Learn TensorFlow with hands-on training courses and specialization programs

There are rewarding career paths available if you have some TensorFlow experience under your belt! We’ve done the hard work and collated a list of resources that provide hands-on and project based learning.

Learn TensorFlow with hands-on training courses and specialization programs
Image by TensorFlow

TensorFlow is a state-of-the-art, open-source framework that streamlines developing and executing advanced analytics applications. It's exceedingly powerful and holds the potential for training a model for any system with the help of graphs.

It is heavily used by developers, data scientists, and ml engineers to automate processes, develop new systems and parallel processing applications such as neural networks. We can train and run deep neural networks for things like image video recognition, word embeddings, handwritten digit classification, etc.

One of the tremendous advantages of TensorFlow is its open-source community of data scientists, ml researchers and data engineers who contribute to its repository to make it faster and more effective to develop and train ML and Deep Learning models. It uses Python as a front-end API for building applications with the framework, but has wrappers in several other languages, including C++ and Java. This means we can train and deploy our models rapidly, regardless of the programming language or platform.

It's undeniably worth learning TensorFlow for making your resume and portfolio strong. We have evaluated a few high-quality TensorFlow courses by world-leading educators to help edify your career goals in great leaps.

Editor's note: kanger.dev reviews resources that are not suitable for everyone. To make wise decisions, we recommend reading “Is this program right for you?” and checking out reviews.

5 Responsible TensorFlow Courses and Specialization Programs

TensorFlow supports deep learning, neural networks, and advanced statistical computing. The following resources will support you in both your academic journey and your job prospects.

Provider Program Details
Udacity Intro to Machine Learning with TensorFlow Learn more
DataCamp Introduction to TensorFlow in Python Learn more
Deeplearning.ai TensorFlow: Data and Deployment Specialization Learn more
DataCamp Introduction to TensorFlow in R Learn more
Deeplearning.ai TensorFlow: Advanced Techniques Specialization Learn more

Intro to Machine Learning with TensorFlow

Udacity's career-focused Nano-degree — Introduction to ML with TensorFlow spans the basics of machine learning algorithms, through to advanced deep learning concepts. It is one of the most comprehensive of its kind — learners build and deploy large-scale machine learning projects, develop a good understanding of neural network design and training in TensorFlow.

Career Outcome

This job-focused program approaches machine learning and AI from a technical perspective and equips data practitioners with the technical vocabulary to work with the scikit-learn, PyTorch and TensorFlow. And it's filled with portfolio-worthy projects to build you up with the functional knowledge to create recommendation systems and voice assistants.

best tensorflow course
ML with TensorFlow Nanodegree - Udacity
Objectives

The training content is extensive and covers everything to help you build a firm foundation in the fundamentals. You'll to train ML Models in the deep learning framework and develop a good understanding of ML and deep learning applications.

Is it right for you?

You will receive technical mentorship from ML experts, and have access to career coaches who help graduates land their first job. This program is practically suitable for Python programmers with a knowledge of basic machine learning concepts, including mathematical statistics.

Introduction to TensorFlow in Python

Developed by the Senior economist of Riksbank, Isaiah Hull, Introduction to TensorFlow in Python is a Deep Learning course that spans artificial neural networks, deep learning, linear models, recurrent neural networks, functional APIs and more. It is will help you leverage the latest deep learning advancements to design innovative solutions with TensorFlow.

Career Outcome

This program helps you gain skills to explore actionable strategies to address critical issues that can affect classification performance and master TensorFlow to process data in different modalities, including text, images, and graphs. You'll use TensorFlow to discover technically how it empowers recommendation systems and image classification to grow your understanding of neural networks.

TensorFlow Course Python
Introduction to TensorFlow in Python Course
Objectives

The curriculum covers complex algorithms of how machine learning really works. It hits the basics on everything from advanced models in TensorFlow, neural networks, Linear models and using high-level APIs for building deep learning models.

Is it right for you?

It is best suited for Python programmers who want to implement the latest ML and AI technology to make accurate predictions using in-depth data modeling and deep learning. It's imperative you'd need the basic knowledge of supervised learning with scikit-learn and statistics with Python. And while the classes aren’t live, there are community features and career services to access the best data jobs through their platform.

TensorFlow: Data and Deployment Specialization

Deeplearning.ai is another education technology company founded by Andrew NG, a globally recognized leader in AI. Its TensorFlow—Data and Deployment Specialization program is good for self-paced learners to build the technical knowledge from the ground up to access, organize, and process training data more easily using TensorFlow data services.

Training content is developed by AI pioneers Laurence Moroney and Andrew Ng, and students praise their ability to explain the mathematical concepts involved in a way that is understandable. You'll also gain functional knowledge of and hands-on training in advanced deployment techniques, including object detection, CNN, ETL, and more.

Career Outcome

You will develop employable data skills in this TensorFlow specialization as you learn different deployment scenarios and devise unique ways to use data productively when training your models. You'll implement projects that you also can showcase in your data science portfolio for job interviews.

