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

( 47 Reviews )

Created by: Jen Rose

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Overall Score : 78 / 100

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.

Time: 12h

Instructor Details

Jen Rose

3.9 Rating
47 Reviews

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

Reviews

3.9

47 total reviews

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By Liuyijie on 15-Jun-16

Actually i want rate 0, as the instruction for the installation of new tools are quite vague and misleading

By THEODOSIOS M A on 3-Sep-16

Not good at all.We see different processes without anyone making clear the reason why we should apply this processes ,under which conditions and what is the question that we have to answer when we apply these processes.The only good is that we get into some new terms and see new things.I could say that for me,it wouldn't make such a difference if it wasn't in this specialization.

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 ÃÂžÃ‘ÂÃ‘Â‚Ã‘Â€ÃÂ¾Ã‘ÂƒÃ‘Â…ÃÂ¾ÃÂ² ÃÂ on 6-Mar-18

Unfirtunately superficial and outdated view on the subject.

By Teo S on 1-Nov-16

Personally felt this course have a lot more potential. The explanations in the lectures felt very robotic especially when describing the scripts. At times the lectures slides felt like displaying the subtitles and reading off them. A lot more diagrams could have been illustrated for explanations. I have to watch other videos in youtube to get a better grasp of the concepts.Good thing is that this is an introductory course, and the codes are given.

By Vanessa Q M on 5-Sep-17

It goes over and over about the adolescent examples, which makes it annoying. The quality and production of the video is bad. Why to use moving scenes in the background (like the horses or the highway)? That's distractive and takes the focus of the content, better to use a blackboard.

By Dinesh B on 5-Nov-17

The material is good but the functions should have been explained in more detail. There is kind of repetition of same thing. It should have given some more examples and changes in code to explain the different types of ways to apply same algorithm.

By Xiaoyang G on 16-Apr-16

It's not an intro class. But you can practice a lot if you know something.

By Karthick K on 12-Dec-16

Course could be better

By Monika K on 29-Apr-16

This level of detail was good for easier statistical concepts but there are much better courses on Coursera for Machine Learning

By Lee X A on 22-Mar-16

Disadvantages : Lacks Rigour, Lacks Support from instructors , Expensive , Peer review ( this is somewhat bad as most barely give any comments, though towards the end, reviews tend to be pretty good). *** DISCLAIMER *** I am not statistically significant as i only receive 3 reviews per week. Advantages :Quick to earn cert, prewritten code available for easy use. Assignments on your own data. This is probably useful for people wanting to learn techniques for data analysis, who need not go too deep into the technique. I would recommend this to people learning techniques for data analysis in various non-mathematical and non-statistical fields, though the content lacks rigour, and you need outside sources to help understand techniques. This course IS NOT WORTH PAYING USD79, there are definitely other courses much more worth the money. You can audit it for free, if you do not want a cert.

By Tristan B on 1-Mar-16