TensorFlow is a state-of-the-art, open-source framework that streamlines the development and execution of advanced analytics applications. It is exceedingly powerful and holds the potential to train 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 tasks like image and 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 to make 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.
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.
Intro to Machine Learning with TensorFlow
Udacity's career-focused Nano-degree — Introduction to ML with TensorFlow covers the basics of machine learning algorithms, as well as advanced deep learning concepts. It is one of the most comprehensive programs of its kind. Learners build and deploy large-scale machine learning projects, and develop a good understanding of neural network design and training in TensorFlow.
Career Outcomes
This job-focused program approaches machine learning and AI from a technical perspective, equipping data practitioners with the technical vocabulary to work with scikit-learn, PyTorch, and TensorFlow. It's filled with portfolio-worthy projects that provide functional knowledge to create recommendation systems and voice assistants.
Objectives
The training content is extensive and covers everything needed to help you build a firm foundation in the fundamentals. You will learn 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 suitable for Python programmers with knowledge of basic machine learning concepts, including mathematical statistics.
Intro to Machine Learning with TensorFlow — Udacity
Introduction to TensorFlow in Python
Developed by Isaiah Hull, Senior Economist at Riksbank, Introduction to TensorFlow in Python is a deep learning course that covers artificial neural networks, deep learning, linear models, recurrent neural networks, functional APIs, and more. It will help you leverage the latest deep learning advancements to design innovative solutions with TensorFlow.
Career Outcomes
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 how it empowers recommendation systems and image classification, growing your understanding of neural networks.
Objectives
The curriculum covers the complex algorithms of how machine learning really works. It covers the basics of 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 that you have basic knowledge of supervised learning with scikit-learn and statistics with Python. While the classes aren't live, there are community features and career services to access the best data jobs through their platform.
Introduction to TensorFlow in Python — DataCamp
TensorFlow: Data and Deployment Specialization
Deeplearning.ai is an 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 who want to build technical knowledge from the ground up to access, organize, and process training data more easily using TensorFlow data services.
The training content is developed by AI pioneers Laurence Moroney and Andrew Ng, and students praise their ability to explain the mathematical concepts involved in an understandable way. You'll also gain functional knowledge 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 about different deployment scenarios and devise unique ways to use data productively when training your models. You'll implement projects that you can also showcase in your data science portfolio for job interviews.
Objectives
The program covers topics such as convolutional neural networks, mathematical optimization, artificial neural networks, ETL, and data pipelines. You can also expect to learn about various real-world ML applications using TensorFlow and develop your skills in TensorFlow Serving, TensorFlow Hub, TensorBoard, and more.
Is it right for you?
This program is designed for experienced data practitioners who want to learn advanced ML concepts. It is best suited for experienced programmers with an understanding of machine learning algorithms, including neural networks and mathematical programming.
TensorFlow: Data and Deployment Specialization — DeepLearning.ai
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 with simple linear regressions and progresses to more complex deep learning neural networks, teaching 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 designed to help you learn the mathematical ways that Deep Learning is applied to data, tools, and services. You'll learn how to formulate problems in TensorFlow and how to employ Deep Learning techniques to solve them.
Objectives
The curriculum covers TensorFlow in R, deep learning in TensorFlow, TensorFlow APIs, creating a deep neural network, and using tools to increase model accuracy. You'll also improve your skills in using 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 who want to gain a better understanding and sharper intuition about translating problems into deep learning terms and using TensorFlow to implement these formulations, as well as which methods to consider in what contexts using the R language. You must have a prerequisite working knowledge of statistics with R and be comfortable with linear regression.
Introduction to TensorFlow in R
TensorFlow: Advanced Techniques Specialization
TensorFlow: Advanced Techniques Specialization covers computer vision and generative deep learning concepts to help expand your working knowledge of the Functional API and non-sequential model types. It will 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 Outcomes
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.
Objectives
All learning modules are self-paced and feature hands-on tutorials on custom and distributed training. They cover the use of computer vision and generative deep learning, and give students a broad overview of custom models, layers, and loss functions with TensorFlow.
Is it right for you?
This program is best suited for experienced learners. You need to have a fairly good understanding of advanced mathematical and statistical concepts, as well as some experience in both machine learning and deep learning.
TensorFlow: Advanced Techniques Specialization — Deeplearning.ai
Related books
Adjunctive resources
- Mathematics for Machine Learning
- Learn Computational Statistics with Python
- Learn Introductory Statistics with R
- Responsible Machine Learning for Finance Courses
- High-ROI Machine Learning Certification
FAQs — Learning TensorFlow
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.