Supervised Machine Learning: Classification

4.9

Learners: 10,800

Instructor: /

Duration:

This course introduces learners to supervised Machine Learning, specifically the classification modeling family. Offered by IBM Skills Network, this free course provides an overview of the fundamentals of Machine Learning. ▼▲

Course Feature Course Overview Pros & Cons Course Provider Discussion and Reviews

Go to class

Course Feature

Cost:

Free

Provider:

Udemy

Certificate:

No Information

Language:

English

Course Overview

❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 06th, 2023]

This course provides an overview of supervised machine learning classification. Participants will learn how to train predictive models to classify categorical outcomes and how to compare different models using error metrics. The hands-on section of the course will focus on best practises for classification, such as train and test splits and handling data sets with unbalanced classes. Participants will also learn about the uses and applications of classification and classification ensembles, as well as how to explain and apply logistic regression models, decision tree and tree-ensemble models, and other ensemble classification methods. They will also learn how to compare and select the classification model that best fits their data using a variety of error metrics, as well as how to use oversampling and undersampling techniques to handle unbalanced classes in a data set.

[Applications]
The application of this course can be seen in a variety of areas, such as medical diagnosis, credit scoring, and fraud detection. After completing this course, learners will be able to use supervised machine learning techniques to classify categorical outcomes, compare different models using error metrics, and handle data sets with unbalanced classes. They will also be able to explain and apply logistic regression models, decision tree and tree-ensemble models, and other ensemble classification methods. Furthermore, learners will be able to compare and select the classification model that best fits their data using a variety of error metrics, as well as use oversampling and undersampling techniques to handle unbalanced classes in a data set.

[Career Paths]
1. Data Scientist: Data Scientists use predictive models to classify categorical outcomes and compare different models using error metrics. They are also responsible for developing and deploying machine learning algorithms to solve complex problems. Data Scientists must have a strong understanding of statistics, mathematics, and computer science. As the demand for data-driven insights continues to grow, the demand for Data Scientists is expected to increase.

2. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning algorithms to solve complex problems. They must have a strong understanding of statistics, mathematics, and computer science. They must also be able to design and implement machine learning models and algorithms. As the demand for data-driven insights continues to grow, the demand for Machine Learning Engineers is expected to increase.

3. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for developing and deploying artificial intelligence algorithms to solve complex problems. They must have a strong understanding of statistics, mathematics, and computer science. They must also be able to design and implement AI models and algorithms. As the demand for data-driven insights continues to grow, the demand for Artificial Intelligence Engineers is expected to increase.

4. Business Intelligence Analyst: Business Intelligence Analysts are responsible for analyzing data to identify trends and insights. They must have a strong understanding of statistics, mathematics, and computer science. They must also be able to design and implement data analysis models and algorithms. As the demand for data-driven insights continues to grow, the demand for Business Intelligence Analysts is expected to increase.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path focuses on the fundamentals of computer science, such as algorithms, data structures, programming languages, and software engineering. It also covers topics such as artificial intelligence, machine learning, and natural language processing. With the increasing demand for data-driven decision making, this degree path is becoming increasingly popular and is expected to continue to grow in the future.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of intelligent systems and their applications. It covers topics such as machine learning, natural language processing, computer vision, robotics, and more. With the increasing demand for AI-driven solutions, this degree path is becoming increasingly popular and is expected to continue to grow in the future.

3. Master of Science in Data Science: This degree path focuses on the analysis and interpretation of large datasets. It covers topics such as data mining, machine learning, predictive analytics, and more. With the increasing demand for data-driven decision making, this degree path is becoming increasingly popular and is expected to continue to grow in the future.

4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of advanced machine learning algorithms and their applications. It covers topics such as deep learning, reinforcement learning, natural language processing, and more. With the increasing demand for AI-driven solutions, this degree path is becoming increasingly popular and is expected to continue to grow in the future.

Course Syllabus

Introduction: What is Classification?

Introduction to Logistic Regression

Classification with Logistic Regression

Logistic Regression with Multi-Classes

Implementing Logistic Regression Models

Confusion Matrix, Accuracy, Specificity, Precision, and Recall

Classification Error Metrics: ROC and Precision-Recall Curves

Implementing the Calculation of ROC and Precision-Recall Curves

Pros & Cons

Pros Cons

Comprehensive course content
Excellent guided demos
Clear and precise presentations
Lots of practical content
Comprehensive theory

Data leakage in predictions
Complex topics not explained properly
Instructor reads off slides

Course Provider

Provider Udemy's Stats at AZClass

Supervised Machine Learning: Classification introduces learners to supervised machine learning, specifically the classification modeling family. Learners can learn how to train predictive models to classify classification results and how to use error metrics to compare different models. They can learn best practices for classification, such as train and test splits and handling datasets with imbalanced classes. They can distinguish the uses and applications of classification and classification ensembles, and interpret and apply logistic regression models, decision trees and tree ensemble models, and other ensemble classification methods. Learners can also use various error metrics to compare and choose the best classification model for their data and use oversampling and undersampling techniques to deal with imbalanced classes in the dataset.