Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.
Machine Learning With Big Data
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Machine Learning With Big Data
This course is part of Big Data Specialization
Instructors: Mai Nguyen
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2,506 reviews
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
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Skills you'll gain
- Model Evaluation
- Unsupervised Learning
- Data Wrangling
- Data Mining
- Descriptive Statistics
- Machine Learning Software
- Machine Learning Algorithms
- Data Analysis
- Model Training
- Regression Analysis
- Data Preprocessing
- Data Transformation
- Machine Learning Methods
- Applied Machine Learning
- Machine Learning
- Statistical Analysis
- Big Data
- Exploratory Data Analysis
Tools you'll learn
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Assessments
11 assignments
Taught in English
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This course is part of the Big Data 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 7 modules in this course
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PT
Reviewed on Jan 8, 2017
The course was the best introduction I had for machine learning. Helped me a lot to understand different concepts from people who already know about the subject and I didn't have any idea.
LB
Reviewed on Oct 18, 2017
This is by far the most enjoyable course of the big data specialization, i loved the teaching approach and enjoyed all the hands on exercises. Great job!
JA
Reviewed on May 9, 2017
Great overview about the machine learning in general. There are still lots not covered specially the Neural Network algorithms. Learning Spark MLlib was great advantage of this course.
