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Mai Nguyen
and
Ilkay Altintas
Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.
At the end of the course, you will be able to:
• Design an approach to leverage data using the steps in the machine learning process.
• Apply machine learning techniques to explore and prepare data for modeling.
Read more
Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.
At the end of the course, you will be able to:
• Design an approach to leverage data using the steps in the machine learning process.
• Apply machine learning techniques to explore and prepare data for modeling.
Read more
Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.
At the end of the course, you will be able to:
• Design an approach to leverage data using the steps in the machine learning process.
• Apply machine learning techniques to explore and prepare data for modeling.
• Identify the type of machine learning problem in order to apply the appropriate set of techniques.
• Construct models that learn from data using widely available open source tools.
• Analyze big data problems using scalable machine learning algorithms on Spark.
Software Requirements:
Cloudera VM, KNIME, Spark
Register for this course and see more details by visiting:
OpenCourser.com/course/nt31to/machine
What's inside
Syllabus
Welcome
Introduction to Machine Learning with Big Data
Data Exploration
Read more
Syllabus
Welcome
Introduction to Machine Learning with Big Data
Data Exploration
Read more
Data Preparation
Classification
Evaluation of Machine Learning Models
Regression, Cluster Analysis, and Association Analysis
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores machine learning techniques, which are becoming essential in various industries
Incorporates hands-on labs and interactive materials, enhancing learning
Taught by industry experts Ilkay Altintas and Mai Nguyen, known for their research and applications in the field
Provides practical knowledge in data exploration, preparation, modeling, and analysis
Covers essential machine learning algorithms and techniques, including classification, regression, clustering, and association analysis
Requires Cloudera VM, KNIME, and Spark software, which may need to be installed and configured
Save this course
Save Machine Learning With Big Data to your list so you can find it easily later:
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Reviews summary
Hands-on: machine learning for big data
Learners say this well-received course provides a solid foundation in machine learning for big data with intuitive instructions. Students appreciate the practical assignments, which include hands-on step-by-step exercises with real-world data using KNIME and Apache Spark. While some learners struggled with outdated software and technical issues, the overall feedback is largely positive.
Students appreciate the introduction to KNIME and Apache Spark, providing them with practical skills in valuable tools for data analysis.
"One of the best courses. Knime is an awesome tool."
"The course content was well structured , excellent presentation and step by step hands on practice using KNIME and Apache Spark."
"the course was very practical and covered relatable examples throughout"
The course explains machine learning concepts and techniques in a clear and understandable manner, making it accessible to learners of all levels.
"Very good course, with a excelent generalization of all cases to use it in the real life."
"It was an interesting and very informative course."
"Este curso presenta de manera clara los conceptos introductorios necesarios para realiza el " Machine Learning", las palabras finales de la tutora son un gran ejemplo y una metodología a seguir. Excellent course"
Students value the practical exercises and hands-on activities that reinforce concepts and provide a real-world perspective.
"I learned a lot especially the techniques, metrics and I loved the practical exercises."
"this course is amazing and its one of the most interesting and informative one in this specialization i really enjoyed it and the instructor is great and illustrates everything in a very simple and clear way "
"The course was clear and the instructor was good at explaining it well"
Some learners felt that the course lacked in-depth coverage of certain topics, leaving them with a superficial understanding of the subject matter.
"The course start excellent talking about categorical predictions but I would like see a similar explanation for regression or numeric predictions."
"I would like to give a three-star rating because of the following reasons:1.Very Few Exercises2.No challenging exercise3.Only discussed Decision tree classifier 4.There are other important machine learning algorithms.5.Overall I don't like the design of this course."
"too easy too short if you want ML stuff you have to look elsewhere if you very new to ML you will find this has something to learn but it's not informative enough Andrews ng's machine learning course is recommended for anyone who wants to know machine learning and if you want to know what is the foundation of machine learning then take introduction to statistical learning(ISL) book and course here"
Several learners encountered difficulties due to outdated software and technical issues, which hindered their learning experience.
"The descriptive topics were The Handson exercise could be more elaborative. Many of the commands are just written but not explained."
"this course is probably a little bit too simple for anyone with a basic background in machine learning."
"The course was thrilling with a lot of hands-on activities..but the downside was that there were errors especially in the second and last hands-on and those bugs are so annoying giving the fact that some of us are still new in the big data world and have no clue to solving such problems"
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 Big Data with these
activities:
Review probability
Show steps
Review probability theory. Concepts such as joint probability, conditional probability, and the Bayes' theorem are fundamental in Machine Learning.
