Overview

This course provides a comprehensive, hands-on introduction to Artificial Intelligence and Predictive Analytics using Python. Learners will progress from foundational concepts of predictive modeling and ensemble methods to advanced unsupervised clustering techniques like Meanshift, Affinity Propagation, and Gaussian Mixture Models. The course then explores supervised learning algorithms, including Logistic Regression, Naive Bayes, and Support Vector Machines, and transitions into logic programming and problem-solving approaches such as heuristic search, local search, and constraint satisfaction problems. The final module introduces Natural Language Processing (NLP) with Python and NLTK, covering tokenization, stemming, lemmatization, segmentation, information extraction, chunking, Named Entity Recognition (NER), and grammar-based parsing techniques including Context-Free Grammar, recursive descent parsing, and shift-reduce parsing. By the end of this course, learners will be able to: • Apply predictive analytics and machine learning algorithms to real-world problems. • Analyze clustering, classification, and NLP pipelines to process structured and unstructured data. • Evaluate model performance using metrics such as confusion matrices and clustering quality measures. • Construct logic-based AI solutions using rules, constraints, and search strategies. • Design end-to-end workflows for predictive modeling, text mining, and syntactic parsing. This course is ideal for learners seeking to apply, analyze, and evaluate AI methods for data science, predictive analytics, and natural language processing applications using Python.

Syllabus

Foundations of Predictive Analytics

This module introduces learners to the fundamentals of predictive analytics with Python, focusing on essential machine learning methods used in real-world applications. Learners will begin by exploring the core concepts of predictive analysis, then progress into powerful ensemble algorithms such as Random Forest, Extremely Random Forest, and Adaboost, while addressing practical challenges like class imbalance. The module culminates in applying these models to a real-world case study on traffic prediction, ensuring learners gain both conceptual understanding and hands-on predictive modeling experience.

Unsupervised Learning & Pattern Discovery

This module explores the power of unsupervised learning techniques in Python for discovering hidden patterns in data. Learners will begin with the foundations of clustering methods such as Meanshift and advance into more sophisticated models like Affinity Propagation and Gaussian Mixture Models. The module emphasizes evaluating clustering quality metrics and applying these techniques in practical programming scenarios. By the end of this module, learners will be able to analyze, implement, and evaluate clustering algorithms for real-world applications in domains like customer segmentation, image processing, and pattern recognition.

Supervised Learning & Logic-Based AI

This module introduces learners to the fundamentals of supervised learning in Python and explores the integration of logic-based programming for AI problem-solving. The first part focuses on popular classification methods such as logistic regression, Naive Bayes, and Support Vector Machines (SVM), along with practical tools like the confusion matrix for evaluating predictive performance. The second part transitions into symbolic AI through logic programming, covering applications such as family tree reasoning, puzzle solving, heuristic search, local search techniques, and constraint satisfaction problems (CSPs). By the end of this module, learners will gain the ability to apply classification algorithms, interpret performance metrics, and construct logic-based solutions to real-world AI challenges.

Natural Language Processing with Python

This module provides a practical foundation in Natural Language Processing (NLP) using Python and NLTK. Learners will explore the complete NLP pipeline, from tokenization and text preprocessing to stemming, lemmatization, and segmentation. The module further introduces advanced tasks such as information extraction, chunking, chinking, and Named Entity Recognition (NER). Finally, learners will study parsing techniques using Context-Free Grammar (CFG), recursive descent parsing, and shift-reduce parsing to analyze sentence structure. By the end of this module, learners will be able to apply NLP techniques in Python for text analysis, information extraction, and grammar-based parsing of natural language.