Built generative AI applications and chatbots using LLMs, RAG, IBM watsonX, and Hugging Face. Developed voice-enabled and web-based AI interfaces with Flask, Gradio, and speech technologies (STT/TTS).

Covered the full Python application development lifecycle, including modules, unit testing, and PEP8 best practices. Built and deployed AI-powered web applications using Flask and IBM Watson AI Libraries.

Established Python fundamentals covering data structures, OOP, and exception handling. Applied Pandas and NumPy for data analysis and used REST APIs and BeautifulSoup for web data extraction.

Applied probability distributions, MLE, and MAP estimation to machine learning problems. Assessed model performance using confidence intervals and statistical margin of error.

Applied derivatives and gradients to analytically and numerically optimize machine learning functions. Implemented gradient descent across neural network architectures with various activation and cost functions.

Worked with vectors, matrices, and operations including dot product, inverse, and determinants. Applied eigenvalues, eigenvectors, and linear transformations to machine learning problems.

Applied supervised and unsupervised ML algorithms — regression, classification, clustering, and dimensionality reduction — using Python and scikit-learn. Built and evaluated end-to-end ML solutions on real-world datasets.

Demonstrated proficiency in building and evaluating machine learning models using Python and scikit-learn.

Used Matplotlib, Seaborn, and Folium to create and customize visualizations for small and large-scale datasets.

Analyzed data using NumPy, Pandas, and SciPy, and applied machine learning techniques with scikit-learn in a Jupyter-based environment.

Explored generative AI capabilities and real-world use cases across industries, covering models and tools for text, code, image, audio, and video generation.

Examined AI fundamentals including machine learning, deep learning, and neural networks applied to real-world scenarios. Analyzed generative AI’s role in business innovation and designed AI solutions with embedded ethical considerations.

Demonstrated the ability to write Python scripts and perform hands-on data analysis using IBM’s Jupyter-based lab environment.

Gained a solid understanding of data analytics concepts, data science methodologies, and the tools used in the data ecosystem, including hands-on practice with IBM Watson Studio.

Built foundational knowledge in AI concepts including NLP, computer vision, machine learning, deep learning, and AI ethics, with hands-on model execution in IBM Watson Studio.

Built Angular applications using TypeScript, covering components, directives, services, data binding, routing, and reactive forms. Implemented client-server communication via HTTP and REST APIs, with an introduction to RxJS and reactive programming.

Developed React applications using JavaScript ES6, covering components, React Router, Redux state management, and controlled forms. Integrated REST APIs via Fetch for client-server communication.

Covered the end-to-end UX design process — from user empathy and pain point analysis to wireframing, prototyping, and usability testing — for web and mobile products.

Identified and mitigated application security vulnerabilities using secure coding practices aligned with industry standards. Applied monitoring and observability techniques to protect systems against data breaches and leaks.