Browse courses on
Probability
Show steps
-
Revisit probability axioms and theorems
-
Review conditional probability and Bayes' theorem
-
Solve practice problems on probability
Practice solving machine learning problems on LeetCode
Show steps
LeetCode offers a collection of machine learning problems to practice and improve problem-solving skills.
Show steps
-
Create a LeetCode account
-
Solve machine learning problems under the 'Machine Learning' category
-
Review solutions and learn from other users' approaches
Follow tutorials on deep learning
Show steps
Deep learning is a rapidly growing field in machine learning. Explore deep learning tutorials to expand knowledge beyond the scope of the course.
Browse courses on
Deep Learning
Show steps
-
Find online tutorials or courses on deep learning
-
Follow the tutorials and complete the exercises
-
Experiment with different deep learning architectures
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a study group
Show steps
Collaborate with peers to discuss course concepts, work on assignments, and quiz each other to reinforce learning.
Show steps
-
Find or create a study group with other students in the course
-
Meet regularly to discuss course material and work on assignments together
Organize and review course materials
Show steps
Organize and review notes, assignments, and other materials to enhance retention and recall of course concepts.
Show steps
-
Gather all course materials
-
Create a system for organizing the materials
-
Regularly review the materials
Attend industry events focused on machine learning
Show steps
Engage with professionals in the field, learn about industry trends, and explore potential career opportunities in machine learning.
Show steps
-
Research and identify industry events related to machine learning
-
Register for and attend the events
-
Network with professionals and learn about their work
Build a simple recommendation engine
Show steps
Build a simple recommendation engine using collaborative filtering or content-based filtering to solidify knowledge of machine learning algorithms.
Show steps
-
Gather and clean data
-
Choose a recommendation algorithm
-
Implement the recommendation algorithm
-
Evaluate the performance of the recommendation engine
Create a presentation on a machine learning case study
Show steps
Research and present a real-world case study that demonstrates the successful application of machine learning techniques to solve a business problem.
Show steps
-
Choose a case study that interests you
-
Research the case study and gather data
-
Develop a presentation that outlines the problem, the solution, and the results
Build a machine learning model using Spark
Show steps
Apply knowledge of Spark to build a scalable machine learning model that can handle large datasets.
Show steps
-
Choose a dataset and a machine learning algorithm
-
Install and configure Spark
-
Implement the machine learning algorithm using Spark
-
Evaluate the performance of the model
Review probability
Show steps
Review probability theory. Concepts such as joint probability, conditional probability, and the Bayes' theorem are fundamental in Machine Learning.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Revisit probability axioms and theorems
- Review conditional probability and Bayes' theorem
- Solve practice problems on probability
Practice solving machine learning problems on LeetCode
Show steps
LeetCode offers a collection of machine learning problems to practice and improve problem-solving skills.
Show steps
Show steps
Show steps
- Create a LeetCode account
- Solve machine learning problems under the 'Machine Learning' category
- Review solutions and learn from other users' approaches
Follow tutorials on deep learning
Show steps
Deep learning is a rapidly growing field in machine learning. Explore deep learning tutorials to expand knowledge beyond the scope of the course.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find online tutorials or courses on deep learning
- Follow the tutorials and complete the exercises
- Experiment with different deep learning architectures
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a study group
Show steps
Collaborate with peers to discuss course concepts, work on assignments, and quiz each other to reinforce learning.
Show steps
Show steps
Show steps
- Find or create a study group with other students in the course
- Meet regularly to discuss course material and work on assignments together
Organize and review course materials
Show steps
Organize and review notes, assignments, and other materials to enhance retention and recall of course concepts.
Show steps
Show steps
Show steps
- Gather all course materials
- Create a system for organizing the materials
- Regularly review the materials
Attend industry events focused on machine learning
Show steps
Engage with professionals in the field, learn about industry trends, and explore potential career opportunities in machine learning.
Show steps
Show steps
Show steps
- Research and identify industry events related to machine learning
- Register for and attend the events
- Network with professionals and learn about their work
Build a simple recommendation engine
Show steps
Build a simple recommendation engine using collaborative filtering or content-based filtering to solidify knowledge of machine learning algorithms.
Show steps
Show steps
Show steps
- Gather and clean data
- Choose a recommendation algorithm
- Implement the recommendation algorithm
- Evaluate the performance of the recommendation engine
Create a presentation on a machine learning case study
Show steps
Research and present a real-world case study that demonstrates the successful application of machine learning techniques to solve a business problem.
