Machine Learning with Apache Spark 3.0 using Scala

Machine Learning with Apache Spark 3.0 using Scala, available at $59.99, has an average rating of 4.3, with 73 lectures, based on 48 reviews, and has 13174 subscribers.

You will learn about Fundamental knowledge on Machine Learning with Apache Spark using Scala Learn and master the art of Machine Learning through hands-on projects, and then execute them up to run on Databricks cloud computing services You will Build Apache Spark Machine Learning Projects (Total 4 Projects) Explore Apache Spark and Machine Learning on the Databricks platform. Launching Spark Cluster Create a Data Pipeline Process that data using a Machine Learning model (Spark ML Library) Hands-on learning Real-time Use Case This course is ideal for individuals who are Apache Spark Beginners, Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist It is particularly useful for Apache Spark Beginners, Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist.

Enroll now: Machine Learning with Apache Spark 3.0 using Scala

Summary

Title: Machine Learning with Apache Spark 3.0 using Scala

Price: $59.99

Average Rating: 4.3

Number of Lectures: 73

Number of Published Lectures: 73

Number of Curriculum Items: 73

Number of Published Curriculum Objects: 73

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Fundamental knowledge on Machine Learning with Apache Spark using Scala

Learn and master the art of Machine Learning through hands-on projects, and then execute them up to run on Databricks cloud computing services

You will Build Apache Spark Machine Learning Projects (Total 4 Projects)

Explore Apache Spark and Machine Learning on the Databricks platform.

Launching Spark Cluster

Create a Data Pipeline

Process that data using a Machine Learning model (Spark ML Library)

Hands-on learning

Real-time Use Case

Who Should Attend

Apache Spark Beginners, Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist

Target Audiences

Apache Spark Beginners, Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Overview

Lecture 3: What is Spark ML?

Lecture 4: Introduction to Machine Learning

Lecture 5: Tips to Improve Your Course Taking Experience

Chapter 2: Apache Spark Basics (Optional)

Lecture 1: Introduction to Spark

Lecture 2: (Old) Free Account creation in Databricks

Lecture 3: (New) Free Account creation in Databricks

Lecture 4: Provisioning a Spark Cluster

Lecture 5: Basics about notebooks

Lecture 6: Why we should learn Apache Spark?

Lecture 7: Spark RDD (Create and Display Practical)

Lecture 8: Spark Dataframe (Create and Display Practical)

Lecture 9: Anonymus Functions in Scala

Lecture 10: Extra (Optional on Spark DataFrame)

Lecture 11: Extra (Optional on Spark DataFrame) in Details

Lecture 12: Spark Datasets (Create and Display Practical)

Chapter 3: Apache Spark Machine Learning

Lecture 1: Types of Machine Learning

Lecture 2: Steps Involved in Machine Learning Program

Lecture 3: Spark MLlib

Lecture 4: Importing Notebook and Data Upload

Lecture 5: Basic statistics Correlation

Lecture 6: Data Sources

Lecture 7: Data Source CSV File

Lecture 8: Data Source JSON File

Lecture 9: Data Source LIBSVM File

Lecture 10: Data Source Image File

Lecture 11: Data Source Arvo File

Lecture 12: Data Source Parquet File

Lecture 13: Machine Learning Data Pipeline Overview

Lecture 14: Machine Learning Project as an Example (Just for Basic Idea)

Lecture 15: Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 1

Lecture 16: Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 2

Lecture 17: Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 3

Lecture 18: Components of a Machine Learning Pipeline

Lecture 19: Extracting, transforming and selecting features

Lecture 20: TF-IDF (Feature Extractor)

Lecture 21: Word2Vec (Feature Extractor)

Lecture 22: CountVectorizer (Feature Extractor)

Lecture 23: FeatureHasher (Feature Extractor)

Lecture 24: Tokenizer (Feature Transformers)

Lecture 25: StopWordsRemover (Feature Transformers)

Lecture 26: n-gram (Feature Transformers)

Lecture 27: Binarizer (Feature Transformers)

Lecture 28: PCA (Feature Transformers)

Lecture 29: Polynomial Expansion (Feature Transformers)

Lecture 30: Discrete Cosine Transform (DCT) (Feature Transformers)

Lecture 31: StringIndexer (Feature Transformers)

Lecture 32: IndexToString (Feature Transformers)

Lecture 33: OneHotEncoder (Feature Transformers)

Lecture 34: SQLTransformer (Feature Transformers)

Lecture 35: VectorAssembler (Feature Transformers)

Lecture 36: RFormula (Feature Selector)

Lecture 37: ChiSqSelector (Feature Selector)

Lecture 38: Classification Model

Lecture 39: Decision tree classifier Project

Lecture 40: Logistic regression Model (Classification Model It has regression in the name)

Lecture 41: Naive Bayes Project (Iris flower class prediction)

Lecture 42: Random Forest Classifier Project

Lecture 43: Gradient-boosted tree classifier Project

Lecture 44: Linear Support Vector Machine Project

Lecture 45: One-vs-Rest classifier (a.k.a. One-vs-All) Project

Lecture 46: Regression Model

Lecture 47: Linear Regression Model Project

Lecture 48: Decision tree regression Model Project

Lecture 49: Random forest regression Model Project

Lecture 50: Gradient-boosted tree regression Model Project

Lecture 51: Clustering KMeans Project (Mall Customer Segmentation)

Lecture 52: Explanation of few terms used in Model

Lecture 53: Linear Regression Model Project – Predict Ads Click

Chapter 4: Download Resources

Lecture 1: Download Resources

Lecture 2: Important Lecture

Lecture 3: Bonus Lecture

Instructors

Bigdata Engineer
Bigdata Engineer

Rating Distribution

1 stars: 3 votes

2 stars: 3 votes

3 stars: 7 votes

4 stars: 13 votes

5 stars: 22 votes

Frequently Asked Questions

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