Practical Machine Learning (Coursera)

Practical Machine Learning (Coursera)
Course Auditing
Categories
Effort
Certification
Languages
Misc

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Practical Machine Learning (Coursera)
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

This course is part of multiple programs

This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:

- Data Science: Statistics and Machine Learning Specialization

- Data Science Specialization


What You Will Learn

- Use the basic components of building and applying prediction functions

- Understand concepts such as training and tests sets, overfitting, and error rates

- Describe machine learning methods such as regression or classification trees

- Explain the complete process of building prediction functions


Syllabus


WEEK 1

Prediction, Errors, and Cross Validation

This week will cover prediction, relative importance of steps, errors, and cross validation.


WEEK 2

The Caret Package

This week will introduce the caret package, tools for creating features and preprocessing.


WEEK 3

Predicting with trees, Random Forests, & Model Based Predictions

This week we introduce a number of machine learning algorithms you can use to complete your course project.


WEEK 4

Regularized Regression and Combining Predictors

This week, we will cover regularized regression and combining predictors.



6
Average: 6 ( 4 votes )

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.