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IBM Skills Network Team
and
Ramesh Sannareddy
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/s1yvpd/machine
What's inside
Syllabus
Get Started with Machine Learning
In this module, you will gain knowledge of machine learning techniques that enable computers to perform tasks without explicit programming. You will explore the lifecycle of machine learning models and understand the crucial role of data engineering in machine learning projects. The module covers supervised and unsupervised learning techniques, including classification, regression, and clustering. Furthermore, you will acquire valuable insights into Generative AI and its potential to revolutionize multiple industries, enhance people's lives, and generate newer and previously unimaginable data and experiences.
Read more
Syllabus
Get Started with Machine Learning
In this module, you will gain knowledge of machine learning techniques that enable computers to perform tasks without explicit programming. You will explore the lifecycle of machine learning models and understand the crucial role of data engineering in machine learning projects. The module covers supervised and unsupervised learning techniques, including classification, regression, and clustering. Furthermore, you will acquire valuable insights into Generative AI and its potential to revolutionize multiple industries, enhance people's lives, and generate newer and previously unimaginable data and experiences.
Read more
Machine Learning with Apache Spark
This module will introduce you to Spark and provide an overview of its key features and applications in the field of data engineering. You will discover the process of connecting to a Spark cluster using SN labs and delve into various topics such as regression, mileage prediction, classification, diabetic classification, clustering, and clustering load data using SparkML. Additionally, you will gain insights into how to construct these models using Spark ML. Moreover, this module will cover GraphFrames on Apache Spark and guide you in hands-on labs.
Data Engineering for Machine Learning using Apache Spark
This module begins with Apache Spark Structured Streaming and its role in processing streaming data with Spark SQL. You will acquire knowledge about key terms associated with Structured Streaming. The module then covers the Extract-Transform-Load process and provides hands-on experience in transferring data from one source to another destination with varying data formats or structures. Additionally, you will gain a practical understanding of feature extraction and transformation using Spark extract and transform features. The module also delves into machine learning pipelines using Spark, demonstrating the process and benefits involved. Lastly, you will grasp the concept of model persistence and its significant role in Machine Learning.
Final Project
In this module, you will apply the data engineering skills and techniques you have acquired throughout the course. The course concludes with a final project and assignments that allow you to demonstrate your proficiency in these areas.
You will step into the role of a data engineer working at a renowned aeronautics consulting company recognized for its adeptness in handling large datasets. Your role as a data engineer is crucial as the data scientists rely on your expertise to carry out ETL (Extract, Transform, Load) tasks and establish machine learning pipelines. While data scientists possess expertise in machine learning, they depend on your specialized knowledge to handle various algorithms and data formats. Your contribution plays a vital role in ensuring the smooth execution of their tasks.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores generative artificial intelligence, which can significantly transform various domains
Covers fundamental and advanced concepts in supervised and unsupervised learning techniques
Led by industry experts from the IBM Skills Network Team
Provides an in-depth understanding of Apache Spark and its functionalities in machine learning
Incorporates hands-on exercises in Spark SQL, ETL processes, and ML model development
Prepares learners for a variety of roles in data engineering, data analysis, and machine learning
Save this course
Save Machine Learning with Apache Spark to your list so you can find it easily later:
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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 Machine Learning with Apache Spark with these
activities:
Review Apache Spark Concepts
Show steps
Brush up on Apache Spark fundamentals to strengthen your understanding of the course content.
Browse courses on
Apache Spark
Show steps
-
Read Spark documentation and tutorials.
-
Complete online Spark exercises.
-
Review Spark code samples.
Join a Spark Study Group
Show steps
Engage with peers through a study group to discuss course concepts, share knowledge, and enhance your understanding.
Browse courses on
Apache Spark
Show steps
-
Find or create a study group with other participants of the course.
-
Establish a regular meeting schedule.
-
Discuss course material, share insights, and work on problems together.
Solve Spark SQL and SparkML Practice Problems
Show steps
Enhance your practical skills by solving practice problems related to Spark SQL and SparkML.
Browse courses on
Spark SQL
Show steps
-
Find online practice platforms or resources.
-
Solve a variety of practice problems.
-
Compare your solutions with others or seek feedback.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow SparkML Tutorials
Show steps
Expand your knowledge by following guided tutorials on SparkML to supplement the course material.
Show steps
-
Experiment with the provided code and examples.
-
Search for relevant SparkML tutorials.
-
Select tutorials that align with specific course topics.
-
Follow the tutorials step-by-step.
Read 'Machine Learning with Apache Spark'
Show steps
Gain deeper insights into Apache Spark for machine learning through this recommended book.
