subject

Machine Learning With Big Data

 with  Paul Rodriguez, Natasha Balac and Andrea Zonca
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.

At the end of the course, you will be able to:
• Design an approach to leverage data using the steps in the machine learning process.
• Apply machine learning techniques to explore and prepare data for modeling.
• Identify the type of machine learning problem in order to apply the appropriate set of techniques.
• Construct models that learn from data using widely available open source tools.
• Analyze big data problems using scalable machine learning algorithms on Spark.

Syllabus

Welcome


Introduction to Machine Learning with Big Data


Data Exploration


Data Preparation


Classification


Evaluation of Machine Learning Models


Regression, Cluster Analysis, and Association Analysis


13 Student
reviews
Cost Free Online Course (Audit)
Pace Upcoming
Provider Coursera
Language English
Certificates Paid Certificate Available
Calendar 5 weeks long
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FAQ View All
What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

13 reviews

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8 out of 8 people found the following review useful
a year ago
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Anonymous completed this course.
Spent 1 hour on the entire course. No practical assignments whatsoever. No new skills learnt. Lecturers read information which are freely available on wikipedia without explaining in-depth anything.

Quizzes are extremely lazy, in the form of true-false format. Some questions even repeat. Extremely easy learning experience which is not at all worth the time.
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2 out of 2 people found the following review useful
10 months ago
Wichaiditsornpon@gmail.com dropped this course and found the course difficulty to be very easy.
too easy too short if you want ML stuff you have to look elsewhere if you very new to ML you will find this has something to learn but it's not informative enough Andrews ng's machine learning course is recommended for anyone who wants to know machine learning and if you want to know what is the foundation of machin Read More
too easy too short if you want ML stuff you have to look elsewhere if you very new to ML you will find this has something to learn but it's not informative enough Andrews ng's machine learning course is recommended for anyone who wants to know machine learning and if you want to know what is the foundation of machine learning then take introduction to statistical learning(ISL) book and course here

http://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/

in my opinion, that is the best course I ever take on ML, Yes Andrews's course come second

if it not enough for you so if you want more advance stuff then read The Elements of Statistical Learning book with same author with ISL book is consider to be the best too
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4 out of 4 people found the following review useful
a year ago
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Anonymous completed this course.
This course makes a joke out of its topic name. Some examples: --Quizzes are too trivial. Some even repeat questions. Some questions are wrong. --Videos are too much ridden with facts all over the place, yet offer little evaluation and explanation. For example, why do we use this tool, what purpose is this concept go Read More
This course makes a joke out of its topic name. Some examples:

--Quizzes are too trivial. Some even repeat questions. Some questions are wrong.

--Videos are too much ridden with facts all over the place, yet offer little evaluation and explanation. For example, why do we use this tool, what purpose is this concept good for, ...

--Hands-on assignments either lack purpose and meaning or the designer never explains those.

I hope other learners who share similar experience with me will take their time and write feedbacks in Coursera itself so this specialization can be improved.
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4 out of 4 people found the following review useful
a year ago
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Anonymous completed this course.
took me about 2 hours to complete the whole course, content is so general and high level that there is basically no added value. nothing is explained in depth, no examples are given, assignments do not force real understanding of material
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0 out of 2 people found the following review useful
a year ago
Vu Anh is taking this course right now, spending 2 hours a week on it and found the course difficulty to be very easy.
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0 out of 1 people found the following review useful
a year ago
Colin Khein completed this course.
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0 out of 1 people found the following review useful
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0 out of 1 people found the following review useful
a year ago
Mark Henry Butler completed this course.
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0 out of 1 people found the following review useful
a year ago
Jason Deng dropped this course.
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10 months ago
Stephane Mysona completed this course.
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7 months ago
Y. Nicodeme completed this course.
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a month ago
Pawel Krzysztofik completed this course.
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8 months ago
Stephane Mysona completed this course.
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