Artificial Intelligence with Python: Foundations to Projects

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.

By completing this beginner-friendly course, learners will be able to set up Python environments, manipulate data using NumPy, and create insightful visualizations with Matplotlib and Seaborn. Designed for those starting their journey in Artificial Intelligence, the course ensures students build a strong computational foundation before progressing to advanced AI concepts. Through step-by-step guidance, learners will first configure Anaconda Navigator and Jupyter Notebook for a seamless workflow, then apply NumPy for array functions, indexing, and filtering techniques essential in AI data handling. Moving forward, they will implement Matplotlib for basic plots and transition to Seaborn for high-level, visually appealing statistical visualizations, including scatter plots, heatmaps, and box plots. What makes this course unique is its practical, project-oriented approach that blends setup, numerical computation, and data visualization into one cohesive learning path. By the end, learners will have both the technical skills and the confidence to explore real-world AI projects, effectively preparing them for more advanced machine learning and deep learning studies.