One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

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- Week 1 -
**Week 1: Prediction, Errors, and Cross Validation**

This week will cover prediction, relative importance of steps, errors, and cross validation. - Week 2 -
**Week 2: The Caret Package**

This week will introduce the caret package, tools for creating features and preprocessing. - Week 3 -
**Week 3: Predicting with trees, Random Forests, & Model Based Predictions**

This week we introduce a number of machine learning algorithms you can use to complete your course project. - Week 4 -
**Week 4: Regularized Regression and Combining Predictors**

This week, we will cover regularized regression and combining predictors.

Coursera est une entreprise numérique proposant des formation en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.

Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.

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5 / 5

Terminé

Great course, but it may take you more than the allotted 4 weeks if you intend to dig a bit deeper and pursue some of the additional resources referenced throughout the course. I would definitely recommend doing that, as there is A LOT of material to cover if you, like me, just have to know the details of what's happening behind the scenes.

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4 / 5

Terminé

Lots of good material, but some things (like PCA) didn't receive enough coverage in the lectures. The quizzes also weren't great at testing the material in the lectures.

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3 / 5

Terminé

The course gives a decent overview of the model building process and covers a good spread of machine learning methodologies. I found that the videos focused too much on some basic/immaterial concepts at times and tended to gloss over the more in-depth or complicated sections. It would have helped if difficult concepts were explained with more examples. This meant that a lot of self study outside the lecture notes had to be done. The way that the final assignment had to be submitted on Github resulted in me spending 8 times longer on learning how to post my results than actually building the model - some more guidance here would have helped a lot as the process was very frustrating.

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4 / 5

Terminé

I realise that the course is practical machine learning, however I find myself wondering more about the 'whys' than the 'hows' after the course! Still, much benefit and many useful concepts covered which can be revisited in greater detail down the track.I would also like to see the final assignment change subtly every so often as there are existing completions on the web and it's too easy/tempting for some to simply copy and paste.

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4 / 5

Terminé

This course is brief but it has the 2 best ingredients for having a really decent first step in Machine Learning:1) It covers a broad group of different algorithms2) It provides reference material for those in which you want to get deeper. Really good job in this course.

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5 / 5

Terminé

In general, great course. But because of the strong interest in ML, I am going to attempt a detailed review.PROS: This course truly de-mystifies "Machine Learning". After completing the course, you will be able to programmatically use 100s of ML algorithms that have been created by others over the years. You will be able to use the Caret package in R to simplify your application, simplify pre-processing, perform automatic cross-validation/model tuning and generate various statistics about the model used by your ML algorithm. You will be able to easily estimate out-of-sample accuracy to determine if your model has any hope of working well, picking one classifier over the other, or using several classifiers to estimate outcome. You will learn how some of the heavily used algorithms in the industry work behind the scenes, and where to go to learn more about these. Several learning databases are introduced. If you tinker with them, you will be amazed at how easy R and Caret make it to apply ML algorithms. You will understand how chatbots, recommender systems, spam filters, "prediction" systems and the like work.WHAT THIS COURSE DOES NOT COVER:It does not cover how to write your own ML algorithms. That requires working knowledge of optimization algorithms, advanced math and probably lots of other resources. WHO SHOULD TAKE THIS COURSE?Only those prepared to work hard, dig in, and persevere through a lot of (sometimes difficult) material will benefit from this course. If you're not confident about your statistics concepts, not comfortable with R and databases, not comfortable with googling for parameters and techniques not directly discussed in class slides, then you will have trouble. Passing the quizes require you to refer to material from prior weeks, read online documents and look for similar solutions at stackexchange etc. TIP FOR MENTORS:For every week of the course, create a pinned post which says "Tips/Errata for Quiz #n". You've collected sufficient feedback from students now and know what the common issues are. Don't make them search through 100s of discussions to figure out solutions to well-known/common problems.

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4 / 5

Terminé

The lectures are very good to get the basic knowledge about machine learning. One suggestion is that the lectures can be longer, covering more detailed stuff and a little bit more advanced materials. Moreover, some codes are not explained clean and clear for me. Hope it would be better in the future.

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5 / 5

Terminé

This is my favourite course in the data science. Prior to taking up this course, I have been using technical analysis to achieve my investment goal. I know how to design trading system to trade. Now with machine learning, I learned something new. System trading is reactive and machine learning is predictive. This subject is the reason why I sign up for data science.

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1 / 5

Terminé

The worst course of the specialisation so far. The quizzes are full of typos, not clear at all, and the videos teach nothing, always refering to elements of statistical learning book. Now that I have completed the course, I do know a bunch of algorithm names involved in machine learning, but I certainly do not understand what they do and when using them.

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4 / 5

Terminé

Some problems with current and old versions of packages and problems with using other packages on different operating systems. Though that did also help foster an independent research style which will help me in the future.

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