Udemy - Spark Machine Learning Project (House Sale Price Prediction)

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Udemy - Spark Machine Learning Project (House Sale Price Prediction) (Size: 1.7 GB)
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ~Get Your Files Here !
  1 - Introduction To The Course
  1 - Welcome To The Course.mp4 36 MB
  2 - Setting Up The Environment
  10 - Hands On Setting Java Environments.mp4 5.8 MB
  11 - Steps For Setting Java Environments.html 1 KB
  12 - Hands On Apache Zeppelin Installation Steps On Ubuntu Machine.mp4 20.8 MB
  13 - Steps For Installing Apache Zeppelin On Ubuntu Machine.html 1.4 KB
  14 - Hands On Installing Docker Desktop On Windows 1011.mp4 7.8 MB
  15 - Steps For Installing Docker On Windows.html 409.6 B
  16 - Hands On Running Apache Zeppelin On Docker Windows.mp4 41.9 MB
  17 - Steps For Running Apache Zeppelin On Docker.html 2.9 KB
  18 - Hands On Configure And Connect To Spark Interpreter.mp4 65.7 MB
  19 - Steps For Configure And Connect To Spark Interpreter.html 2.7 KB
  3 - Download Resources
  20 - Download Resources.html 204.8 B
  20 - House-Price-Prediction.zpln 159.7 KB
  20 - train.csv 449.9 KB
  21 - Importing Zeppelin File In Zeppelin Environment.mp4 12.4 MB
  4 - Zeppelin Basics
  22 - What Is Apache Zeppelin.mp4 11.9 MB
  23 - Features Benefits.mp4 40.8 MB
  24 - Notebook Ui Overview.mp4 56 MB
  25 - Markdown And Text Formatting.mp4 31.9 MB
  26 - Creating And Running Paragraphs.mp4 16.1 MB
  27 - Hands On Creating And Running Paragraphs.mp4 44.1 MB
  28 - Visualization Options Tables Bar Chart Pie Chart Etc.mp4 11.6 MB
  29 - Hands On Types Of Default Chart In Zeppelin.mp4 31 MB
  5 - Zeppelin With Apache Spark
  30 - Spark Interpreter Details.mp4 55.4 MB
  31 - Working With Rdds And Dataframes.mp4 19.5 MB
  32 - Spark Sql Queries And Caching.mp4 38.9 MB
  33 - Visualizing Spark Outputs.mp4 72.5 MB
  34 - Job Tracking And Performance Tuning Basics.mp4 56.2 MB
  6 - Machine Learning Project
  35 - Understanding Spark Imports For Ml.mp4 14.5 MB
  36 - Loading Source Data In Spark.mp4 23.1 MB
  37 - Preparing Training Data.mp4 24.9 MB
  38 - Understanding Stringindexer In Spark.mp4 15.3 MB
  39 - Defining The Pipeline In Spark Mllib.mp4 43.9 MB
  40 - Split The Data.mp4 12.8 MB
  41 - Using Vectorassembler To Prepare Training Data.mp4 59.7 MB
  42 - Train A Regression Model In Spark.mp4 47.9 MB
  43 - Prepare The Testing Data.mp4 25.6 MB
  44 - Testing The Regression Model In Spark.mp4 51.9 MB
  45 - Evaluating The Regression Model In Spark.mp4 20 MB
  46 - Evaluating Model Performance Using Rmse.mp4 40.7 MB
  7 - Introduction
  47 - Introduction.mp4 31.5 MB
  8 - Download Resources
  48 - Download Resources.html 204.8 B
  48 - train.csv 449.9 KB
  SourceCode
  9 - Project Begins
  49 - Introduction To Spark.mp4 42.6 MB
  50 - Old Free Account Creation In Databricks.mp4 7.7 MB
  51 - New Free Account Creation In Databricks.mp4 8.7 MB
  52 - Tips To Improve Your Course Taking Experience.mp4 3 MB
  53 - Provisioning A Spark Cluster.mp4 10 MB
  54 - Introduction To Machine Learning.mp4 35 MB
  55 - Basics About Notebooks.mp4 30.7 MB
  56 - Dataframes.mp4 21.9 MB
  57 - Regression Model.mp4 9.7 MB
  58 - Explanation Of Few Terms Used In Model.mp4 16.7 MB
  59 - File Content.html 22.9 KB
  60 - Project Explaination.mp4 271.5 MB
  61 - Important Lecture.mp4 6.1 MB
  62 - Bonus Lecture.html 8.3 KB
  House Price Prediction.dbc 106.5 KB
  7 - Requirements.html 204.8 B
  8 - Hands On Installing Java.mp4 128.5 MB
  9 - Steps For Installing Java.html 921.6 B
  2 - What You Will Learn.mp4 8.5 MB
  3 - Why Spark Mllib For Machine Learning Projects.mp4 8.1 MB
  4 - Course Workflow Project Overview.mp4 10.2 MB
  5 - Tools Well Use Apache Spark Spark Ml Apache Zeppelin.mp4 9.5 MB
  6 - Overview Of House Sale Dataset.mp4 11.5 MB

Description


Spark Machine Learning Project (House Sale Price Prediction)
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MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz

Language: English | Size: 1.69 GB | Duration: 4h 56m
Spark Machine Learning Project (House Sale Price Prediction) for beginner using Databricks Notebook (Unofficial)
What you'll learn

Understand the end-to-end workflow of a Spark ML project.

Set up the environment by installing Java, Apache Zeppelin, Docker, and Spark.

Work with Zeppelin notebooks for running Spark jobs and visualizations.

Understand the house sales dataset and prepare it for machine learning.

Perform data preprocessing and feature engineering using Spark MLlib.

Use StringIndexer for handling categorical features.

Apply VectorAssembler to transform multiple features into a single vector column.

Split data into training and testing sets for machine learning tasks.

Train a regression model in Spark MLlib for predicting house sale prices.

Test and evaluate the regression model with metrics like RMSE.

Visualize outputs and interpret model results for business insights.

Run Spark jobs both in Apache Zeppelin and in Databricks (cloud environment).

Gain practical experience with Spark DataFrames, SQL queries, caching, and job tracking.

Build confidence to apply Spark MLlib in real-world business projects.
Requirements

Basic knowledge of programming (Scala or Python familiarity is helpful but not mandatory).

A computer with Windows, Linux, or MacOS.

Willingness to install software (Java, Apache Zeppelin, Docker, or Databricks free account).

Basic understanding of machine learning concepts (regression, training, testing).

No prior knowledge of Spark MLlib is required — everything will be taught from scratch.

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