Name: Supervised Machine Learning: Classification
Rating: 5 (8 reviews)

Module 1: Logistic Regression Logistic regression is one of the most studied and widely used classification algorithms, probably due to its popularity in regulated industries and financial settings. Although more modern classifiers might likely output models with higher accuracy, logistic regressions are great baseline models due to their high interpretability and parametric nature. This module will walk you through extending a linear regression example into a logistic regression, as well as the most common error metrics that you might want to use to compare several classifiers and select that best suits your business problem.Module 2: K Nearest Neighbors K Nearest Neighbors is a popular classification method because they are easy computation and easy to interpret. This module walks you through the theory behind k nearest neighbors as well as a demo for you to practice building k nearest neighbors models with sklearn.Module 3: Support Vector Machines This module will walk you through the main idea of how support vector machines construct hyperplanes to map your data into regions that concentrate a majority of data points of a certain class. Although support vector machines are widely used for regression, outlier detection, and classification, this module will focus on the latter.Module 4: Decision Trees Decision tree methods are a common baseline model for classification tasks due to their visual appeal and high interpretability. This module walks you through the theory behind decision trees and a few hands-on examples of building decision tree models for classification. You will realize the main pros and cons of these techniques. This background will be useful when you are presented with decision tree ensembles in the next module.Module 5: Ensemble Models Ensemble models are a very popular technique as they can assist your models be more resistant to outliers and have better chances at generalizing with future data. They also gained popularity after several ensembles helped people win prediction competitions. Recently, stochastic gradient boosting became a go-to candidate model for many data scientists.This model walks you through the theory behind ensemble models and popular tree-based ensembles.Module 6: Modeling Unbalanced Classes Some classification models are better suited than others to outliers, low occurrence of a class, or rare events. The most common methods to add robustness to a classifier are related to stratified sampling to re-balance the training data. This module will walk you through both stratified sampling methods and more novel approaches to model data sets with unbalanced classes.

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Coursera offers this short-term course in collaboration with IBM, a leading cloud platform and cognitive solutions company and one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. It is one of the largest technology and consulting employers globally, with more than 350,000 employees serving clients in 170 countries. It partners with 200+ leading universities and companies to offer flexible, affordable, job-relevant online learning to professionals worldwide.

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Flexibility- Self/Instructor paced

Pedagogy

The professors who teach this "Supervised Machine Learning: Classification" course are from well-known universities. Thus, the intermediate level course gives learners the chance to study with international teachers and learn about other cultures. Self-paced discussions are part of the instruction, giving learners the chance to learn online, interact and learn from different perspectives. The program prepares learners for real-world careers.

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Projects- Capstone, Industry-Simulation

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Career impact

This certification course may assist learners in elevating skillsets and careers, making it simpler to advance or transfer within or across jobs. A practical-oriented program gives learners thorough knowledge and helps all enrolled learners through a dedicated methodology. They give learners critical career insights, assisting them in developing a well-rounded resume. This certification training will allow learners to build on the knowledge to create unique projects with activities.

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Pricing

The course is 11 hours long and can be accessed on a laptop or a desktop. It costs more than similar courses in the same field. However, this course supports the business world, practical learning, instructor-moderated conversations, and an approved degree. By the end of the course, learners can better understand Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

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Assessment

Program certificate is given to the learners who complete the course successfully, and a merit certificate is given to the learners. Post enrolment, learners are provided access to the study material and given assignments every week. The assignments focus on both the practical and theoretical learning of the learners. The assessments are made part of the programs to ensure that all learners are engaged throughout the learning process.

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Coursera provides this course for those who want to learn more about modelling families of supervised Machine Learning. It offers extensive certification training. Learners with a bachelor's degree, professionals in their mid-career years, and developers are encouraged to enrol in this course. One may work as an expert in various settings, including corporations, technology organisations, start-ups, and government bodies. Hands-On Training, Instructor-Moderated Discussions, CapStone Project and International Faculty are included in the curriculum to provide the best possible learning experience.

This course will teach you about the most important type of supervised machine-learning modelling families: classification. This course will show you how to create predictive models that can categorize outcomes. For comparisons between models, you'll also be able to use error metrics. This course covers practical aspects of classification. This course will teach you how to create and test split models, and how to handle data sets that contain unbalanced classes.

Mark J Grover

Miguel Maldonado

Supervised Machine Learning: Classification by Coursera is top course for Artificial Intelligence & Data Science. It is an Online course and is Paid

Supervised Machine Learning: Classification

Supervised Machine Learning: Classification

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Mark J Grover

Miguel Maldonado

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