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[FreeCoursesOnline.Me] [Packt] Hands-On Machine Learning with Scala and Spark [FCO]

[FreeCoursesOnline.Me] [Packt] Hands-On Machine Learning with Scala and Spark [FCO]

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Description




By: Tomasz Lelek

Released: Wednesday, January 30, 2019 New Release!

Torrent Contains: 32 Files, 6 Folders

Course Source: https://www.packtpub.com/big-data-and-business-intelligence/hands-machine-learning-scala-and-spark-video



Implement machine learning algorithms and evaluate how well they perform with the Scala programming language



Video Details



ISBN 9781789342468

Course Length 1 hours 41 minutes



Table of Contents



• ADVANCED TEXT PROCESSING IN SPARK AND BUILDING A CLASSIFICATION MODEL

• BUILDING A REGRESSION MODEL WITH SPARK

• BUILDING A CLUSTERING MODEL WITH SPARK

• DIMENSIONALITY REDUCTIONS AND RECOMMENDATION ENGINES

• DEEP LEARNING WITH SPARK



Video Description



Programmers 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-...



Style and Approach



This is a step-by-step and fast-paced guide that will help you learn how to create a ML model using the Apache Spark ML toolkit. With this practical approach, you will take your skills to the next level and will be able to create ML pipelines effectively.



What You Will Learn



• Extract features from data

• Write Scala code implementing ML algorithms for prediction and clustering

• Analyze the structure of datasets with exploratory data analysis techniques using Scala.

• Get to grips with the most popular machine learning algorithms used in the areas of regression, classification, clustering, dimensionality reduction, PCA, and neuralnetworks.

• Use the power of MLlib libraries to implement machine learning with Spark

• Using GMM to reason about time series data

• Work with the k-means and Naive Bayes algorithms and their methods and implement them in Scala with real datasets



Authors



Tomasz Lelek



Tomasz Lelek is a Software Engineer, programming mostly in Java and Scala. He has been working with the Spark and ML APIs for the past 5 years with production experience in processing petabytes of data.

He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before solving a problem. Recently he was a speaker at conferences in Poland, Confitura and JDD (Java Developers Day), and at Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference.

He is a co-founder of www.initlearn.com, an e-learning platform that was built with the Java language.



For More Udemy Free Courses >>> http://www.freetutorials.eu

For more Lynda and other Courses >>> https://www.freecoursesonline.me/

Our Forum for discussion >>> https://discuss.freetutorials.eu/








File list
  • [FreeCoursesOnline.Me] [Packt] Hands-On Machine Learning with Scala and Spark [FCO]
  • 01.Advanced Text Processing in Spark and Building a Classification Model/0101.The Course Overview.mp4 19.2 MB
  • 01.Advanced Text Processing in Spark and Building a Classification Model/0102.Analyzing Text Input Data.mp4 18.9 MB
  • 01.Advanced Text Processing in Spark and Building a Classification Model/0103.Feature Generation from Text – Count Vectorizer, TFIDF, LDA.mp4 14.1 MB
  • 01.Advanced Text Processing in Spark and Building a Classification Model/0104.Extracting Features from Data – Transforming Text into Vector of Numbers.mp4 22.7 MB
  • 01.Advanced Text Processing in Spark and Building a Classification Model/0105.Bag-of-Words and Skip Gram.mp4 8.4 MB
  • 01.Advanced Text Processing in Spark and Building a Classification Model/0106.Training Classification Models – Implementing Word2Vect Using Apache Spark.mp4 16.3 MB
  • 02.Building a Regression Model with Spark/0201.Logistic Regression Explanation.mp4 8.6 MB
  • 02.Building a Regression Model with Spark/0202.Writing a Logistic Regression Model Per Author in Apache Spark.mp4 8.6 MB
  • 02.Building a Regression Model with Spark/0203.Training Regression Model.mp4 15.3 MB
  • 02.Building a Regression Model with Spark/0204.Key Concepts, Machine Learning Pipelines, and Operations.mp4 10.4 MB
  • 02.Building a Regression Model with Spark/0205.Learn How to Validate Models Using Cross-Validation.mp4 18.6 MB
  • 03.Building a Clustering Model with Spark/0301.Analyzing Time of Post Using Clustering – (GMM Explanation).mp4 6.1 MB
  • 03.Building a Clustering Model with Spark/0302.Implementing GMM in Apache Spark.mp4 31.5 MB
  • 03.Building a Clustering Model with Spark/0303.K-Means Clustering Explanation and Use Cases.mp4 4.1 MB
  • 03.Building a Clustering Model with Spark/0304.Implementing K-Means Clustering in Apache Spark.mp4 18.2 MB
  • 03.Building a Clustering Model with Spark/0305.Measure Accuracy Using Area Under ROC.mp4 12.6 MB
  • 04.Dimensionality Reductions and Recommendation Engines/0401.Dimensionality Reduction Using Singular Value Decomposition (SVD).mp4 14.1 MB
  • 04.Dimensionality Reductions and Recommendation Engines/0402.Building Recommendation Engine in Spark Using Collaborative Filtering.mp4 18.2 MB
  • 04.Dimensionality Reductions and Recommendation Engines/0403.Using Recommendation Engine to Get Top Recommendations.mp4 26.3 MB
  • 04.Dimensionality Reductions and Recommendation Engines/0404.Dense and Sparse Vectors.mp4 13.7 MB
  • 04.Dimensionality Reductions and Recommendation Engines/0405.LabeledPoints, Rating, and Other Data Types.mp4 10.5 MB
  • 05.Deep Learning with Spark/0501.The Spark versus Deep Learning Use Case.mp4 7.7 MB
  • 05.Deep Learning with Spark/0502.Spark for Parallelizing Deep Learning Evaluation.mp4 15.3 MB
  • 05.Deep Learning with Spark/0503.Deep Learning As a Feature Generator for Existing Spark ML Algorithms.mp4 11.5 MB
  • 05.Deep Learning with Spark/0504.SparkDeep Learning Made Simple.mp4 15.2 MB
  • Discuss.FreeTutorials.Us.html 165.7 KB
  • Exercise Files/exercise_files.zip 56.2 KB
  • FreeCoursesOnline.Me.html 108.3 KB
  • FreeTutorials.Eu.html 102.2 KB
  • How you can help Team-FTU.txt 259 B

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