Learning outcomes are concrete, formal statements that state what students are expected to learn in a course. Program Outcomes (POs) are the knowledge, skills, and attitudes that students should have at the end
This helps us see how well our algorithm performs, usually supervised learning algorithms. It should be structured into 4 different parts: True Positive (TP): What we predicted to be positive and is actually positive. True Negative (TN): What we predicted to be negative and is actually ...
Remember, an interview is a two-way street.Of course, it is essential to provide correct and articulate answers to an interviewer's technical questions, but don't overlook the value of asking your own questions. Build a shortlist of these questions before the appointment to ask at opportune ...
4 Steps to develop iterative algorithm , types of Iterative Algorithm 50 minutes 1 Black Board 5 Recursion - Forward and Backward Recursion – Examples 50 minutes 1 Black Board 6 Tower of Hanoi – Analysis 50 minutes 1 Black Board 7 Check list for Recursive Algorithms, Stack Frame 50 minutes...
Our Data Science course in Bangalore will help you master all the essential skills needed to pursue a successful career. You will learn Python programming to clean, manipulate, modify and visualize data and predict business outcomes. Master Statistics, Machine Learning and training AI using Supervised...
Prescriptive analytics is whereartificial intelligenceandbig datacombine to help predict outcomes and identify what actions to take. This category of analytics can be further broken down intooptimizationandrandom testing. Using advancements in machine learning (ML), prescriptive analytics can help answer ...
These systems do not just respond to prompts; they evaluate, revise, and reflect on their actions, often using past experiences to guide current decisions. But to solve more complex, multi-step problems, agents need structure. That’s where hierarchical and recursive designs come into play. ...
Prescriptive analytics builds on predictive analytics by helping you understand why future outcomes might happen. It uses data from sources like statistics, machine learning, and data mining to reveal the best course of action. Example: A logistics company optimizes delivery routes using historical dat...
The prediction accuracy achieved by the algorithm for the UK-Gri dataset (MAE = 0.6898 gC m−2 d−1; RMSE = 0.9558; R2 = 0.8903) and the hybrid UK-Gri plus NL-Loo dataset (MAE = 0.5072 gC m−2 d−1; RMSE = 0.7746; R2 = 0.9149) substantially outperform the NEE prediction ...
Social network analysis (SNA): To interpret and analyze the structure and relations in CSCL tasks. • Process mining: To extract process-related knowledge from event logs recorded by an information system to have a clear visual representation of the whole process. • Distillation of data for ...