_evaluation-metrics-for-machine-learning-models-124-full-course-preview-1.jpg "Evaluation Metrics for Machine Learning Models | Full Course from ml model evaluation Video Screenshot Preview 1")
Gallery_evaluation-metrics-for-machine-learning-models-124-full-course-preview-3.jpg "Evaluation Metrics for Machine Learning Models | Full Course from ml model evaluation Video Screenshot Preview 3")
Gallery_evaluation-metrics-for-machine-learning-models-124-full-course-preview-hqdefault.jpg "Evaluation Metrics for Machine Learning Models | Full Course from ml model evaluation Video Screenshot Preview hqdefault")
GalleryPlay Video:
(Note: The default playback of the video is HD VERSION. If your browser is buffering the video slowly, please play the REGULAR MP4 VERSION or Open The Video below for better experience. Thank you!)
Jump To Video Parts
⏲ Duration: 50 minutes 7 seconds
👁 View: 2.3K times
Play Audio:👁 View: 2.3K times
Description:
Analytics Vidhya
Analytics Vidhya
Share with your friends:
Whatsapp | Viber | Telegram | Line | SMS
Email | Twitter | Reddit | Tumblr | Pinterest
Related Videos
_all-machine-learning-models-explained-in-5-minutes-124-types-of-ml-models-basics.webp "Learn with Whiteboard")
⏲ 5 minutes 1 second 👁 587.7K
_105-evaluating-a-classification-model-6-classification-report-124-creating-machine-learning-models.webp "Machine Learning")
⏲ 10 minutes 17 seconds 👁 19.5K
_model-parallelism-in-spark-ml-cross-validation-nick-pentreath-ibm-and-bryan-cutler-ibm.webp "Tuning a Spark ML model with cross-validation can be an extremely computationally expensive process. As the number of hyperparameter combinations increases, so does the number of models being evaluated. The default configuration in Spark is to evaluate each of these models one-by-one to select the best performing. When running this process with a large number of models, if the training and evaluation of a model does not fully utilize the available cluster resources then that waste will be compou")
⏲ 19 min 11 sec ✓ 11-Jun-2018
_maria-khalusova-machine-learning-model-evaluation-metrics-124-pydata-la-2019.webp "PyData")
⏲ 39 minutes 47 seconds 👁 4.4K
_pharmacy-claims-fraud-detection-using-apache-spark.webp "Tuning a Spark ML model with cross-validation can be an extremely computationally expensive process. As the number of hyperparameter combinations increases, so does the number of models being evaluated. The default configuration in Spark is to evaluate each of these models one-by-one to select the best performing. When running this process with a large number of models, if the training and evaluation of a model does not fully utilize the available cluster resources then that waste will be compou")
_154-understanding-the-training-and-validation-loss-curves.webp "DigitalSreeni")
_house-price-prediction-in-python-full-machine-learning-project.webp "NeuralNine")
_machine-learning-for-the-apache-spark-developer-paige-lu-microsoft.webp "Developers learning Apache Spark are often times quickly impressed by Apache Spark’s powerful capability to do large scale ETL and Streaming in a simple and elegant way. However learning to use Spark effectively in Machine Learning is still challenging, not only because ML introduces many new concepts outside of computer science, but also because:nn• Many ML tutorials and samples are written in R or Pythonnn• Many Spark ML examples and online classes use Spark versions prior to 2.0 and the")
_all-machine-learning-models-explained-in-5-minutes-124-types-of-ml-models-basics.jpg)
_105-evaluating-a-classification-model-6-classification-report-124-creating-machine-learning-models.jpg)
_model-parallelism-in-spark-ml-cross-validation-nick-pentreath-ibm-and-bryan-cutler-ibm.jpg)
_maria-khalusova-machine-learning-model-evaluation-metrics-124-pydata-la-2019.jpg)
_pharmacy-claims-fraud-detection-using-apache-spark.jpg)
_154-understanding-the-training-and-validation-loss-curves.jpg)
_house-price-prediction-in-python-full-machine-learning-project.jpg)
_machine-learning-for-the-apache-spark-developer-paige-lu-microsoft.jpg)
![[+]](/images/menu.gif){kind=small}