Predictive Analytics Using Apache Spark MLlib on Databricks from Pluralsight

This course will teach you to understand and implement important techniques for predictive analytics such as regression and classification using Apache Spark MLlib APIs on Databricks.

The Spark unified analytics engine is one of the most popular frameworks for big data analytics and processing. Spark offers extremely comprehensive and easy to use APIs for machine learning which you can use to build predictive models for regression and classification and pre-process data to feed into these models.

In this course, Predictive Analytics Using Apache Spark MLlib on Databricks, you will learn to implement machine learning models using Spark ML APIs. First, you will understand the different Spark libraries available for machine learning, the older RDD-based library, and the newer DataFrame based library. You will then explore the range of transformers available in Spark for pre-processing data for machine learning - such as scaling and standardization transformers for numeric data and label encoding and one-hot encoding transformers for categorical data.

Next, you will use linear regression and ensemble models such as random forest and gradient boosted trees to build regression models. You will use these models for prediction on batch data. In addition, you will also see how you can use Spark ML Pipelines to chain together transformers and estimators to build a complete machine learning workflow.

Finally, you will implement classification models using logistic regression as well as decision trees. You will train the ML model using batch data but perform predictions on streaming data. You will also use hyperparameter tuning and cross-validation to find the best model for your data.

When you’re finished with this course, you’ll have the skills and knowledge to create ML models with Spark MLlib needed to perform predictive analysis using machine learning.

What's inside

Syllabus

Course Overview

Getting Started with Machine Learning with Apache Spark on Databricks

Performing Regression on Batch Data

Implementing Classification on Streaming Data

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches machine learning models used in industry

Develops skills needed to perform predictive analysis

Instructors have recognized work in machine learning

Uses hands-on examples and labs

Requires learners to come in with background knowledge

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Predictive Analytics Using Apache Spark MLlib on Databricks with these activities:

Organize and review notes and assignments

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Improve retention and understanding by reviewing and organizing course materials.

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Gather and organize all course notes, assignments, and quizzes.
Review the materials regularly to reinforce learning.

Read 'Machine Learning with Spark' by Holden Karau and Andy Konwinski

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Gain a comprehensive understanding of Spark MLlib and machine learning concepts.

View Learning Spark: Lightning-Fast Big Data Analysis on Amazon

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Read the relevant chapters on regression and classification.
Review the code examples and exercises provided in the book.

Follow tutorials on Spark MLlib

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Following tutorials will help you grasp the basics of how to apply Spark MLlib for machine learning.

Browse courses on Spark MLlib

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Find tutorials on Spark MLlib online or in books
Watch or read the tutorials
Try out the examples in the tutorials

Seven other activities

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Participate in a study group with other students

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Enhance understanding and retention by discussing course material with peers.

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Find a group of students who are also taking the course.
Schedule regular meetings to discuss the course material.
Take turns presenting concepts and leading discussions.

Follow a tutorial on using Spark MLlib

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Gain practical experience with Spark MLlib by following a guided tutorial.

Browse courses on Spark MLlib

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Identify a relevant tutorial on using Spark MLlib.
Follow the tutorial step-by-step.
Experiment with the code provided in the tutorial.

Solve spark mllib practice problems

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Drills for practice will help you improve your understanding of how to apply Spark MLlib for machine learning.

Browse courses on Spark MLlib

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Solve the problems using Spark MLlib
Find practice problems online or in books
Check your answers against the solutions

Complete practice regression problems

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Practice solving regression problems to reinforce the concepts learned in the course.

Browse courses on Linear Regression

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Review the lecture materials on regression.
Attempt the practice problems provided in the course.
Check your answers against the provided solutions.

Build a machine learning model using Spark MLlib

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Building your own model will force you to apply what you've learned to a new project and will help you retain knowledge.

Browse courses on Spark MLlib

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Choose a dataset and a machine learning task (regression or classification)
Load the data into Spark
Preprocess the data
Train a machine learning model
Evaluate the model

Develop a visualization of regression results

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Deepen understanding of regression results by creating visual representations.

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Gather the necessary data and regression results.
Choose an appropriate visualization tool.
Create a visualization that effectively communicates the regression results.

Build a predictive model using Spark MLlib

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Apply the skills learned in the course to a real-world project.

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Identify a suitable dataset for the project.
Preprocess the data using Spark MLlib transformers.
Build and train a predictive model using Spark MLlib estimators.
Evaluate the performance of the model.
Deploy the model for production use.

