AI & Predictive Analytics with Python

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Overview

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Artificial Intelligence and predictive analytics are transforming how organizations analyze data and solve complex problems. In this course, you will build practical skills in AI, predictive analytics, machine learning, and Natural Language Processing (NLP) with Python by progressing from foundational predictive modeling techniques to advanced AI methods. You will begin by exploring predictive analytics concepts, ensemble learning methods, hyperparameter optimization, and real-world prediction tasks. Next, you will apply unsupervised learning techniques, including Meanshift, Affinity Propagation, and Gaussian Mixture Models, to discover patterns in unlabeled data and evaluate clustering performance. The course then introduces supervised learning with Logistic Regression, Naive Bayes, and Support Vector Machines, while also exploring logic programming, heuristic search, local search, and constraint satisfaction for AI problem solving. Finally, you will build practical NLP workflows using Python and NLTK, covering text preprocessing, information extraction, Named Entity Recognition (NER), and grammar-based parsing techniques. Designed for learners interested in artificial intelligence, predictive analytics, data science, and NLP, this course emphasizes applying, analyzing, and evaluating AI techniques through practical Python-based examples. By the end of the course, you will be able to apply predictive models, evaluate clustering and classification algorithms, construct logic-based AI solutions, and develop end-to-end NLP workflows for structured and unstructured data analysis.

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.