View
Advanced Analytics with PySpark: Patterns for...
on Amazon
Show steps
-
Read the book thoroughly.
-
Take notes and highlight important concepts.
-
Discuss the book's content with peers.
Build a Machine Learning Model Using SparkML
Show steps
Demonstrate your understanding by creating a machine learning model using SparkML to reinforce the course concepts.
Browse courses on
Machine Learning
Show steps
-
Gather and preprocess the necessary data.
-
Choose the appropriate SparkML algorithm.
-
Train and evaluate your model.
-
Document your approach.
Participate in Kaggle Competitions
Show steps
Put your skills to the test and gain valuable experience by participating in Kaggle competitions related to Spark.
Browse courses on
Machine Learning
Show steps
-
Identify relevant competitions on Kaggle.
-
Team up with peers or collaborate individually.
-
Develop and submit a strong solution.
-
Analyze the results and learn from the experience.
Contribute to Apache Spark Projects
Show steps
Deepen your understanding of Spark's inner workings and contribute to the community by working on open-source projects.
Browse courses on
Apache Spark
Show steps
-
Explore the Spark project repository and find areas to contribute.
-
Fork the repository and create a new branch.
-
Make changes and submit pull requests.
-
Collaborate with other developers and review code.
Review Apache Spark Concepts
Show steps
Brush up on Apache Spark fundamentals to strengthen your understanding of the course content.
Browse courses on
Apache Spark
Show steps
Show steps
Show steps
- Read Spark documentation and tutorials.
- Complete online Spark exercises.
- Review Spark code samples.
Join a Spark Study Group
Show steps
Engage with peers through a study group to discuss course concepts, share knowledge, and enhance your understanding.
Browse courses on
Apache Spark
Show steps
Show steps
Show steps
- Find or create a study group with other participants of the course.
- Establish a regular meeting schedule.
- Discuss course material, share insights, and work on problems together.
Solve Spark SQL and SparkML Practice Problems
Show steps
Enhance your practical skills by solving practice problems related to Spark SQL and SparkML.
Browse courses on
Spark SQL
Show steps
Show steps
Show steps
- Find online practice platforms or resources.
- Solve a variety of practice problems.
- Compare your solutions with others or seek feedback.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow SparkML Tutorials
Show steps
Expand your knowledge by following guided tutorials on SparkML to supplement the course material.
Show steps
Show steps
Show steps
- Experiment with the provided code and examples.
- Search for relevant SparkML tutorials.
- Select tutorials that align with specific course topics.
- Follow the tutorials step-by-step.
Read 'Machine Learning with Apache Spark'
Show steps
Gain deeper insights into Apache Spark for machine learning through this recommended book.
View
Advanced Analytics with PySpark: Patterns for...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly.
- Take notes and highlight important concepts.
- Discuss the book's content with peers.
Build a Machine Learning Model Using SparkML
Show steps
Demonstrate your understanding by creating a machine learning model using SparkML to reinforce the course concepts.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Gather and preprocess the necessary data.
- Choose the appropriate SparkML algorithm.
- Train and evaluate your model.
- Document your approach.
Participate in Kaggle Competitions
Show steps
Put your skills to the test and gain valuable experience by participating in Kaggle competitions related to Spark.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify relevant competitions on Kaggle.
- Team up with peers or collaborate individually.
- Develop and submit a strong solution.
- Analyze the results and learn from the experience.
Contribute to Apache Spark Projects
Show steps
Deepen your understanding of Spark's inner workings and contribute to the community by working on open-source projects.
Browse courses on
Apache Spark
Show steps
Show steps
Show steps
- Explore the Spark project repository and find areas to contribute.
- Fork the repository and create a new branch.
- Make changes and submit pull requests.
- Collaborate with other developers and review code.
Career center
Learners who complete Machine Learning with Apache Spark will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course can help you build a solid foundation in machine learning, Apache Spark, and data engineering. With the skills you'll gain, you'll be able to develop and deploy machine learning models that can solve real-world problems.
Data Engineer
Data Engineer
A Data Engineer plays a critical role in building and maintaining the infrastructure that supports a company's data needs. This course can help you develop the skills you need to succeed in this role, including data engineering, machine learning, and Apache Spark. With the knowledge and experience you'll gain from this course, you'll be well-prepared to build and manage data pipelines, develop machine learning models, and work with big data technologies.
Data Scientist
Data Scientist
Data Scientists use their skills in machine learning, statistics, and programming to extract insights from data. This course can help you build a strong foundation in machine learning, data engineering, and Apache Spark. With the skills you'll gain, you'll be able to develop and deploy machine learning models that can solve real-world problems.
