Data for Machine Learning

Overview

Title:

Data for Machine Learning

Description:

Dive into the essentials of data utilization in machine learning with this comprehensive course. Discover the significant role of data in various phases of machine learning, including learning, training, and operational phases.

Gain expertise in identifying critical elements of data, understanding and correcting biases, and implementing strategies to enhance your model's generalizational capabilities. Learn to mitigate the effects of overfitting, apply rigorous testing and validation techniques, and refine model accuracy through strategic feature engineering.

Additionally, explore the effect of algorithmic parameters on the strength of your models.

This course is designed for individuals with at least a beginner-level understanding of Python programming, capable of reading and modifying existing code and familiar with basic programming constructs like conditionals, loops, and data structures such as lists, dictionaries, and arrays. A fundamental grasp of linear algebra and statistics, including vector notation and the basics of probability distributions, is also essential.

University:

Alberta Machine Intelligence Institute

Provider:

Coursera

Categories:

Statistics & Probability Courses, Machine Learning Courses, Data Analysis Courses, Linear Algebra Courses

This course is a part of the Applied Machine Learning Specialization, an exclusive offering by Coursera in collaboration with the Alberta Machine Intelligence Institute, designed to deepen your knowledge and skills in machine learning.

---

Taught by

Anna Koop

---

Subjects