Show steps
Show steps
Show steps
- Choose a case study that interests you
- Research the case study and gather data
- Develop a presentation that outlines the problem, the solution, and the results
Build a machine learning model using Spark
Show steps
Apply knowledge of Spark to build a scalable machine learning model that can handle large datasets.
Show steps
Show steps
Show steps
- Choose a dataset and a machine learning algorithm
- Install and configure Spark
- Implement the machine learning algorithm using Spark
- Evaluate the performance of the model
Career center
Learners who complete Machine Learning With Big Data will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists play a crucial role in extracting valuable insights from data, which is essential for businesses in today's data-driven landscape. This course provides a solid foundation in machine learning techniques, enabling you to explore, analyze, and leverage data effectively. By掌握ing the concepts taught in this course, you can enhance your data science skills and advance your career in this rapidly growing field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course equips you with the knowledge and skills to excel in this role by providing a comprehensive overview of machine learning techniques, including data exploration, preparation, modeling, and evaluation. With the expertise gained from this course, you can build a strong foundation for a successful career as a Machine Learning Engineer.
Data Analyst
Data Analyst
Data Analysts are in high demand as businesses seek to make data-driven decisions. This course provides the essential skills and knowledge to succeed in this role. You will learn how to explore, prepare, and analyze data using machine learning techniques, enabling you to extract meaningful insights and contribute to informed decision-making within organizations.
Software Engineer
Software Engineer
Software Engineers play a critical role in developing and maintaining software systems. This course enhances your software engineering skills by providing a foundation in machine learning techniques. You will learn how to incorporate machine learning algorithms into software applications, enabling you to build more intelligent and efficient systems.
Business Analyst
Business Analyst
Business Analysts bridge the gap between business and technology, helping organizations make informed decisions. This course provides you with the skills to leverage machine learning techniques in your business analysis work. You will learn how to analyze data, identify trends, and develop recommendations that drive business value.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course provides a strong foundation in machine learning techniques, which are increasingly used in quantitative analysis. By mastering the concepts taught in this course, you can enhance your skills and gain a competitive edge in the field of quantitative analysis.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data infrastructure. This course provides you with the knowledge and skills to leverage machine learning techniques in your data engineering work. You will learn how to prepare and transform data for machine learning models, enabling you to build more efficient and scalable data pipelines.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course provides a foundation in machine learning techniques, which are increasingly used in operations research. By mastering the concepts taught in this course, you can enhance your skills and advance your career in operations research.
Market Researcher
Market Researcher
Market Researchers conduct research to understand consumer behavior and market trends. This course provides you with the skills to leverage machine learning techniques in your market research work. You will learn how to analyze data, identify trends, and develop insights that drive marketing strategies.
Insurance Analyst
Insurance Analyst
Insurance Analysts assess risks and determine insurance premiums. This course provides you with the skills to leverage machine learning techniques in your insurance analysis work. You will learn how to analyze data, identify trends, and develop models that help insurance companies price policies and manage risk.
Statistician
Statistician
Statisticians use statistical methods to analyze data and draw conclusions. This course provides a foundation in machine learning techniques, which are increasingly used in statistics. By mastering the concepts taught in this course, you can enhance your skills and advance your career in statistics.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. This course provides you with the skills to leverage machine learning techniques in your financial analysis work. You will learn how to analyze data, identify trends, and develop insights that drive investment decisions.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visual representations of data. This course provides you with the skills to leverage machine learning techniques in your data visualization work. You will learn how to analyze data, identify trends, and develop visualizations that effectively communicate insights to stakeholders.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course provides you with the skills to leverage machine learning techniques in your actuarial work. You will learn how to analyze data, identify trends, and develop models that help organizations manage risk.
Risk Manager
Risk Manager
Risk Managers identify and manage risks that organizations face. This course provides you with the skills to leverage machine learning techniques in your risk management work. You will learn how to analyze data, identify trends, and develop models that help organizations mitigate risks.
For more career information including salaries, visit:
OpenCourser.com/course/nt31to/machine
Reading list
We've selected 27 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 Big Data.
Comprehensive treatment of machine learning techniques with big data. It covers a wide range of topics, including data exploration, data preparation, feature engineering, model selection, and evaluation.
Provides a comprehensive treatment of machine learning with Spark. It covers a wide range of topics, including data exploration, data preparation, feature engineering, model selection, and evaluation.