Data Analyst
Data Analyst
Data Analysts use their skills in data analysis, statistics, and programming to extract insights from data. This course can help you build a strong foundation in machine learning, data engineering, and Apache Spark. With the skills you'll gain, you'll be able to develop and deploy data analysis pipelines that can solve real-world problems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help you build a strong foundation in machine learning, data engineering, and Apache Spark. With the skills you'll gain, you'll be able to develop and deploy software applications that can solve real-world problems.
Business Analyst
Business Analyst
Business Analysts use their skills in business analysis, data analysis, and communication to help businesses improve their operations. This course can help you build a strong foundation in machine learning, data engineering, and Apache Spark. With the skills you'll gain, you'll be able to develop and deploy business analysis models that can solve real-world problems.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course can help you build a strong foundation in machine learning, data engineering, and Apache Spark. With the skills you'll gain, you'll be able to develop and launch new products that meet the needs of your customers.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. This course can help you build a strong foundation in machine learning, data engineering, and Apache Spark. With the skills you'll gain, you'll be able to plan, execute, and close projects successfully.
Data Visualization Engineer
Data Visualization Engineer
Data Visualization Engineers are responsible for creating visualizations that help people understand data. This course can help you build a strong foundation in machine learning, data engineering, and Apache Spark. With the skills you'll gain, you'll be able to create visualizations that help people understand data and make better decisions.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and managing sales teams. This course can help you build a strong foundation in machine learning, data engineering, and Apache Spark. With the skills you'll gain, you'll be able to lead and manage sales teams to success.
Technical Support Engineer
Technical Support Engineer
Technical Support Engineers are responsible for providing technical support to customers. This course can help you build a strong foundation in machine learning, data engineering, and Apache Spark. With the skills you'll gain, you'll be able to provide technical support to customers and help them solve their problems.
DevOps Engineer
DevOps Engineer
DevOps Engineers are responsible for bridging the gap between development and operations teams. This course can help you build a strong foundation in machine learning, data engineering, and Apache Spark. With the skills you'll gain, you'll be able to bridge the gap between development and operations teams and help them work together more effectively.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products or services. This course can help you build a strong foundation in machine learning, data engineering, and Apache Spark. With the skills you'll gain, you'll be able to ensure that customers are satisfied with their products or services.
Systems Engineer
Systems Engineer
Systems Engineers are responsible for designing, developing, and maintaining computer systems. This course can help you build a strong foundation in machine learning, data engineering, and Apache Spark. With the skills you'll gain, you'll be able to design, develop, and maintain computer systems that meet the needs of your organization.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. This course can help you build a strong foundation in machine learning, data engineering, and Apache Spark. With the skills you'll gain, you'll be able to develop and execute marketing campaigns that reach your target audience.
For more career information including salaries, visit:
OpenCourser.com/course/s1yvpd/machine
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning with Apache Spark.
Looking at big data and its analytics from a Spark approach, this book provides guidance to data science and analytics professionals on how to use Spark in real-world, large-scale data projects.
Save
An established classic, this textbook provides an extensive overview of data mining and its applications in various fields, including machine learning.
Offers a practical introduction to machine learning with Python. It covers various supervised and unsupervised learning techniques.
Provides a comprehensive introduction to machine learning using Python, covering a wide range of topics from data preprocessing to model evaluation.
Provides an introduction to machine learning with Spark for beginners, including data engineering, ML algorithms, and Spark ML.
Provides a theoretical foundation for machine learning, covering topics such as probability theory, Bayesian inference, and graphical models.
A comprehensive textbook on pattern recognition and machine learning, this book covers a wide range of topics including supervised and unsupervised learning, dimensionality reduction, and statistical modeling.
A classic textbook on statistical learning, this book provides a comprehensive overview of various statistical learning methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/s1yvpd/machine
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Effort
4 weeks of study, 2-4 hours/week
Level
Intermediate
Via
Coursera
Institution
IBM
Instructors
IBM Skills Network Team
Ramesh Sannareddy
Language
English
Transcripts
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This course belongs to a
collection
called
NoSQL, Big Data, and Spark Foundations. It's comprised of three courses:
- Machine Learning with Apache Spark (viewing)
- Introduction to NoSQL Databases
- Introduction to Big Data with Spark and Hadoop
Good to know
Explores generative artificial intelligence, which can significantly transform various domains
Covers fundamental and advanced concepts in supervised and unsupervised learning techniques
Led by industry experts from the IBM Skills Network Team
Provides an in-depth understanding of Apache Spark and its functionalities in machine learning
Incorporates hands-on exercises in Spark SQL, ETL processes, and ML model development
Prepares learners for a variety of roles in data engineering, data analysis, and machine learning
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