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.
Offert par
Université Johns-HopkinsÀ propos de ce cours
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Certificat partageable
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Approx. 9 heures pour terminer
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Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Coréen, Allemand, Russe, Anglais, Espagnol
Ce que vous allez apprendre
Use the basic components of building and applying prediction functions
Understand concepts such as training and tests sets, overfitting, and error rates
Describe machine learning methods such as regression or classification trees
Explain the complete process of building prediction functions
Compétences que vous acquerrez
Random ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Approx. 9 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Coréen, Allemand, Russe, Anglais, Espagnol
Offert par
Université Johns-Hopkins
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Programme du cours : ce que vous apprendrez dans ce cours
2 heures pour terminer
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
2 heures pour terminer
9 vidéos (Total 73 min), 4 lectures, 1 quiz
2 heures pour terminer
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
2 heures pour terminer
9 vidéos (Total 96 min)
1 heure pour terminer
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.
1 heure pour terminer
5 vidéos (Total 48 min)
3 heures pour terminer
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
3 heures pour terminer
4 vidéos (Total 33 min), 2 lectures, 3 quiz
Avis
Meilleurs avis pour APPRENTISSAGE MECHANIQUE PRATIQUE
par MR13 août 2020
recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course
par AD28 févr. 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.
par AS30 août 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.
par DH17 juin 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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