Jen Rose
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. At Wesleyan, distinguished scholar–teachers work closely with students, taking advantage of fluidity among disciplines to explore the world with a variety of tools. The university seeks to build a diverse, energetic community of students, faculty, and staff who think critically and creatively and who value independence of mind and generosity of spirit.
Instructor Details
Courses : 4
Specification: Machine Learning for Data Analysis
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43 reviews for Machine Learning for Data Analysis
Add a review
| Price | Free |
|---|---|
| Provider | |
| Duration | 12 hours |
| Year | 2016 |
| Language | English |
| Certificate | Yes |
| Quizzes | Yes |
FREE
MANOJ K –
I really liked this course. Concepts well explained. I was hoping for more practical exercises on different types of data sets along with how to improve model accuracy in various algorithm taught. concept such as pruning etc. were missing. But I am sure in future, we will have more on it. Thanks Professor.
Yaman S –
Excellent course
Tristan B –
Not deep enough on diagnostic and interpretation
Susanne W B –
It was okay for an introduction to the methods, but I would have liked to learn about them in more details, i.e. the course was too short.
Richard M –
Not impressed with the teaching style. Seems that lectures were being read and not taught.
Ivan C –
I would like to have an opportunity to contact my reviews.
Lee X A –
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.
Mengyue S –
More examples in coding and results are expected. So it is more convenient for students to compare different results and understand deeper
Artem A –
Noiiice!
Xiaoyang G –
It’s not an intro class. But you can practice a lot if you know something.
Monika K –
This level of detail was good for easier statistical concepts but there are much better courses on Coursera for Machine Learning
Liuyijie –
Actually i want rate 0, as the instruction for the installation of new tools are quite vague and misleading
Edward M –
Good introduction with python example for famous algorithm such as random forest and k mean
Felix A R –
Option of learning both SAS and Python is great!
Mathilde v E –
V
THEODOSIOS M A –
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.
Deleted A –
short vedios and good ma
Bruno G C –
Very good course. I recommend to anyone who’s interested in data analysis and machine learning.
Thomas C K –
Great Class!
Leonardo A –
Excellent course, some basic tecniques of Machine Learning are implemented in Python and SAS.
Teo S –
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.
Karthick K –
Course could be better
Michael B –
Excellent introductory course on machine learning focusing on simple linear and multiple regression, lasso regression and k means clustering. A background in Python programming is useful but not required as the instructors discuss the techniques with annotated code examples.
Jinbo C –
easy to capture the concept
Oriana A –
Very good. I enjoyed doing it and learned a lot. I would have liked that it had included r as one of the softwares.
Genara P –
Excellet! I highly recommend!
Christine R –
I definitely appreciate this information on Machine Learning. And from an outsider perspective would say it is quite clear when I put it into practice will see how it goes. I do like the video format and will say that through out the course the instructor
Steven L –
Good!
Vanessa Q M –
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.
Macarena E –
I enjoyed this course a lot. It’s easy and I’ve learnt what I need to apply the machine learning techniques. Easy and simple. You don’t need to be a mathematician.
Dinesh B –
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.
karthik –
Well structured .
ADITYA Y P –
More Implementation oriented and less math also contains distracting background videos when explaining important concepts
Dmitry B –
There is some problems because of changes both in SAS and Python after creating the course
Adrielle S –
Excelente curso. Explicacoes didaticas com exemplos reais implementados e detalhados em python. Descricao muito boa das aplicacoes das tecnicas apresentadas bem como de suas limitacoes. Parabens para as professoras por esse excelente curso e muito obrigada por nos disponibilizar este trabalho maravilhoso no Coursera.
Ruben D S P –
Great classes. It is the beginning to machine learning, and you can try more classes about it. You can find many job about it.
Drew M –
Learned some really useful ML models.
Ponciano R –
It’s a good course but it does not goes deep enough in the examples and techniques.
Aurimas D –
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.
Santhosh K J –
GREAT KNOWLEDGE
Mukkesh G –
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)
Shreyans J –
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.
Thoai N T –
This is good course