Machine Learning with Apache Spark 3.0 using Scala
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Machine Learning with Apache Spark 3.0 using Scala with Examples and Project
“Big data” analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark. Employers including Amazon, eBay, NASA, Yahoo, and many more. All are using Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You’ll learn those same techniques, using your own Operating system right at home.
So, What are we going to cover in this course then?
Learn and master the art of Machine Learning through hands-on projects, and then execute them up to run on Databricks cloud computing services (Free Service) in this course. Well, the course is covering topics:
1) Overview
2) What is Spark ML
3) Types of Machine Learning
4) Steps Involved in the Machine learning program
5) Basic Statics
6) Data Sources
7) Pipelines
8) Extracting, transforming and selecting features
9) Classification and Regression
10) Clustering
Projects:
1) Will it Rain Tomorrow in Australia
2) Railway train arrival delay prediction
3) Predict the class of the Iris flower based on available attributes
4) Mall Customer Segmentation (K-means) Cluster
In order to get started with the course And to do that you’re going to have to set up your environment.
So, the first thing you’re going to need is a web browser that can be (Google Chrome or Firefox, or Safari, or Microsoft Edge (Latest version)) on Windows, Linux, and macOS desktop
This is completely Hands-on Learning with the Databricks environment.
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6Introduction to SparkVideo lesson
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7(Old) Free Account creation in DatabricksVideo lesson
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8(New) Free Account creation in DatabricksVideo lesson
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9Provisioning a Spark ClusterVideo lesson
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10Basics about notebooksVideo lesson
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11Why we should learn Apache Spark?Video lesson
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12Spark RDD (Create and Display Practical)Video lesson
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13Spark Dataframe (Create and Display Practical)Video lesson
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14Anonymus Functions in ScalaVideo lesson
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15Extra (Optional on Spark DataFrame)Video lesson
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16Extra (Optional on Spark DataFrame) in DetailsVideo lesson
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17Spark Datasets (Create and Display Practical)Video lesson
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18Types of Machine LearningVideo lesson
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19Steps Involved in Machine Learning ProgramVideo lesson
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20Spark MLlibVideo lesson
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21Importing Notebook and Data UploadVideo lesson
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22Basic statistics CorrelationVideo lesson
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23Data SourcesVideo lesson
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24Data Source CSV FileVideo lesson
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25Data Source JSON FileVideo lesson
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26Data Source LIBSVM FileVideo lesson
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27Data Source Image FileVideo lesson
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28Data Source Arvo FileVideo lesson
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29Data Source Parquet FileVideo lesson
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30Machine Learning Data Pipeline OverviewVideo lesson
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31Machine Learning Project as an Example (Just for Basic Idea)Video lesson
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32Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 1Video lesson
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33Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 2Video lesson
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34Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 3Video lesson
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35Components of a Machine Learning PipelineVideo lesson
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36Extracting, transforming and selecting featuresVideo lesson
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37TF-IDF (Feature Extractor)Video lesson
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38Word2Vec (Feature Extractor)Video lesson
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39CountVectorizer (Feature Extractor)Video lesson
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40FeatureHasher (Feature Extractor)Video lesson
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41Tokenizer (Feature Transformers)Video lesson
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42StopWordsRemover (Feature Transformers)Video lesson
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43n-gram (Feature Transformers)Video lesson
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44Binarizer (Feature Transformers)Video lesson
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45PCA (Feature Transformers)Video lesson
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46Polynomial Expansion (Feature Transformers)Video lesson
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47Discrete Cosine Transform (DCT) (Feature Transformers)Video lesson
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48StringIndexer (Feature Transformers)Video lesson
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49IndexToString (Feature Transformers)Video lesson
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50OneHotEncoder (Feature Transformers)Video lesson
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51SQLTransformer (Feature Transformers)Video lesson
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52VectorAssembler (Feature Transformers)Video lesson
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53RFormula (Feature Selector)Video lesson
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54ChiSqSelector (Feature Selector)Video lesson
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55Classification ModelVideo lesson
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56Decision tree classifier ProjectVideo lesson
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57Logistic regression Model (Classification Model It has regression in the name)Video lesson
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58Naive Bayes Project (Iris flower class prediction)Video lesson
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59Random Forest Classifier ProjectVideo lesson
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60Gradient-boosted tree classifier ProjectVideo lesson
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61Linear Support Vector Machine ProjectVideo lesson
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62One-vs-Rest classifier (a.k.a. One-vs-All) ProjectVideo lesson
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63Regression ModelVideo lesson
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64Linear Regression Model ProjectVideo lesson
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65Decision tree regression Model ProjectVideo lesson
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66Random forest regression Model ProjectVideo lesson
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67Gradient-boosted tree regression Model ProjectVideo lesson
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68Clustering KMeans Project (Mall Customer Segmentation)Video lesson
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69Explanation of few terms used in ModelVideo lesson
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70Linear Regression Model Project - Predict Ads ClickVideo lesson
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