AI & Predictive Analytics with Python

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.