Predictive Analytics: Basic Modeling Techniques
Master essential predictive analytics and machine learning techniques, from regression to neural networks, with hands-on practice in Python.
Master essential predictive analytics and machine learning techniques, from regression to neural networks, with hands-on practice in Python.
This comprehensive course introduces fundamental predictive analytics and machine learning concepts. Students gain hands-on experience with supervised learning techniques, including linear regression, logistic regression, decision trees, neural networks, and ensemble methods. The course emphasizes practical skills in data analysis, model fitting, and performance evaluation while exploring the foundations of artificial intelligence and machine learning operations.
Instructors:
English
English
Powered by
What you'll learn
Develop machine learning algorithms for classification and regression tasks
Master linear and logistic regression modeling techniques
Create and optimize decision trees for predictive analytics
Implement ensemble methods to improve model performance
Understand neural networks and deep learning fundamentals
Evaluate model performance using appropriate metrics
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams, Discussion prompts, 6 exercises, 4 quizzes
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
Closed caption
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This course provides a thorough introduction to predictive analytics and machine learning fundamentals. Students learn to develop and evaluate various machine learning models for both classification and regression tasks. The curriculum covers essential techniques including linear and logistic regression, decision trees, ensemble methods, and neural networks. Special emphasis is placed on practical implementation using Python, model performance evaluation, and understanding the CRISP-DM framework for data science projects.
Data Structures; Linear and Logistic Regression
Module 1
Assessing Models; Decision Trees
Module 2
Ensembles
Module 3
Neural Networks
Module 4
Fee Structure
Instructors
5 Courses
Prominent Educator and Author in Statistics and Data Science
Peter Bruce is the Chief Learning Officer at Elder Research and the Founder of the Institute for Statistics Education at Statistics.com, which specializes in online education in statistics and data analytics. He has co-authored several influential works, including Responsible Data Science (Wiley, 2021), Data Mining for Business Analytics (Wiley, 2006–2021), which has seen 13 editions and is used in over 600 universities worldwide, and Practical Statistics for Data Scientists (O'Reilly, 2nd ed. 2020). Additionally, he authored Introductory Statistics and Analytics: A Resampling Perspective (Wiley, 2015). With a background that includes degrees from Princeton and Harvard, as well as an MBA from the University of Maryland, Peter has leveraged his extensive knowledge to develop a comprehensive curriculum that addresses various aspects of statistics and analytics. His commitment to education is reflected in his role at the Institute, where he oversees course development and faculty recruitment while teaching courses on resampling methods
1 Course
Experienced Data Scientist Specializes in Insider Threat and Text Analytics
Veronica Carlan is a Data Scientist at Elder Research, bringing extensive experience from her work with the intelligence community since 2009, where she previously served as an intelligence analyst. Currently, she focuses on insider threat detection, text analytics, and application development and maintenance. Her expertise includes analyzing complex data sets to identify potential security risks and developing tools for effective data management. Veronica's background uniquely positions her to bridge the gap between data science and practical applications in security, enhancing organizational capabilities in monitoring and mitigating insider threats.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.