Chevron Left
返回到 Practical Machine Learning

Practical Machine Learning, Johns Hopkins University

4.5
2,215 个评分
434 个审阅

课程信息

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....

热门审阅

创建者 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.

创建者 AS

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.

筛选依据:

427 个审阅

创建者 Carlo G Inovero

Dec 04, 2018

thank you

创建者 Javier Eslava Schmalbach

Dec 02, 2018

Excellent.

创建者 Sulan LIU

Nov 19, 2018

I hope we can have more détails in this cours and to see how to use the algorithms for the big data. Thank you.

创建者 Raunak Shakya

Nov 19, 2018

a very good course for those wanting to learn Machine Learning to implement in Data Science.

创建者 German Rafael Mejia Salgado

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.

创建者 Naman Khurpia

Nov 13, 2018

please remove the checking by students

创建者 Alfonso R Reyes

Nov 13, 2018

Hands on course. Loved it. It goes a little bit fast, however, the content is ambitious.

创建者 adam reiner

Nov 11, 2018

Best course in the data science series. It is practical, so if you are looking for something theoretical this will not be the course for you. Also good introduction the methods for model testing and validation.

创建者 Terry L Jones

Nov 09, 2018

Lot of good material, however, on all of these courses, it would be very helpful if they were better organized for learning.

Overview of learning objectives in a step sequence for a more organized approach for learning (maybe even a process roadmap map sequencing activity to follow that you can reference back to.

Detailed information for each step in the learning process that can be followed that maps back to the roadmap.

A summary of the learning objective in the roadmap sequence.

Basically, just like writing a paper, > overview/objectives > Main topics >subtopics, etc. > summary

创建者 Thomas Bell

Nov 08, 2018

Lectures and course material is insufficient to get the right amount of knowledge to be able to do the tests and the course project