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Machine Learning for Data Analysis

Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions.The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. Throughout the Specialization, you will analyze a research question of your choice and summarize your insights. In the Cap

Created by: Jen Rose

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Overall Score : 78 / 100

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Course Description

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering. By completing this course, you will learn how to apply, test, and interpret machine learning algorithms as alternative methods for addressing your research questions.

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CoursesData Analysis ToolsData Analysis and Interpretation CapstoneRegression Modeling in PracticeMachine Learning for Data Analysis

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Reviews

3.9

47 reviews in total

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By Shreyans J on 27-Jun-19

It is definitely a good one and easy to understand... What I mostly struggled was with the data sets which were hard to find... probably if some data sets would have been provided would have really helped - would have been easier to run the program through with multiple sets and see the best results across. Essentially the major learning happens when you actually run it on your own (for which you may have to go back and forth with the instructors examples / teachings.

By Mukkesh G on 30-May-19

A good introduction to Machine Learning. Makes me curious to know about the methods that are available outside of this course. Great material as usual. Update After actually studying Machine Learning for months: A pretty intro to the world of ML. After learning the math behind it and other algorithms, I can say that this specialization is pretty much just the Statistical interpretations of your analysis (explained with the implementation of some powerful yet basic algorithms without really getting into the Hard Core math behind it)

By Santhosh K J on 25-Feb-19

GREAT KNOWLEDGE

By Aurimas D on 1-Feb-19

Absolutely unbalanced course. Course has 4 different topics, but it does not explain well non of them. In reality whole course should be dedicated for at least one of provided topics.

By Ponciano R on 23-Jan-19

It´s a good course but it does not goes deep enough in the examples and techniques.

By Drew M on 13-Oct-18

Learned some really useful ML models.

A must to do introductory course. I will never regrett taking that valuable course but I have to say that some improvements would make it much better. The theoretical background is too short and the proffesors seem to spend more time to describe simple functions like saying put there an ('underscore', 'parenthesis') than seting the reasons of doing that and what are the targets of the programmes. Any way all of these problems and maybe some more are not a reason for someone who wants to start machine learning to not participate in that course especially if he is a pythonist.

By Ruben D S P on 29-Jun-18

Great classes. It is the beginning to machine learning, and you can try more classes about it. You can find many job about it.

Unfirtunately superficial and outdated view on the subject.

Great course!

The course was indeed pretty interesting, I've learned a lot of new things (and got to learn how to do a little bit of coding using Python). The only thing I would recommend is to add some more datasets, because even though it's pretty easy to find some datasets on the Internet, I think 3 out of 5 suggested datasets were extremely difficult to figure out and were much more complex than the other two.

By Adrielle S on 6-Feb-18

Excelente curso. Explicações didáticas com exemplos reais implementados e detalhados em python. Descrição muito boa das aplicações das técnicas apresentadas bem como de suas limitações. Parabéns para as professoras por esse excelente curso e muito obrigada por nos disponibilizar este trabalho maravilhoso no Coursera.

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