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Building Machine Learning Solutions with TensorFlow.js 2
Abhishek Kumar
Abhishek Kumar
In this course, you'll learn to use TensorFlow.js to build, train, and deploy machine learning and deep learning models to power client-side and server-side applications using the JavaScript language.
Read more
In this course, you'll learn to use TensorFlow.js to build, train, and deploy machine learning and deep learning models to power client-side and server-side applications using the JavaScript language.
Read more
In this course, you'll learn to use TensorFlow.js to build, train, and deploy machine learning and deep learning models to power client-side and server-side applications using the JavaScript language.
Machine learning and deep learning are powering some of the most groundbreaking applications of the current era. However, up until recently, JavaScript was not considered the go-to language for machine learning model development and deployment, despite being one of the most popular languages in the world. TensorFlow.js now allows JavaScript developers to extend their skills to build, train, and deploy machine learning and deep learning models. In this course, Building Machine Learning Solutions with TensorFlow.js 2, you'll learn about the TensorFlow.js ecosystem and how to set it up on the client-side in the browser and on the server-side with Node.js. First, you'll discover how to use the environment to build an end-to-end machine learning application that uses natural language processing (NLP) under the hood to detect toxic elements in unstructured text. Next, you'll learn how to import and process data, build, train, and export a model, and finally predict using the trained model. Finally, you'll explore how to use existing models trained in Python on the client-side using TensorFlow.js, and even retrain the pre-trained model using transfer learning. By the end of this course, you'll have the skills and knowledge of TensorFlow.js to build, train, and deploy machine learning and deep learning models on the client-side, as well as on the server-side that can power sophisticated applications.
Register for this course and see more details by visiting:
OpenCourser.com/course/ugtooi/building
What's inside
Syllabus
Course Overview
Introduction
Setting up TensorFlow.js Environment
Understanding TensorFlow.js Core Concepts
Read more
Syllabus
Course Overview
Introduction
Setting up TensorFlow.js Environment
Understanding TensorFlow.js Core Concepts
Read more
Preparing Data for Machine Learning Model: Part 1
Preparing Data for Machine Learning Model: Part 2
Building, Training, and Evaluating Machine Learning Model
Saving and Loading Machine Learning Model
Predicting Using Trained Machine Learning Model
Using Pre-trained Models with TensorFlow.js
What's Next?
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Designed for JavaScript, a common language for machine learning and deep learning development and deployment, this course can help you if you're already familiar with the language and want to expand your skillset
Led by Abhishek Kumar, this course can help you deepens your skillset and knowledge in machine learning model development and deployment in TensorFlow.js on both the client and server side
This course provides hands-on exploration of how to use natural language processing (NLP) under the hood to detect toxic elements in unstructured text
Through this course, you will learn how to explore pre-trained models trained in Python on the client-side using TensorFlow.js, and even retrain the pre-trained model using transfer learning
Students are expected to come in with some background knowledge in machine learning and deep learning
Save this course
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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 Building Machine Learning Solutions with TensorFlow.js 2 with these
activities:
Practice JavaScript Coding
Show steps
Refreshes and strengthens JavaScript coding skills, which are essential for working with TensorFlow.js.
Browse courses on
JavaScript
Show steps
-
Review JavaScript syntax and concepts
-
Practice solving JavaScript coding challenges
-
Build small JavaScript applications
Create a TensorFlow.js Resource Compilation
Show steps
Enhances organization and retention by compiling relevant notes, assignments, and resources related to TensorFlow.js in one accessible location.
Show steps
-
Gather and organize notes, assignments, and resources
-
Create a digital or physical compilation
Review Linear Algebra
Show steps
Improves foundational knowledge in Linear Algebra, which is a prerequisite for understanding the mathematical concepts used in TensorFlow.js.
Browse courses on
Linear Algebra
Show steps
-
Review notes and textbooks on Linear Algebra
-
Practice solving Linear Algebra problems
-
Take a practice test or quiz on Linear Algebra
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Data Preprocessing and Engineering Drills
Show steps
Engage in hands-on drills to master data preprocessing and engineering techniques, enabling you to prepare high-quality data for your TensorFlow.js models.
Browse courses on
Data Preprocessing
Show steps
-
Clean and normalize data
-
Handle missing values and outliers
-
Feature engineering and transformation
Follow TensorFlow.js Tutorials
Show steps
Provides hands-on experience with TensorFlow.js, familiarizing students with the library's syntax and basic operations.
