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519 Bewertungen

Über den Kurs

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....

Top-Bewertungen

AD

Mar 01, 2017

Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.

DH

Jun 18, 2018

Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.

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51 - 75 von 510 Bewertungen für Practical Machine Learning

von Dale H

Jun 18, 2018

Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.

von Araks S

Aug 31, 2017

Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.

von German R M S

Nov 14, 2018

Este es un muy buen curso, aprendes lo básico para poder entrar en el mundo del machine learning y te da la oportunidad de desarrollar modelos realmente útiles.

Recomendado, definitivamente.

von Jared P

Jun 25, 2017

Awesome course. Would recommend it, but only to those who have a bit of stats and R background. This definitely helped me get a solid enough understanding of using R for machine learning.

von Simeon E

Aug 02, 2017

Great Course. No so easy, as I expected, but, definitely, it worth all the time I've spent on it. Be careful: it requires a lot of self-studying and don't forget to read the Course Forum.

von Harris P

Jan 16, 2017

It was like opening up a door to a whole new world. I have discovered new tools that I will thoroughly enjoy to use for the exploration of data and for predictions. Thanks Team Coursera !

von Nikhil K

Feb 19, 2016

Some of the terms used here vary from the terms used in the industry. For example recall, precision etc. Overall this is a very good course with provides basics of machine learning.

von Caner A I

Apr 12, 2017

Jeff Leek is a great professor .The delivery of the course material is very clear and covers a lot of predictive methods by using mainly R's caret package. Recommended for sure.

von João F

Feb 14, 2019

Very good course. Clear explanations and examples give a good overview of the foundations of Machine Learning. After this course the student can build Machine Learning models.

von Lopamudra S

Feb 04, 2018

The practical machine learning course is a booster for the data science aspirant.The concept taught by the Prof Jeff Leek is easily understandable. Thank you so much Sir.

von Keidzh S

Jul 15, 2018

Practical Machine learning helped me to achieve my personal goals. Algorithm of prediction became clear, that gives the understanding of main point of the data science.

von Greg A

Feb 22, 2018

A great course that really helps demystify what machine learning is and how anyone can use it to build prediction models and start to answer tough questions using data.

von Florian

Jul 09, 2016

Great primer for machine learning with ample additional resources for those who are interested. I feel this course gave me a solid basis to delve deeper into the topic.

von Supharerk T

Mar 07, 2016

I want to learn ML in R so I go straight to this course without taking any other course in this specialization, and it doesn't disappoint me. Thanks for a great course!

von Saul L

Feb 08, 2016

This is by far the most enlightening class in the whole specialization. I really got a good handle about how to build a predictive model and apply it to real datasets.

von Camilla J

May 12, 2018

This course was really informative and extremely efficient by letting you know just the few basics needed to build some quite advanced models such as random forest..

von Nicholas A

Mar 30, 2018

This was my favorite class of the specialization. It was taught very well, and I felt like everything I learned in the previous classes were finally coming together.

von Pablo L

Sep 20, 2018

Excelent course, it's a little bit short considering the breadth of the topic, but covers the most important algorithms and never abandon it's focus on methodology.

von Rachit K

Sep 16, 2017

The course gets you deep into ML very quickly ...but I think that's enough to get someone introduced to machine learning. The recommended book a great accompaniment

von Emanuele M

Nov 15, 2016

It very well done, good pace, and gives you real and concrete elements and examples to build a fully functional machine learning algorithm! i recommend this course

von Piotr K

Oct 23, 2016

Nice introduction to machine learning in R. It is rather basic level, so it not for people that already know some basics related to regression and classification.

von Jan K

Aug 02, 2017

A nice overview of the most popular Machine Learning algorithms. Also very thorough, given the limited amount of time. I recommend anyone interested to take it!

von Francisco J D d S F G

Nov 27, 2016

The best course of the specialization along with the statistical inference one - the final assignment is very fun to do, pretty much like a Kaggle competition.

von David R

Jan 14, 2019

Great introduction to Machine Learning in R. Concepts explained very clearly and project gave opportunity to test out the concepts introduced to real data.

von Vinicio D S

May 22, 2018

You will learn how to use the caret package and learn how to implement ML algorithms. If you want the theory behind it, you need to go to other courses