Laying the groundwork for understanding uncertainty, randomness, and their applications in modern analytics. Topics include probability, statistics, stochastic processes, Markov Chains, and Bayesian inference.
Guiding learners through the intricacies of modeling sequential data, including ARIMA/SARIMA/SARIMAX, state-space models, and advanced methods like LSTMs for forecasting and anomaly detection.
From fundamentals like regression and classification to advanced techniques such as ensemble methods, neural networks, and reinforcement learning. I emphasize the theoretical underpinnings of algorithms alongside practical implementation.
Exploring the latest trends in AI and ML, including generative models, deep learning architectures, and their applications across industries. I also focus on ethical considerations and interpretability in AI.
I employ a hands-on, example-driven teaching style, incorporating real-world datasets and tools like R (IDE: RStudio/ Interface: Exploratory) and Python interfaces (Orange ML). By blending theory with practical exercises, I ensure learners not only understand the “how” but also the “why” behind the methods. This approach prepares them to apply their knowledge effectively in both academic and professional settings.
Whether teaching through structured courses, webinars, or training programs, my goal is to inspire curiosity, instill confidence, and nurture a deep understanding of the subjects. With a focus on problem-solving and critical thinking, I aim to equip students with skills that endure as technology evolves.