Show steps
-
Complete the official TensorFlow.js tutorials
-
Follow additional tutorials and examples from reputable sources
NLP Toxicity Detection Model
Show steps
Develop and train a custom NLP model using TensorFlow.js to detect toxic elements in text data, thus enhancing your understanding of the end-to-end ML workflow.
Browse courses on
Natural Language Processing
Show steps
-
Define project goals and requirements
-
Collect and prepare a dataset of text data
-
Build the NLP model architecture
-
Train the model and evaluate its performance
-
Deploy the model for real-world use
Practice Machine Learning Coding
Show steps
Strengthens coding skills in JavaScript and improves understanding of machine learning algorithms by applying them in practice.
Show steps
-
Solve coding exercises and challenges on websites like LeetCode and HackerRank
-
Build small-scale machine learning models using TensorFlow.js
TensorFlow.js Project Peer Review
Show steps
Participate in peer review sessions to gain valuable feedback and insights on your TensorFlow.js project from fellow learners, promoting collaboration and knowledge exchange.
Browse courses on
Peer Review
Show steps
-
Present your project to peers
-
Receive and provide constructive feedback
-
Incorporate feedback to improve your project
Join Study or Discussion Groups
Show steps
Provides opportunities for collaboration, knowledge sharing, and diverse perspectives on TensorFlow.js concepts.
Show steps
-
Identify study or discussion groups related to TensorFlow.js
-
Participate in discussions and share your knowledge
-
Collaborate on projects or assignments
TensorFlow.js Model Deployment Tutorial
Show steps
Follow a guided tutorial to deploy your TensorFlow.js model on a server or cloud platform, ensuring seamless integration and accessibility.
Browse courses on
Model Deployment
Show steps
-
Choose a deployment platform
-
Configure the deployment environment
-
Deploy the model and test its functionality
TensorFlow.js Model Presentation
Show steps
Create a presentation that showcases your TensorFlow.js model's capabilities and insights, fostering effective communication and dissemination of your work.
Show steps
-
Practice and refine presentation skills
-
Define presentation goals and audience
-
Gather and organize project materials
-
Design and develop presentation slides
-
Deliver the presentation
Contribute to TensorFlow.js Open Source Community
Show steps
Engage with the TensorFlow.js open source community by reporting bugs, contributing code, or participating in discussions, enhancing your understanding of the framework and fostering a collaborative spirit.
Browse courses on
Community Involvement
Show steps
-
Identify areas for contribution
-
Submit bug reports or documentation improvements
-
Contribute code patches or new features
-
Participate in community discussions
Mentor Junior Developers
Show steps
Reinforces understanding of TensorFlow.js by sharing knowledge and providing guidance to others, fostering a deeper grasp of the subject matter.
Show steps
-
Identify opportunities to mentor junior developers or students
-
Share your knowledge and experiences with TensorFlow.js
-
Provide guidance and support to help others learn and grow
Practice JavaScript Coding
Show steps
Refreshes and strengthens JavaScript coding skills, which are essential for working with TensorFlow.js.
Browse courses on
JavaScript
Show steps
Show steps
Show steps
- Review JavaScript syntax and concepts
- Practice solving JavaScript coding challenges
- Build small JavaScript applications
Create a TensorFlow.js Resource Compilation
Show steps
Enhances organization and retention by compiling relevant notes, assignments, and resources related to TensorFlow.js in one accessible location.
Show steps
Show steps
Show steps
- Gather and organize notes, assignments, and resources
- Create a digital or physical compilation
Review Linear Algebra
Show steps
Improves foundational knowledge in Linear Algebra, which is a prerequisite for understanding the mathematical concepts used in TensorFlow.js.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review notes and textbooks on Linear Algebra
- Practice solving Linear Algebra problems
- Take a practice test or quiz on Linear Algebra
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Data Preprocessing and Engineering Drills
Show steps
Engage in hands-on drills to master data preprocessing and engineering techniques, enabling you to prepare high-quality data for your TensorFlow.js models.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Clean and normalize data
- Handle missing values and outliers
- Feature engineering and transformation
Follow TensorFlow.js Tutorials
Show steps
Provides hands-on experience with TensorFlow.js, familiarizing students with the library's syntax and basic operations.
