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Practical Predictive Analytics: Models and Methods
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Practical Predictive Analytics: Models and Methods

Master statistical experiment design and machine learning methods for effective predictive analytics and data science.

Course Cost

Free course

Intermediate

Skill Level

7 Hours

Self-paced lessons

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Science at Scale Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

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4.1

37,585 Enrolled

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English

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olive-leaves-logo

4.1

37,585 Enrolled

olive-leaves-logo

English

What you'll learn

  • Design and analyze statistical experiments effectively

  • Apply resampling methods for robust statistical analysis

  • Implement classification methods of varying complexity

  • Master supervised and unsupervised learning techniques

  • Understand optimization methods including gradient descent

Skills you'll gain

Random Forest
Predictive Analytics
Machine Learning
R Programming
Statistical Inference
Hypothesis Testing
Supervised Learning
Unsupervised Learning

This course includes:

4.83 Hours PreRecorded video

1 quiz

Access on Mobile, Tablet, Desktop

FullTime access

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Certificate

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Share your certificate with prospective employers and your professional network on LinkedIn.

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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.

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There are 4 modules in this course

This comprehensive course focuses on practical applications of statistical experiment design and analytics in data science. Students learn to design effective experiments, analyze results using modern methods, and apply machine learning techniques to real-world problems. The curriculum covers statistical inference, supervised and unsupervised learning, and optimization methods. Through hands-on exercises in R programming, learners develop skills in implementing predictive analytics solutions and understanding common pitfalls in statistical arguments.

Practical Statistical Inference

Module 1 · 2 Hours to complete

Supervised Learning

Module 2 · 2 Hours to complete

Optimization

Module 3 · 41 Minutes to complete

Unsupervised Learning

Module 4 · 1 Hours to complete

Fee Structure

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Practical Predictive Analytics: Models and Methods

Intermediate

Skill Level

7 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

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.