Executive Development Programme in Data Analysis: Gradient Descent
-- ViewingNowThe Executive Development Programme in Data Analysis, focusing on Gradient Descent, is a vital certificate course designed to empower professionals with in-demand data analysis skills. This programme emphasizes the optimization technique, Gradient Descent, which is essential for minimizing loss functions in machine learning models.
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โข Unit 1: Introduction to Data Analysis – Understanding the basics of data analysis, its importance, and the role of data analysis in business decision making.
โข Unit 2: Introduction to Gradient Descent – Understanding the concept of gradient descent, its importance, and how it is used in data analysis.
โข Unit 3: Mathematical Foundations of Gradient Descent – Covering the mathematical concepts and formulas used in gradient descent, including differentiation and optimization.
โข Unit 4: Implementing Gradient Descent – Learning how to implement gradient descent in practice, including choosing the learning rate and handling multiple variables.
โข Unit 5: Stochastic Gradient Descent – Understanding the concept of stochastic gradient descent, its benefits, and how it differs from standard gradient descent.
โข Unit 6: Advanced Gradient Descent Techniques – Covering advanced topics in gradient descent, such as momentum, adaptive learning rates, and regularization.
โข Unit 7: Practical Applications of Gradient Descent – Exploring real-world examples of how gradient descent is used in data analysis, including linear regression and logistic regression.
โข Unit 8: Troubleshooting Gradient Descent – Learning how to identify and solve common problems that can arise when implementing gradient descent, such as vanishing or exploding gradients.
โข Unit 9: Optimization Algorithms – Understanding alternative optimization algorithms, such as conjugate gradient and BFGS, and comparing them to gradient descent.
โข Unit 10: Evaluating Model Performance – Learning how to evaluate the performance of a model trained using gradient descent, including metrics such as mean squared error and accuracy.
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