Best TensorFlow Courses
TensorFlow Specialization by DeepLearning.ai | kanger.dev
Objectives

The program spans convolutional neural networks, mathematical optimization, artificial neural network, ETL, data pipelines, etc. You can also expect to learn various real-world ML applications with TensorFlow and develop your skills in TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more.

Is it right for you?

For experienced data practitioners who want to learn advanced ML concepts, this program cuts to the chase. It is practically suitable for experienced programmers with an understanding of machine learning algorithms, including neural networks and mathematical programming.

Introduction to TensorFlow in R

TensorFlow in R is a hands-on course where lectures are augmented with exercises and in class-programming labs to implement, train, and improve models using TensorFlow in R. It starts from simple linear regressions to more complex deep learning neural networks and teaches concepts that are in high demand, such as neural networks, TensorFlow, and the latest machine learning techniques that matter in practice.

Career Outcome

TensorFlow and R programming are both exceptional skills for a rewarding data career. The program is developed to help you learn the mathematical ways that Deep Learning is applied to data, tools, and services. You'll learn to formulate problems in TensorFlow and how to employ Deep Learning techniques to solve them.

TensorFlow Course R
TensorFlow in R Course by DataCamp | kanger.dev
Objectives

The curriculum spans TensorFlow in R, Deep learning in TensorFlow, TensorFlow APIs, creating a deep neural network, and using tools for increasing model accuracy. You'll also improve skills to use TensorFlow to apply probability and statistics to data sets.

Is it right for you?

This program is suitable for aspiring Data Scientists and AI/ML Engineers to gain a better understanding and sharper intuitions about translating problems into the deep learning terms and using TensorFlow to implement these formulations and which methods to consider in what contexts using R language. You must have a prerequisite working knowledge of statistics with R and comfortability with linear regression.

TensorFlow: Advanced Techniques Specialization

TensorFlow: Advanced Techniques Specialization touches on computer vision and generative deep learning concepts to help expand your working knowledge of the Functional API and non-sequential model types. It'll take you beyond the basics of using pre-defined models to more effectively build scalable, accurate, and production-ready models.

The program gives learners the opportunity to learn directly from AI experts, Laurence Moroney and Eddy Shyu.

Career Outcome

This high-quality specialization covers advanced data skills that are valuable in the competitive job market. You'll gain functional knowledge and hands-on training of advanced TensorFlow techniques to optimize training in different environments and also get introduced to advanced computer vision engineering.

TensorFlow Specialization
Advanced TensorFlow Specialization by Deeplearning.ai | kanger.dev 
Objectives

All learning modules are self-paced, featuring hands-on tutorials on custom and distributed training, covers the use of computer vision, generative deep learning, and gives students a broad overview of custom models, layers, and loss functions with TensorFlow.

Is it right for you?

This program is practically suitable for experienced learners. You need to have a fairly good understanding of advanced mathematical and statistical concepts and some experience in both machine learning and deep learning.


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Adjunctive resources

FAQs — Learning TensorFlow with hands-on training courses

Is TensorFlow for Freshers?

TensorFlow is a deep learning framework that makes machine learning easy for beginners. To learn TensorFlow, you’re going to need a reliable reservoir of expertise, ranging from statistical programming, mathematical statistics, and the ability to write algorithms, and a familiarity with basic machine learning concepts. However, even for freshers, TensorFlow is easy.

Is TensorFlow Hard to Learn?

While learning TensorFlow from scratch is difficult but having an experience in statistical programming, mathematical logic and a background in traditional science can make it easier. Depending on your current level of skills, expect learning ML with TensorFlow to take you anywhere from one to five years of religious efforts.

What is the Best TensorFlow Course?

The best TensorFlow Course is the one that meets your needs. It’s one thing to learn all the Machine Learning skills and master the tools needed to do the work of a researcher; it’s another to get a foot in the door. If you're serious about data science and machine learning, consider learning TensorFlow because of its popularity in the research community.

How Do You Choose a TensorFlow Course?

We have done the heavy lifting for you. The learning programs mentioned in this article will prepare you for a career in the chosen field of Deep Learning, even if you are a just getting started with TensorFlow. These educators go above and beyond to help give you a competitive edge. You will gain ML skills and receive a career support. These are important things to consider when choosing a course.


TL;DR

TensorFlow is undoubtedly well worth learning for increasing Job prospects. Data Scientists and ML Engineers can command high salaries in specific technical areas, and TensorFlow is popular in the research community. It's worth learning TensorFlow for making your resume portfolio strong.

Disclosure: The views expressed in this article are those of the author and do not reflect the views of TensorFlow, Google, Udacity, Deeplearning.ai, Coursera, DataCamp or their partners. This article may contain links to content on third-party sites. By providing such links, kanger.dev does not adopt, guarantee, approve, or endorse the information, views, or products available on such sites.

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