Show steps
Show steps
Show steps
- Complete the official TensorFlow.js tutorials
- Follow additional tutorials and examples from reputable sources
NLP Toxicity Detection Model
Show steps
Develop and train a custom NLP model using TensorFlow.js to detect toxic elements in text data, thus enhancing your understanding of the end-to-end ML workflow.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Define project goals and requirements
- Collect and prepare a dataset of text data
- Build the NLP model architecture
- Train the model and evaluate its performance
- Deploy the model for real-world use
Practice Machine Learning Coding
Show steps
Strengthens coding skills in JavaScript and improves understanding of machine learning algorithms by applying them in practice.
Show steps
Show steps
Show steps
- Solve coding exercises and challenges on websites like LeetCode and HackerRank
- Build small-scale machine learning models using TensorFlow.js
TensorFlow.js Project Peer Review
Show steps
Participate in peer review sessions to gain valuable feedback and insights on your TensorFlow.js project from fellow learners, promoting collaboration and knowledge exchange.
Browse courses on
Peer Review
Show steps
Show steps
Show steps
- Present your project to peers
- Receive and provide constructive feedback
- Incorporate feedback to improve your project
Join Study or Discussion Groups
Show steps
Provides opportunities for collaboration, knowledge sharing, and diverse perspectives on TensorFlow.js concepts.
Show steps
Show steps
Show steps
- Identify study or discussion groups related to TensorFlow.js
- Participate in discussions and share your knowledge
- Collaborate on projects or assignments
TensorFlow.js Model Deployment Tutorial
Show steps
Follow a guided tutorial to deploy your TensorFlow.js model on a server or cloud platform, ensuring seamless integration and accessibility.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Choose a deployment platform
- Configure the deployment environment
- Deploy the model and test its functionality
TensorFlow.js Model Presentation
Show steps
Create a presentation that showcases your TensorFlow.js model's capabilities and insights, fostering effective communication and dissemination of your work.
Show steps
Show steps
Show steps
- Practice and refine presentation skills
- Define presentation goals and audience
- Gather and organize project materials
- Design and develop presentation slides
- Deliver the presentation
Contribute to TensorFlow.js Open Source Community
Show steps
Engage with the TensorFlow.js open source community by reporting bugs, contributing code, or participating in discussions, enhancing your understanding of the framework and fostering a collaborative spirit.
Browse courses on
Community Involvement
Show steps
Show steps
Show steps
- Identify areas for contribution
- Submit bug reports or documentation improvements
- Contribute code patches or new features
- Participate in community discussions
Mentor Junior Developers
Show steps
Reinforces understanding of TensorFlow.js by sharing knowledge and providing guidance to others, fostering a deeper grasp of the subject matter.
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior developers or students
- Share your knowledge and experiences with TensorFlow.js
- Provide guidance and support to help others learn and grow
Career center
Learners who complete Building Machine Learning Solutions with TensorFlow.js 2 will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and maintaining machine learning models, which can be used for a variety of tasks, such as image recognition, natural language processing, and predictive analytics. TensorFlow.js is a popular JavaScript library for building machine learning models, and this course will teach you how to use it to create your own models. This course will help you build a strong foundation in machine learning and TensorFlow.js, which will be invaluable if you want to pursue a career as a Machine Learning Engineer.
Data Scientist
Data Scientist
Data Scientists use machine learning and other statistical techniques to extract insights from data. TensorFlow.js is a powerful tool for data scientists, as it allows them to quickly and easily build and train machine learning models. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as a Data Scientist. TensorFlow.js has become a popular choice for Data Scientists due to its flexibility and ease of use, especially for those who prefer working in JavaScript.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. TensorFlow.js is a powerful tool for software engineers, as it allows them to add machine learning capabilities to their applications. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as a Software Engineer. The integration of machine learning into software applications is becoming increasingly common, making this course a valuable addition to any Software Engineer's skillset.
Web Developer
Web Developer
Web Developers design and develop websites. TensorFlow.js is a powerful tool for web developers, as it allows them to add machine learning capabilities to their websites. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as a Web Developer. With the growing popularity of machine learning, web developers who are proficient in TensorFlow.js will be in high demand.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. TensorFlow.js is a powerful tool for quantitative analysts, as it allows them to quickly and easily build and train machine learning models. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as a Quantitative Analyst. TensorFlow.js is particularly well-suited for financial data analysis due to its ability to handle large datasets and complex models.
