners will analyze variable relationships, construct a regression model, and evaluate its predictive performance using standard metrics and visualizations. By the end of the course, learners will confidently interpret model results and validate them against actual outcomes—equipping them with the core skills to build and assess linear regression models using Python. This course blends practical demonstrations, clear conceptual explanations, and structured assessments—including practice and graded quizzes aligned with Bloom’s Taxonomy—to promote deep, outcome-oriented learning.

This hands-on course equips learners with the practical knowledge and technical skills to develop, implement, and evaluate a sentiment analysis model using Python. Beginning with an introduction to sentiment analysis and its real-world applications, learners will explore and identify appropriate tools including IDEs and essential libraries used in natural language processing (NLP).

As the course progresses, learners will analyze the use of various algorithms suitable for sentiment classification and gain experience in constructing a full analysis pipeline—from data preprocessing and cleaning to model training and evaluation. Each lesson is crafted to reinforce applied learning, enabling participants to demonstrate mastery through building a working sentiment analysis system capable of classifying textual data based on emotional tone. By the end of the course, learners will be able to: • Identify key concepts in sentiment analysis. • Select and configure appropriate tools and libraries for text classification. • Implement code for data cleaning, transformation, and feature extraction. • Train and evaluate machine learning models for sentiment classification. • Assess model performance using standard evaluation metrics. This course is ideal for learners with basic Python knowledge who want to delve into NLP and machine learning through a practical, project-based case study.

This course offers a hands-on, case study-driven introduction to classical and modern cryptography using Python. Through a progression of real-world cipher implementations, learners will understand foundational encryption principles, apply cipher algorithms programmatically, and analyze vulnerabilities in both classical and modern encryption schemes.

Starting with basic reverse and Caesar ciphers, the course advances through brute force attacks, transposition techniques, and affine-based cryptography before culminating in public key cryptosystems like RSA. Learners will gain practical experience in building encryption and decryption tools, evaluating cryptographic strength, and creating secure systems using libraries like PyCrypto. By the end of the course, learners will be able to construct, experiment with, and critically evaluate cryptographic systems for secure communication using Python programming, while also demonstrating fluency in key cryptographic concepts such as hashing, key generation, and symmetric vs. asymmetric encryption.