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
Machine Learning With Big Data
This course is part of Big Data Specialization
Instructors: Mai Nguyen
79,240 already enrolled
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Gain insight into a topic and learn the fundamentals.
2,504 reviews
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
96%
Most learners liked this course
Skills you'll gain
- Data Wrangling
- Machine Learning Algorithms
- Regression Analysis
- Data Preprocessing
- Machine Learning
- Machine Learning Methods
- Applied Machine Learning
- Unsupervised Learning
- Machine Learning Software
- Data Transformation
- Model Evaluation
- Model Training
- Statistical Analysis
- Big Data
- Data Analysis
- Descriptive Statistics
- Data Mining
- Exploratory Data Analysis
Tools you'll learn
Details to know
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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.
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- Develop job-relevant skills with hands-on projects
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There are 7 modules in this course
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Showing 3 of 2504
RC
Reviewed on Aug 31, 2018
Amazing training on ML for people starting their first experiences with the topic. Practical and easy to understand examples that can be further extended by the student.
TA
Reviewed on Dec 26, 2019
The fact that the assignments are graded means that there’s incentive to work on them, solve problems, and ask questions. Traditional online courses don’t offer that incentive.
NP
Reviewed on Jul 2, 2019
This course provides you starting point to start working in KNIME and helps you in explaining basic statistics concepts along with its application in KNIME and Spark