Research Scientist
Research Scientist
Research Scientists conduct scientific research in a variety of fields. TensorFlow.js is a powerful tool for research scientists, as it allows them to quickly and easily build and train machine learning models. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as a Research Scientist. TensorFlow.js is particularly well-suited for research due to its flexibility and ease of use.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines. TensorFlow.js is a powerful tool for data engineers, as it allows them to quickly and easily build and train machine learning models. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as a Data Engineer. TensorFlow.js is particularly well-suited for data engineering due to its ability to handle large datasets and complex models.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. TensorFlow.js is a powerful tool for machine learning researchers, as it allows them to quickly and easily build and train machine learning models. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as a Machine Learning Researcher. TensorFlow.js is particularly well-suited for research due to its flexibility and ease of use.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop artificial intelligence systems. TensorFlow.js is a powerful tool for artificial intelligence engineers, as it allows them to quickly and easily build and train machine learning models. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as an Artificial Intelligence Engineer. TensorFlow.js is particularly well-suited for artificial intelligence due to its ability to handle large datasets and complex models.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. TensorFlow.js is a powerful tool for business analysts, as it allows them to quickly and easily build and train machine learning models. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as a Business Analyst. TensorFlow.js is particularly well-suited for business analysis due to its ability to handle large datasets and complex models.
Product Manager
Product Manager
Product Managers design and develop new products. TensorFlow.js is a powerful tool for product managers, as it allows them to quickly and easily build and train machine learning models. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as a Product Manager. TensorFlow.js is particularly well-suited for product management due to its ability to handle large datasets and complex models.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to help businesses make better marketing decisions. TensorFlow.js is a powerful tool for marketing analysts, as it allows them to quickly and easily build and train machine learning models. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as a Marketing Analyst. TensorFlow.js is particularly well-suited for marketing analysis due to its ability to handle large datasets and complex models.
Financial Analyst
Financial Analyst
Financial Analysts use data to help businesses make better financial decisions. TensorFlow.js is a powerful tool for financial analysts, as it allows them to quickly and easily build and train machine learning models. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as a Financial Analyst. TensorFlow.js is particularly well-suited for financial analysis due to its ability to handle large datasets and complex models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to help businesses make better decisions. TensorFlow.js is a powerful tool for operations research analysts, as it allows them to quickly and easily build and train machine learning models. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as an Operations Research Analyst. TensorFlow.js is particularly well-suited for operations research due to its ability to handle large datasets and complex models.
Risk Analyst
Risk Analyst
Risk Analysts use data to help businesses make better decisions about risk. TensorFlow.js is a powerful tool for risk analysts, as it allows them to quickly and easily build and train machine learning models. This course will teach you how to use TensorFlow.js to build your own models, which will be invaluable if you want to pursue a career as a Risk Analyst. TensorFlow.js is particularly well-suited for risk analysis due to its ability to handle large datasets and complex models.
For more career information including salaries, visit:
OpenCourser.com/course/ugtooi/building
Reading list
We've selected seven 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
Building Machine Learning Solutions with TensorFlow.js 2.
Provides a comprehensive introduction to deep learning using Python. It covers the fundamentals of deep learning, as well as how to build and train deep learning models for a variety of tasks.
Provides a comprehensive guide to TensorFlow 2, the latest version of the TensorFlow machine learning library. It covers the fundamentals of TensorFlow 2, as well as how to build and train machine learning models for a variety of tasks.
Provides a concise overview of the good parts of JavaScript. It covers the essential features of JavaScript, as well as how to avoid common pitfalls.
Provides a collection of design patterns for Node.js. It covers a wide range of topics, including asynchronous programming, error handling, and testing.
Provides a fast-paced introduction to JavaScript for experienced programmers. It covers the fundamentals of JavaScript, as well as how to build and deploy web applications.
Provides a comprehensive guide to JavaScript. It covers the fundamentals of JavaScript, as well as how to build and deploy web applications.
Provides a comprehensive guide to JavaScript algorithms and data structures. It covers the fundamentals of algorithms and data structures, as well as how to implement them in JavaScript.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ugtooi/building
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Time
248 hours
Level
Intermediate
Via
Pluralsight
Instructor
Abhishek Kumar
Language
English
Good to know
Designed for JavaScript, a common language for machine learning and deep learning development and deployment, this course can help you if you're already familiar with the language and want to expand your skillset
Led by Abhishek Kumar, this course can help you deepens your skillset and knowledge in machine learning model development and deployment in TensorFlow.js on both the client and server side
This course provides hands-on exploration of how to use natural language processing (NLP) under the hood to detect toxic elements in unstructured text
Through this course, you will learn how to explore pre-trained models trained in Python on the client-side using TensorFlow.js, and even retrain the pre-trained model using transfer learning
Students are expected to come in with some background knowledge in machine learning and deep learning
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