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Machine Learning With Spark

Price
2,340 USD
4 Days
DCSK-110
Classroom Training, Online Training
Other

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Course Description

Overview

This Machine Learning with Spark course is designed to teach Machine Learning at Scale with the popular Apache Spark framework. This course is taught using Spark & Python.

For each machine learning concept, we first discuss the foundations, its applicability, and limitations. Then we explain the implementation and use, and specific use cases. This is achieved through a combination of about 50% lecture, 50% lab work.

Please note that this course does not cover the in-depth coverage of Math / Stats is behind Machine Learning.
 

Objectives

Upon completion of the Machine Learning with Spark course, students will be able to:
  • Learn popular machine learning algorithms, their applicability, and limitations
  • Practice the application of these methods in the Spark machine learning environment
  • Learn practical use cases and limitations of algorithms

Audience

  • Data Scientists and Software Engineers

Prerequisites

    • Working knowledge of Apache Spark.
    • If students are new to Apache Spark, we can offer one day of ‘Introduction to Spark’ training
    • Programming background
    • Familiarity with Python would be a plus, but not required
    • No machine learning knowledge is assumed

Topics

Section 1: Machine Learning (ML) Overview
  • Machine Learning landscape
  • Machine Learning applications
  • Understanding ML algorithms & models (supervised and unsupervised)
Section 2: ML in Python and Spark
  • Spark ML Overview
  • Introduction to Jupyter notebooks
    • Lab: Working with Jupyter + Python + Spark
    • Lab: Spark ML utilities
Section 3: Machine Learning Concepts
  • Statistics Primer
  • Covariance, Correlation, Covariance Matrix
  • Errors, Residuals
  • Overfitting / Underfitting
  • Cross-validation, bootstrapping
  • Confusion Matrix
  • ROC curve, Area Under Curve (AUC)
    • Lab: Basic stats
Section 4: Feature Engineering (FE)
  • Preparing data for ML
  • Extracting features, enhancing data
  • Data cleanup
  • Visualizing Data
    • Lab: data cleanup
    • Lab: visualizing data
Section 5: Linear regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Running LR
  • Evaluating LR model performance
    • Lab
  • Use case: House price estimates
Section 6: Logistic Regression
  • Understanding Logistic Regression
  • Calculating Logistic Regression
  • Evaluating model performance
    • Lab
  • Use case: credit card application, college admissions
Section 7: Classification: SVM (Supervised Vector Machines)
  • SVM concepts and theory
  • SVM with kernel
    • Lab
  • Use case: Customer churn data
Section 8: Classification
  • Theory behind trees
  • Classification and Regression Trees (CART)
  • Random Forest concepts
    • Labs
  • Use case: predicting loan defaults, estimating election contributions
Section 9: Classification: Naive Bayes
  • Theory
    • Lab
  • Use case: spam filtering
Section 10: Clustering (K-Means)
  • Theory behind K-Means
  • Running K-Means algorithm
  • Estimating the performance
    • Lab
  • Use case: grouping cars data, grouping shopping data
Section 11: Principal Component Analysis (PCA)
  • Understanding PCA concepts
  • PCA applications
  • Running a PCA algorithm
  • Evaluating results
    • Lab
Use case: analyzing retail shopping data Section 12: Recommendations (Collaborative filtering)
  • Recommender systems overview
  • Collaborative Filtering concepts
    • Lab
  • Use case: movie recommendations, music recommendations
Section 13: Performance
  • Best practices for scaling and optimizing Apache Spark
  • Memory and processing optimization in Spark and how to take advantage of them
  • Effective transformations
  • Beyond JVM
  • Testing and validation
  • Machine Learning Performance
Section 14: Final workshop (time permitting)
 
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