Brief Introduction
Predictive analytics has proved to be a powerful tool to help businesses analyze data and predict future outcomes and trends. In this course, you’ll learn how to use classification predictive models to solve business problems such as predicting whether or not a customer will respond to a marketing campaign, the likelihood of default on a loan, or which product a customer will buy. You'll learn this through improving your fluency in Alteryx, a data analytics tool that enables you prepare, blend,Course Summary
Learn how to build and evaluate classification models like logistic regression and decision trees in this course. Gain practical experience with real-world datasets and apply your knowledge to solve business problems.Key Learning Points
- Understand the fundamentals of classification models and how they work
- Build and evaluate different types of classification models
- Apply classification models to real-world business problems
Related Topics for further study
Learning Outcomes
- Build and evaluate classification models using real-world datasets
- Apply classification models to solve business problems
- Understand the strengths and weaknesses of different types of classification models
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with statistical concepts
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
Similar Courses
- Regression Models
- Data Analysis with R
Related Education Paths
Notable People in This Field
- Andrew Ng
- Sebastian Thrun
Related Books
Description
Learn how to use classification predictive models to solve business problems involving non-numeric data.Requirements
- No programming experience required Interested in using data to make better business decisions Alteryx license (provided to nanodegree students at no cost, compatible with Windows only) See the Technology Requirements for using Udacity.
Knowledge
- Instructor videosLearn by doing exercisesTaught by industry professionals
Outline
- lesson 1 Introduction to Classification Modeling Learn the key terminology used in predictive modeling. Learn the how to choose variables to use in a predictive model. Practice preparing a dataset for modeling. lesson 2 Binary Classification Models Learn how to use models to predict data with two possible outcomes. Use logistic regression and decision tree models. Learn how to compare models and interpret results. lesson 3 Non-Binary Classification Models Learn how to use models to predict categorical data with three or more possible outcomes. Learn how to use decision tree forest and boosted models. Compare models and interpret results.
Summary of User Reviews
Read reviews for the Classification Models course on Udacity. Students have found the course to be valuable and informative. One key aspect that many users thought was good is the practical approach to learning.Pros from User Reviews
- Practical approach to learning
- Clear and concise explanations
- Hands-on projects to reinforce concepts
- Great instructor support
- Good pacing
Cons from User Reviews
- Some topics could have been covered in more detail
- Some exercises were too easy
- No real-time interaction with other students
- Limited feedback on assignments
- Not suitable for beginners
Lazy Trading Part 6: Detect Market status with AI
- 4.5
-
![]()
![]()
![]()
![]()
![]()
Learn to use Supervised Deep Learning modelling to detect patterns of Financial Assets Several algorithms will be built by performing basic data cycle 'data input-data manipulation - analysis -output'....
Natural Language Processing (NLP) in Python for Beginners
- 4.6
-
![]()
![]()
![]()
![]()
![]()
Text Cleaning, Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, LSTM for Sentiment, Emotion, Spam & CV Parsing Welcome to KGP Talkie's Natural Language Processing course....
Machine Learning: Classification
- 4.7
-
![]()
![]()
![]()
![]()
![]()
Case Studies: Analyzing Sentiment & Loan Default Prediction . . ). In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks....
Meaningful Predictive Modeling
- 4.4
-
![]()
![]()
![]()
![]()
![]()
This course will help us to evaluate and compare the models we have developed in previous courses. Or how do we decide which of two regression algorithms is better?...
