À propos de ce cours
4.3
3 notes
100 % en ligne

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Heures pour terminer

Approx. 38 heures pour terminer

Recommandé : 6 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais...
100 % en ligne

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Heures pour terminer

Approx. 38 heures pour terminer

Recommandé : 6 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais...

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
Heures pour terminer
1 heure pour terminer

Course Orientation

You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course....
Reading
2 vidéos (Total 9 min), 4 lectures, 1 quiz
Video2 vidéos
Meet Professor Brunner4 min
Reading4 lectures
Syllabus10 min
About the Discussion Forums10 min
Updating Your Profile10 min
Social Media10 min
Quiz1 exercice pour s'entraîner
Orientation Quiz10 min
Heures pour terminer
9 heures pour terminer

Module 1: Introduction to Machine Learning

This module provides the basis for the rest of the course by introducing the basic concepts behind machine learning, and, specifically, how to perform machine learning by using Python and the scikit learn machine learning module. First, you will learn how machine learning and artificial intelligence are disrupting businesses. Next, you will learn about the basic types of machine learning and how to leverage these algorithms in a Python script. Third, you will learn how linear regression can be considered a machine learning problem with parameters that must be determined computationally by minimizing a cost function. Finally, you will learn about neighbor-based algorithms, including the k-nearest neighbor algorithm, which can be used for both classification and regression tasks....
Reading
4 vidéos (Total 47 min), 3 lectures, 2 quiz
Video4 vidéos
Introduction to Machine Learning14 min
Introduction to Linear Regression14 min
Introduction to k-nn12 min
Reading3 lectures
Module 1 Overview10 min
Lesson 1-1 Readings10 min
Lesson 1-2 Readings10 min
Quiz1 exercice pour s'entraîner
Module 1 Graded Quiz20 min
Semaine
2
Heures pour terminer
9 heures pour terminer

Module 2: Fundamental Algorithms

This module introduces several of the most important machine learning algorithms: logistic regression, decision trees, and support vector machine. Of these three algorithms, the first, logistic regression, is a classification algorithm (despite its name). The other two, however, can be used for either classification or regression tasks. Thus, this module will dive deeper into the concept of machine classification, where algorithms learn from existing, labeled data to classify new, unseen data into specific categories; and, the concept of machine regression, where algorithms learn a model from data to make predictions for new, unseen data. While these algorithms all differ in their mathematical underpinnings, they are often used for classifying numerical, text, and image data or performing regression in a variety of domains. This module will also review different techniques for quantifying the performance of a classification and regression algorithms and how to deal with imbalanced training data....
Reading
5 vidéos (Total 52 min), 4 lectures, 2 quiz
Video5 vidéos
Introduction to Fundamental Algorithms3 min
Introduction to Logistics Regression14 min
Introduction to Decision Trees15 min
Introduction to Support Vector Machine13 min
Reading4 lectures
Module 2 Overview10 min
Lesson 2-1 Readings10 min
Lesson 2-3 Readings10 min
Lesson 2-4 Readings10 min
Quiz1 exercice pour s'entraîner
Module 2 Graded Quiz20 min
Semaine
3
Heures pour terminer
8 heures pour terminer

Module 3: Practical Concepts in Machine Learning

This module introduces several important and practical concepts in machine learning. First, you will learn about the challenges inherent in applying data analytics (and machine learning in particular) to real world data sets. This also introduces several methodologies that you may encounter in the future that dictate how to approach, tackle, and deploy data analytic solutions. Next, you will learn about a powerful technique to combine the predictions from many weak learners to make a better prediction via a process known as ensemble learning. Specifically, this module will introduce two of the most popular ensemble learning techniques: bagging and boosting and demonstrate how to employ them in a Python data analytics script. Finally, the concept of a machine learning pipeline is introduced, which encapsulates the process of creating, deploying, and reusing machine learning models. ...
Reading
5 vidéos (Total 40 min), 3 lectures, 2 quiz
Video5 vidéos
Introduction to Modeling Success6 min
Introduction to Bagging11 min
Introduction to Boosting9 min
Introduction to ML Pipelines8 min
Reading3 lectures
Module 3 Overview10 min
Lesson 3-1 Readings10 min
Lesson 3-2 Readings10 min
Quiz1 exercice pour s'entraîner
Module 3 Graded Quiz20 min
Semaine
4
Heures pour terminer
9 heures pour terminer

Module 4: Overfitting & Regularization

This module introduces the concept of regularization, problems it can cause in machine learning analyses, and techniques to overcome it. First, the basic concept of overfitting is presented along with ways to identify its occurrence. Next, the technique of cross-validation is introduced, which can mitigate the likelihood that overfitting can occur. Next, the use of cross-validation to identify the optimal parameters for a machine learning algorithm trained on a given data set is presented. Finally, the concept of regularization, where an additional penalty term is applied when determining the best machine learning model parameters, is introduced and demonstrated for different regression and classification algorithms....
Reading
5 vidéos (Total 48 min), 4 lectures, 2 quiz
Video5 vidéos
Introduction to Overfitting4 min
Introduction to Cross-Validation13 min
Introduction to Model-Selection16 min
Introduction to Regularization8 min
Reading4 lectures
Module 4 Overview10 min
Lesson 4-1 Readings10 min
Lesson 4-2 Readings10 min
Lesson 4-3 Readings10 min
Quiz1 exercice pour s'entraîner
Module 4 Graded Quiz20 min

Enseignant

Avatar

Robert Brunner

Professor
Accountancy
Graduation Cap

Start working towards your Master's degree

This cours is part of the 100% online Master of Science in Accountancy (iMSA) from University of Illinois at Urbana-Champaign. If you are admitted to the full program, your courses count towards your degree learning.

À propos de University of Illinois at Urbana-Champaign

The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs. ...

Foire Aux Questions

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