Pluralsight Building Machine Learning Models in Spark 2 Torrent Download Locations

Building Machine Learning Models in Spark 2
    01.Course Overview
          0101.Course Overview.mp4 -

    02.Machine Learning Packages spark.mllib vs. spark.ml
          0201.Module Overview.mp4 -

          0202.Prerequisites and Course Overview.mp4 -

          0203.RDDs The Building Blocks of Spark.mp4 -

          0204.DataFrames in Spark 2.mp4 -

          0205.Demo Spark 2 Installation and Working with Jupyter Notebooks.mp4 -

          0206.spark.mllib vs. spark.ml.mp4 -

          0207.Introducing Decision Trees.mp4 -

          0208.Gini Impurity and Pros and Cons of Decision Trees.mp4 -

          0209.Demo Basic Project Setup.mp4 -

          0210.Demo Wine Classification Using Decision Trees in spark.mllib.mp4 -

          0211.Demo Working with the LIBSVM Data Format.mp4 -

          0212.Demo Decision Trees Using the LIBSVM Data Format.mp4 -

    03.Building Classification and Regression Models in Spark ML
          0301.Module Overview.mp4 -

          0302.ML Pipelines, Estimators, and Transformers.mp4 -

          0303.Training and Prediction Pipeline Stages.mp4 -

          0304.Feature Engineering.mp4 -

          0305.Feature Extractors.mp4 -

          0306.Feature Transformers.mp4 -

          0307.Feature Selectors and Locality Sensitive Hashing.mp4 -

          0308.The Confusion Matrix Accuracy, Precision, Recall, F1 Score.mp4 -

          0309.Demo Wine Classification Using Decision Trees in Spark ML.mp4 -

          0310.Demo Converting Categorical Data to Numeric Values.mp4 -

          0311.Demo The Decision Tree Classifier.mp4 -

          0312.Random Forests.mp4 -

          0313.Demo Income Classification Using Random Forests.mp4 -

          0314.Demo Using ML Pipelines.mp4 -

          0315.Demo Predictions Using the Random Forest .mp4 -

          0316.Introducing Regularized Regression Models to Prevent Overfitting.mp4 -

          0317.Lasso and Ridge Regression.mp4 -

          0318.Demo Linear Regression with the Elastic Net Param.mp4 -

          0319.Demo Predictions Using the Regression Model.mp4 -

          0320.Demo Hyperparameter Tuning.mp4 -

    04.Implementing Clustering and Dimensionality Reduction in Spark ML
          0401.Module Overview.mp4 -

          0402.Supervised and Unsupervised Learning Techniques.mp4 -

          0403.Clustering Objectives.mp4 -

          0404.Visualizing K-means Clustering.mp4 -

          0405.Number of Clusters as a Hyperparameter The Elbow and Silhouette Method.mp4 -

          0406.Demo K-means Clustering on the Titanic Dataset.mp4 -

          0407.Demo Exploring Clusters.mp4 -

          0408.Principal Component Analysis Intuition.mp4 -

          0409.Demo Regression Model Without PCA.mp4 -

          0410.Demo Performing Regression on Principal Components.mp4 -

    05.Building Recommendation Systems in Spark ML
          0501.Module Overview.mp4 -

          0502.Content-based and Collaborative Filtering.mp4 -

          0503.Estimating the Ratings Matrix.mp4 -

          0504.The Alternating Least Squares Method.mp4 -

          0505.Explicit and Implicit Ratings.mp4 -

          0506.Cold Start Strategies and Compute Intensity.mp4 -

          0507.Demo Building a Recommendation System Using Explicit Ratings.mp4 -

          0508.Demo Getting Movie Recommendations for Specific Users.mp4 -

          0509.Demo Building a Recommendation System Using Implicit Ratings.mp4 -

          0510.Demo Getting Artist Recommendations for Specific Users.mp4 -

          0511.Summary and Further Study.mp4 -

    Exercise Files
          spark-2-building-machine-learning-models.zip -