Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.
Practical Predictive Analytics: Models and Methods
Practical Predictive Analytics: Models and Methods
This course is part of Data Science at Scale Specialization
Instructor: Bill Howe
Access provided by The Technological Institute of Textile and Sciences
39,109 already enrolled
Gain insight into a topic and learn the fundamentals.
4.1
(322 reviews)
7 hours to complete
Flexible schedule
Learn at your own pace
Skills you'll gain
- Predictive Analytics
- Statistical Modeling
- Network Analysis
- Unsupervised Learning
- Graph Theory
- Statistical Analysis
- Machine Learning Algorithms
- Statistical Methods
- Decision Tree Learning
- R Programming
- Statistical Inference
- Random Forest Algorithm
- Probability & Statistics
- Supervised Learning
- Sampling (Statistics)
- Machine Learning
- Big Data
Details to know
Shareable certificate
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Taught in English
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This course is part of the Data Science at Scale Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 4 modules in this course
Learn the basics of statistical inference, comparing classical methods with resampling methods that allow you to use a simple program to make a rigorous statistical argument. Motivate your study with current topics at the foundations of science: publication bias and reproducibility.
What's included
28 videos
Follow a tour through the important methods, algorithms, and techniques in machine learning. You will learn how these methods build upon each other and can be combined into practical algorithms that perform well on a variety of tasks. Learn how to evaluate machine learning methods and the pitfalls to avoid.
What's included
26 videos1 reading1 assignment
You will learn how to optimize a cost function using gradient descent, including popular variants that use randomization and parallelization to improve performance. You will gain an intuition for popular methods used in practice and see how similar they are fundamentally.
What's included
11 videos
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
What's included
4 videos1 peer review
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Instructor
Instructor ratings
4.8 (5 ratings)Offered by
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4.1
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Showing 3 of 322
RS
4
Reviewed on Jun 12, 2017
Very good approach to each method; the assignments are a good test for the topics.
WL
5
Reviewed on Jun 5, 2016
A quick overview of technology terms used for Machine Learning, and gentle introduction into learning through Kaggle.
DS
5
Reviewed on Jul 2, 2020
Hands on practices are very good. learning predictive model was a challenge.
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