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Machine Learning with Apache Spark
  • Level Professional
  • المدة 15 ساعات hours
  • الطبع بواسطة IBM
  • Offered by

عن

Explore the exciting world of machine learning with this IBM course. Start by learning ML fundamentals before unlocking the power of Apache Spark to build and deploy ML models for data engineering applications. Dive into supervised and unsupervised learning techniques and discover the revolutionary possibilities of Generative AI through instructional readings and videos. Gain hands-on experience with Spark structured streaming, develop an understanding of data engineering and ML pipelines, and become proficient in evaluating ML models using SparkML. In practical labs, you'll utilize SparkML for regression, classification, and clustering, enabling you to construct prediction and classification models. Connect to Spark clusters, analyze SparkSQL datasets, perform ETL activities, and create ML models using Spark ML and sci-kit learn. Finally, demonstrate your acquired skills through a final assignment. This intermediate course is suitable for aspiring and experienced data engineers, as well as working professionals in data analysis and machine learning. Prior knowledge in Big Data, Hadoop, Spark, Python, and ETL is highly recommended for this course.

الوحدات

Welcome to the Course

    • 1 Videos
    • 2 Readings
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1 Videos

  • Course Introduction

2 Readings

  • Course Overview
  • How to make the most of this course

Get Started with Machine Learning

    • 10 Videos
    • 2 Readings
    • 5 ExternalTool
    • 1 Plugin
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Get Started with Machine Learning

5 External Tool

  • Hands-on Lab: Building and Training a Prediction Model using Linear Regression
  • Hands-on Lab: Build a Classifier Model using Logistic Regression
  • Hands-on Lab: Metrics for Regression
  • Hands-on Lab: Metrics for Classification
  • Hands-on Lab: Clustering

10 Videos

  • Introduction to Machine Learning for Everyone
  • Role of Data Engineering in Machine Learning
  • Machine Learning Model Lifecycle
  • Supervised vs Unsupervised Learning
  • Regression
  • Classification
  • Evaluating Machine Learning Models
  • Clustering
  • Generative AI Overview and Use Cases
  • Generative AI Applications

2 Readings

  • (Optional)Reading: Slope and Intercept
  • Module 1 Glossary

Module 1 Quiz and Summary

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Graded Quiz: Get Started with Machine Learning

1 Readings

  • Summary and Highlights

Machine Learning with Apache Spark

    • 5 Videos
    • 1 Readings
    • 5 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Machine Learning with Apache Spark

5 External Tool

  • Hands-on Lab: Connecting to Spark Cluster using SN Labs
  • Hands on Lab:Distributed Architecture of Spark
  • Hands-on Lab: Regression using SparkML
  • Hands-on Lab: Classification using SparkML
  • Hands-on Lab: Clustering Customer Data using SparkML

5 Videos

  • Spark for Data Engineers
  • Regression using SparkML
  • Classification using SparkML
  • Clustering using SparkML
  • GraphFrames on Apache Spark

1 Readings

  • Module 2 Glossary

Module 2 Quiz and Summary

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Graded Quiz: Machine Learning with Apache Spark

1 Readings

  • Summary and Highlights

Data Engineering for Machine Learning using Apache Spark

    • 6 Videos
    • 1 Readings
    • 6 ExternalTool
    • 2 Plugin
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Data Engineering for Machine Learning using Apache Spark

6 External Tool

  • Hands-on Lab: Analyze a dataset using SparkSQL
  • Hands-on Lab: ETL using Spark
  • Hands-on Lab: Leveraging Apache Spark for Smart Building HVAC Monitoring
  • Hands-on Lab: Feature Extraction and Transformation Lab
  • Hands-on Lab: PipeLine creation using SparkML
  • Hands-on Lab: Model Persistence

6 Videos

  • Spark SQL
  • ETL Workloads
  • Spark Structured Streaming
  • Feature Extraction and Transformation
  • Machine Learning Pipelines using Spark
  • Model Persistence

1 Readings

  • Module 3 Glossary

Module 3 Quiz and Summary

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Graded Quiz: Data Engineering for Machine Learning using Apache Spark

1 Readings

  • Summary and Highlights

Final Project

    • 2 Readings
    • 2 ExternalTool
    • 1 Assignment
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1 Assignment

  • Quiz: Final Project - Evaluation

2 External Tool

  • Practice Project: Create a Machine Learning Pipeline for a Regression Project
  • Final Project: Build a Machine Learning Pipeline for Airfoil Noise Prediction

2 Readings

  • Practice Project Overview
  • Final Project Overview

Course Wrap Up

    • 2 Readings
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2 Readings

  • Congratulations and Next Steps
  • Thanks from the Course Team

Auto Summary

Discover "Machine Learning with Apache Spark," an IBM course on Coursera designed for data science and AI enthusiasts. Dive into ML fundamentals, supervised and unsupervised learning, and the power of Apache Spark for data engineering applications. Gain hands-on experience with SparkML, SparkSQL, and practical labs on regression, classification, and clustering. Ideal for aspiring and experienced data engineers, this intermediate course spans 900 minutes. Prior knowledge in Big Data, Hadoop, Spark, Python, and ETL is recommended. Subscriptions start at the Starter level.

IBM Skills Network Team
Instructors

IBM Skills Network Team

Ramesh Sannareddy
Instructors

Ramesh Sannareddy