Building Machine Learning Solutions with TensorFlow.js 2 from Pluralsight

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.

What's inside

Syllabus

Course Overview

Introduction

Setting up TensorFlow.js Environment

Understanding TensorFlow.js Core Concepts

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

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

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

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Refreshes and strengthens JavaScript coding skills, which are essential for working with TensorFlow.js.

Browse courses on JavaScript

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Review JavaScript syntax and concepts
Practice solving JavaScript coding challenges
Build small JavaScript applications

Create a TensorFlow.js Resource Compilation

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Enhances organization and retention by compiling relevant notes, assignments, and resources related to TensorFlow.js in one accessible location.

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Gather and organize notes, assignments, and resources
Create a digital or physical compilation

Review Linear Algebra

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Improves foundational knowledge in Linear Algebra, which is a prerequisite for understanding the mathematical concepts used in TensorFlow.js.

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Review notes and textbooks on Linear Algebra
Practice solving Linear Algebra problems
Take a practice test or quiz on Linear Algebra

Ten other activities

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Data Preprocessing and Engineering Drills

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Engage in hands-on drills to master data preprocessing and engineering techniques, enabling you to prepare high-quality data for your TensorFlow.js models.

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Clean and normalize data
Handle missing values and outliers
Feature engineering and transformation

Follow TensorFlow.js Tutorials

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Provides hands-on experience with TensorFlow.js, familiarizing students with the library's syntax and basic operations.

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Complete the official TensorFlow.js tutorials
Follow additional tutorials and examples from reputable sources

NLP Toxicity Detection Model

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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.

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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

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Strengthens coding skills in JavaScript and improves understanding of machine learning algorithms by applying them in practice.

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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

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Participate in peer review sessions to gain valuable feedback and insights on your TensorFlow.js project from fellow learners, promoting collaboration and knowledge exchange.

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Present your project to peers
Receive and provide constructive feedback
Incorporate feedback to improve your project

Join Study or Discussion Groups

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Provides opportunities for collaboration, knowledge sharing, and diverse perspectives on TensorFlow.js concepts.

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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

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Follow a guided tutorial to deploy your TensorFlow.js model on a server or cloud platform, ensuring seamless integration and accessibility.

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Choose a deployment platform
Configure the deployment environment
Deploy the model and test its functionality

TensorFlow.js Model Presentation

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Create a presentation that showcases your TensorFlow.js model's capabilities and insights, fostering effective communication and dissemination of your work.

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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

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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.

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Identify areas for contribution
Submit bug reports or documentation improvements
Contribute code patches or new features
Participate in community discussions

Mentor Junior Developers

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Reinforces understanding of TensorFlow.js by sharing knowledge and providing guidance to others, fostering a deeper grasp of the subject matter.

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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 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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Software Engineer

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.

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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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Research Scientist

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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Machine Learning Researcher

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.

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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.

See salaries and explore the career path for Business Analyst

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.

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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.

See salaries and explore the career path for Marketing 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.

See salaries and explore the career path for Financial 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.

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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.

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Building Machine Learning Solutions with TensorFlow.js 2

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