by Tomasz Lelek | Released January 2019 | ISBN: 9781789342468

Implement machine learning algorithms and evaluate how well they perform with the Scala programming language About This VideoLearn how to extract ML features from unstructured data for input to ML models so you can build models for any input dataLeverage Spark's Powerful ML toolkit to build models by learning how to choose the best model for your problemUse Deep Learning methods with Apache Spark to stay on the cutting edge of ML techniquesIn DetailProgrammers face multiple challenges while implementing ML; dealing with unstructured data and picking the proper ML model are among the hardest.In this course we will go through day-to-day challenges that programmers face when implementing ML pipelines and consider different approaches and models to solve complex problems.You will learn about the most effective machine learning techniques and implement them in your favor. You will implement algorithms in practical hands-on projects, building data models and understanding how they work by using different types of algorithm.Each section of the course deals with a specific machine learning problem and analysis and gives you insights by using real-world datasets.By the end of this course, you will be able to take huge datasets, extract features from it, and apply a machine learning model that is well suited to your problem.The code bundle for the course is available at: https://github.com/PacktPublishing/Hands-On-Machine-Learning-with-Scala-and-SparkDownloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you. Show and hide more

1. Chapter 1 : Advanced Text Processing in Spark and Building a Classification Model
   • The Course Overview 00:02:26
   • Analyzing Text Input Data 00:04:14
   • Feature Generation from Text – Count Vectorizer, TFIDF, LDA 00:03:49
   • Extracting Features from Data – Transforming Text into Vector of Numbers 00:06:09
   • Bag-of-Words and Skip Gram 00:04:59
   • Training Classification Models – Implementing Word2Vect Using Apache Spark 00:04:15
2. Chapter 2 : Building a Regression Model with Spark
   • Logistic Regression Explanation 00:05:00
   • Writing a Logistic Regression Model Per Author in Apache Spark 00:05:31
   • Training Regression Model 00:04:04
   • Key Concepts, Machine Learning Pipelines, and Operations 00:02:47
   • Learn How to Validate Models Using Cross-Validation 00:04:47
3. Chapter 3 : Building a Clustering Model with Spark
   • Analyzing Time of Post Using Clustering – (GMM Explanation) 00:03:52
   • Implementing GMM in Apache Spark 00:05:21
   • K-Means Clustering Explanation and Use Cases 00:02:42
   • Implementing K-Means Clustering in Apache Spark 00:04:30
   • Measure Accuracy Using Area Under ROC 00:03:04
4. Chapter 4 : Dimensionality Reductions and Recommendation Engines
   • Dimensionality Reduction Using Singular Value Decomposition (SVD) 00:03:42
   • Building Recommendation Engine in Spark Using Collaborative Filtering 00:04:59
   • Using Recommendation Engine to Get Top Recommendations 00:05:01
   • Dense and Sparse Vectors 00:04:19
   • LabeledPoints, Rating, and Other Data Types 00:02:41
5. Chapter 5 : Deep Learning with Spark
   • The Spark versus Deep Learning Use Case 00:04:30
   • Spark for Parallelizing Deep Learning Evaluation 00:03:24
   • Deep Learning As a Feature Generator for Existing Spark ML Algorithms 00:03:11
   • Spark/Deep Learning Made Simple 00:02:53
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