Most modern Enterprises across variety of verticals that have become “data rich” due to the first wave of digital transformation (Automation) are now investing heavily to become “AI First” in their second wave of digital transformation. They are aspiring to migrate from broadcast, batch, reactive, tactical decision-making to personalized, real-time, proactive, strategic decision-making. This has the potential to improve the quality of service they provide to their customers, the efficiency and reliability of their operations, optimal utilization of their resources – personnel, money, and materials, and adapting their ability to make dynamic decisions with evolving contexts. This in turn leads to systemic growth in customer satisfaction, net profitability, and market share.
This unprecedented paradigm shifts in “how Enterprises make decisions” is enabled by a proven suite of algorithms, techniques, frameworks, processes, and platforms that have emerged in the varied fields of Artificial Intelligence, Machine Learning, Data Mining, Statistical Pattern Recognition, etc. over the last several decades.
In this course, we will study a plethora of frameworks, algorithms, techniques, tools, and guiding principles to (a) understand any type of data better, (b) combine it with the domain knowledge appropriately, (d) find deeper insights hidden in the data, (d) build a wide variety of powerful descriptive, predictive, and prescriptive models, and (e) continuously improve them with feedback data coming from the environment they are deployed in.