Career center

Learners who complete Predictive Analytics Using Apache Spark MLlib on Databricks will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their knowledge of statistics, programming, and machine learning algorithms to extract insights from data. The course's focus on regression, classification, data pre-processing, and model building would be extremely helpful to a Data Scientist as they build and evaluate predictive models for various business applications. Especially, the use of Spark ML pipelines and hyperparameter tuning in the course can help Data Scientists streamline their workflow and improve model performance.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, building, and deploying machine learning models. This course can be a valuable asset to Machine Learning Engineers, as it covers the practical aspects of building predictive models using Apache Spark MLlib, including data pre-processing, model selection, and evaluation. The course's focus on using Spark ML Pipelines and performing hyperparameter tuning can help Machine Learning Engineers streamline their workflow and improve model performance.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts use data to solve business problems and make informed decisions. A solid understanding of predictive analytics can help Data Analysts identify trends and patterns in data, forecast future outcomes, and make data-driven recommendations. This course can help them expand their analytical toolkit by providing hands-on experience with regression and classification models using Spark MLlib.

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Business Analyst

Business Analysts use data to understand business needs and opportunities. Predictive analytics can be a valuable tool for Business Analysts, as it allows them to forecast future trends, evaluate the impact of different decisions, and make data-driven recommendations. This course can provide Business Analysts with the technical skills needed to leverage Spark MLlib for predictive analytics.

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Statistician

Statisticians use statistical methods to collect, analyze, interpret, and present data. Predictive analytics is a specialized field within statistics that focuses on building models to predict future outcomes. This course can provide Statisticians with the practical skills needed to apply statistical concepts to real-world predictive modeling problems using Apache Spark MLlib.

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Market Researcher

Market Researchers gather and analyze data about consumers and markets. Predictive analytics can be a valuable tool for Market Researchers, as it allows them to forecast future trends, identify potential customers, and optimize marketing campaigns. This course can provide Market Researchers with the technical skills needed to leverage Spark MLlib for predictive analytics.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. Predictive analytics can be a valuable tool for Quantitative Analysts, as it allows them to forecast future trends, evaluate the risk of different investments, and make data-driven trading decisions. This course can provide Quantitative Analysts with the technical skills needed to leverage Spark MLlib for predictive analytics.

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Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. Predictive analytics can be a valuable tool for Actuaries, as it allows them to forecast future trends, evaluate the risk of different events, and make data-driven decisions. This course can provide Actuaries with the technical skills needed to leverage Spark MLlib for predictive analytics.

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Financial Analyst

Financial Analysts use financial data to make investment recommendations and evaluate the performance of companies. Predictive analytics can be a valuable tool for Financial Analysts, as it allows them to forecast future trends, identify undervalued stocks, and make data-driven investment decisions. This course can provide Financial Analysts with the technical skills needed to leverage Spark MLlib for predictive analytics.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. Predictive analytics can be a valuable tool for Operations Research Analysts, as it allows them to forecast future demand, optimize supply chains, and make data-driven decisions. This course can provide Operations Research Analysts with the technical skills needed to leverage Spark MLlib for predictive analytics.

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Software Engineer

Software Engineers design, develop, and maintain software applications. A solid understanding of predictive analytics can be beneficial to Software Engineers, as it allows them to build more intelligent and data-driven applications. This course can provide Software Engineers with the technical skills needed to leverage Spark MLlib for predictive analytics.

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Data Architect

Data Architects design and manage data systems. A solid understanding of predictive analytics can be beneficial to Data Architects, as it allows them to design systems that can support predictive modeling. This course can provide Data Architects with the technical skills needed to leverage Spark MLlib for predictive analytics.

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Database Administrator

Database Administrators manage and maintain database systems. A solid understanding of predictive analytics can be beneficial to Database Administrators, as it allows them to optimize database performance for predictive modeling workloads. This course can provide Database Administrators with the technical skills needed to leverage Spark MLlib for predictive analytics.

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Systems Analyst

Systems Analysts analyze and design computer systems. A solid understanding of predictive analytics can be beneficial to Systems Analysts, as it allows them to design systems that can support predictive modeling. This course can provide Systems Analysts with the technical skills needed to leverage Spark MLlib for predictive analytics.

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Business Intelligence Analyst

Business Intelligence Analysts use data to help businesses make better decisions. A solid understanding of predictive analytics can be beneficial to Business Intelligence Analysts, as it allows them to develop more sophisticated and data-driven insights. This course can provide Business Intelligence Analysts with the technical skills needed to leverage Spark MLlib for predictive analytics.

See salaries and explore the career path for Business Intelligence Analyst