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.
ofrecido por
Universidad Johns HopkinsAcerca de este Curso
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Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Aprox. 9 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
Qué aprenderás
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
Habilidades que obtendrás
Random ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Aprox. 9 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
Universidad 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.
Programa - Qué aprenderás en este curso
2 horas para completar
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
2 horas para completar
9 videos (Total 73 minutos), 4 lecturas, 1 cuestionario
2 horas para completar
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
2 horas para completar
9 videos (Total 96 minutos)
1 hora para completar
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 hora para completar
5 videos (Total 48 minutos)
3 horas para completar
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
3 horas para completar
4 videos (Total 33 minutos), 2 lecturas, 3 cuestionarios
Reseñas
Principales reseñas sobre PRACTICAL MACHINE LEARNING
por MR13 de ago. de 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
por AD28 de feb. de 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.
por DH17 de jun. de 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.
por AS30 de ago. de 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.
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