Provides a comprehensive treatment of machine learning for data science. It covers a wide range of topics, including data exploration, data preparation, feature engineering, model selection, and evaluation.
Comprehensive guide to statistical learning. It covers a wide range of topics, including linear regression, logistic regression, and decision trees. It's a good resource for learning about the theory and practice of statistical learning.
Provides a comprehensive overview of advanced machine learning with Python. It covers a wide range of topics, including deep learning, natural language processing, and computer vision. It's a good resource for learning about the different tools and techniques used in building advanced machine learning systems.
Provides a practical guide to building machine learning systems with Python. It covers a wide range of topics, including data preparation, model selection, and evaluation. It's a good resource for learning about the different tools and techniques used in building machine learning systems.
Provides a comprehensive overview of big data analytics. It covers a wide range of topics, including data collection, storage, processing, and analysis.
Provides a comprehensive overview of machine learning and data mining. It covers a wide range of topics, including different types of algorithms and how to use them. It's a good resource for learning about the theory and practice of machine learning and data mining.
Provides a comprehensive treatment of machine learning for finance. It covers a wide range of topics, including financial data analysis, financial forecasting, and financial risk management.
Save
Provides a comprehensive treatment of machine learning for the web. It covers a wide range of topics, including web data collection, web data mining, and web data analysis.
Save
Provides a comprehensive overview of big data principles and best practices, covering key concepts, technologies, and best practices for building and managing scalable real-time data systems.
Provides a comprehensive overview of big data analytics with Hadoop. It covers a wide range of topics, including data storage, data processing, and data analysis.
Offers a practical guide to building machine learning models using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow.
Provides a practical introduction to machine learning for big data. It covers a wide range of topics, including data exploration, data preparation, feature engineering, and model selection.
Save
Comprehensive introduction to data mining. It covers a wide range of topics, including data preprocessing, feature selection, clustering, classification, and regression. It valuable resource for anyone who wants to learn about the fundamentals of data mining.
Offers a practical guide to big data analytics, covering key concepts, technologies, and best practices for leveraging big data in business applications.
Offers a practical introduction to data analytics, covering key concepts, techniques, and tools for data analysis and visualization.
Save
Comprehensive introduction to deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, recurrent neural networks, and deep reinforcement learning.
Good introduction to machine learning for beginners. It covers the basics of machine learning, including different types of algorithms and how to use them.
Offers a practical introduction to machine learning using R, covering key concepts, algorithms, and best practices for building machine learning models in R.
Good introduction to machine learning for beginners. It covers the basics of machine learning, including different types of algorithms and how to use them. It's a good resource for learning about the theory and practice of machine learning.
Provides a practical guide to big data analytics. It covers a wide range of topics, including data storage, data processing, and data analysis.
Offers a practical introduction to machine learning using Python, covering key concepts, algorithms, and best practices for building machine learning models in Python.
Save
Practical guide to machine learning with R. It covers a wide range of topics, including data exploration, feature engineering, model training, and model evaluation.
Provides a good introduction to data science for business. It covers a wide range of topics, including data collection, data cleaning, data analysis, and data visualization.
Comprehensive introduction to Hadoop. It covers a wide range of topics, including Hadoop architecture, data storage, data processing, and data analysis.
Save
Provides a good introduction to data mining. It covers a wide range of topics, including data preprocessing, feature selection, clustering, classification, and regression.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nt31to/machine
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Effort
5 Weeks, 3 - 5 hours per week
Via
Coursera
Institution
University of California San Diego
Instructors
Mai Nguyen
Ilkay Altintas
Language
English
Transcripts
Español
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中文
العربية
한국어
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آذربایجان دیلی
This course belongs to a
collection
called
Big Data. It's comprised of seven courses:
- Graph Analytics for Big Data
- Big Data - Capstone Project
- Introduction to Big Data
- Machine Learning With Big Data (viewing)
- Big Data Integration and Processing
- Big Data Modeling and Management Systems
- Programming for Data Science
Good to know
Explores machine learning techniques, which are becoming essential in various industries
Incorporates hands-on labs and interactive materials, enhancing learning
Taught by industry experts Ilkay Altintas and Mai Nguyen, known for their research and applications in the field
Provides practical knowledge in data exploration, preparation, modeling, and analysis
Covers essential machine learning algorithms and techniques, including classification, regression, clustering, and association analysis
Requires Cloudera VM, KNIME, and Spark software, which may need to be installed and configured
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