double quote Supercharge your career growth in Artificial Intelligence
  1. Great Learning
  2. Free Courses
  3. Artificial Intelligence

TensorFlow Python

Comprehend the essential fundamentals of Machine Learning using TensorFlow Python through our free course. Learn neural networks, image classification, and tensor concepts online.

4.5
average rating

Ratings

Intermediate

Level

3.75 Hrs

Learning hours

9.2K+
local_fire_department

Learners

Skills you’ll Learn

About this Free Certificate Course

TensorFlow is an open-source library used for creating machine learning models. There are various uses of TensorFlow that particularly focuses on training and deploying deep neural networks. You will get familiar with deep learning, TensorFlow library, tensors, image classification, and neural network concepts of Machine Learning and Artificial Intelligence. Further, you will be learning how you can create deep learning models through the hands-on demonstration given by the tutor. Enroll in this free course and complete all the modules, followed by a quiz to gain a course completion certificate. 

Are you up for stepping into an advanced career in Machine Learning? Great Learning provides professional Artificial Intelligence and Machine Learning courses that cover all the important concepts to help you build a career in this domain. Enroll in the paid programs of Great Learning to advance your skills and achieve a certificate. 

 

Why upskill with us?

check circle outline
1000+ free courses
In-demand skills & tools
access time
Free life time Access

Course Outline

Introduction for TensorFlow

With the high demand for Deep Learning, it is essential to learn about TensorFlow to create Deep Learning models. Here, we will learn what TensorFlow is, the essential library, and the in-demand skill. 

 

What are Tensors?

This section will discuss the prerequisites to understand tensors, including Linear Algebra, Vector Calculus, and Python Calculus, and how they work together to provide effective results.

 

 

 

How to install TensorFlow?

In this section, you will learn about the latest version and compatibility and how you can install TensorFlow based on what GUI/CLI you use for Python.

 

Getting Started with TensorFlow

In this section, you will understand what a Tensor looks like, how it works, how we can programmatically write the Tensor, and how to get it to perform operations for us eventually.

 

 

Demo #1: MNIST Character Recognition with TensorFlow

This will be a hands-on session discussing 2 use cases: Digit classification using the MNIST dataset & image classification using CNN. This chapter will talk about the former demo.

 

Demo #2: Binary classifier using Convolutional Neural Network

In this section, you will get clarity on what CCN is and then go on to solve the demo use case.

 

Premium programs from top universities

Make the right decision for your career growth today!

KNOW MORE

Trusted by 10 Million+ Learners globally

What our learners say about the course

Find out how our platform helped our learners to upskill in their career.

4.5
Course Rating
69%
21%
8%
1%
1%

What our learners enjoyed the most

Ratings & Reviews of this Course

Reviewer Profile

5.0

Great Introduction to TensorFlow for Beginners!
I thoroughly enjoyed the online course about TensorFlow. The lessons were well-structured, making complex concepts easy to understand. I particularly liked the hands-on projects, which allowed me to apply what I learned in real-world scenarios. The instructor was knowledgeable and responsive to questions, enhancing the overall learning experience. This course has greatly improved my confidence in using TensorFlow for machine learning projects!
Reviewer Profile

5.0

TensorFlow Course on Google Colab Using Python
This is a nicely structured TensorFlow course on Google Colab using Python.
Reviewer Profile

4.0

Introduction to TensorFlow, Python for Data Analysis
I like everything in the course, the instructors, and the content.
Reviewer Profile

5.0

TensorFlow Python Certification
The video lengths are suitable and manageable, making it easy to follow along without feeling overwhelmed. Moreover, I genuinely enjoyed the instructor's explanations, as they were clear and insightful. He showed great examples that illustrated the concepts perfectly and provided the practical insights I was hoping to see. This approach made the learning experience both engaging and informative.

TensorFlow Python

3.75 Learning Hours . Intermediate

Why upskill with us?

check circle outline
1000+ free courses
In-demand skills & tools
access time
Free life time Access
10 Million+ learners

Success stories

Can Great Learning Academy courses help your career? Our learners tell us how.

And thousands more such success stories..

Frequently Asked Questions

How long does it take to complete this free TensorFlow Python course?

The course contains 2 hours of video content that you can finish at your own pace. 

 

Will I have lifetime access to the free course?

Yes, the course comes with lifetime access. So whenever you feel like brushing up on your skills, you can revisit the course and start revising your knowledge.

Will I have lifetime access to the free course?

Yes, the course comes with lifetime access. So whenever you feel like brushing up on your skills, you can revisit the course and start revising your knowledge.

What are the prerequisites required to learn this TensorFlow Python course?

There are no particular prerequisites required to start this free course. Any learners can take this course and start learning from it.  

What are the steps to enroll in this course?

The following steps are required to enroll in this course:

1. Go to the Great Learning Academy homepage.

2. Search for ‘TensorFlow Python Course’.

3. Click on the ‘Enroll for free’ button. 

4. Register yourself with Great Learning Academy by providing the necessary information.

5. After successful registration, you need to go to your profile dashboard to start the course. 

 

Who is eligible to take this TensorFlow Python course?

There are no specific criteria for taking this course. Great Learning Academy design these courses in a way that any learner can easily adapt the knowledge no matter at which level they are. So, whether you are a beginner or a mid-level ML developer, the course will help both of you to enhance your knowledge. Hence, the course doesn’t require any prior knowledge of the concepts discussed in this course. 

Why choose Great Learning Academy for this TensorFlow Python course?

Great Learning Academy is one of the largest platforms that provides education in various domains. These courses are beneficial for learners seeking to build their careers. This free TensorFlow Python course is very helpful for those looking forward to pursuing a career in the Machine Learning domain. The course covers all the fundamental concepts of TensorFlow to start building ML workflows. 

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, Great Learning Academy offers multiple courses in various domains that you can sign up for. And there’s no limit to signing up for these courses. So, you can also sign up for other courses without any problem. 

Is there a limit on how many times I can take this TensorFlow Python course?

No, it doesn’t limit you to learning from this course. You can take this course as many times as you want. Hence, you can revisit the course whenever you feel like revising your skills.

How much does this TensorFlow Python course cost?

This TensorFlow Python course doesn’t cost anything to any learner. The course is 100% free, and learners can enroll in this course without any hustle. 

 

What knowledge and skills will I gain upon completing this TensorFlow Python course?

The free course will help you understand TensorFlow basics by familiarizing you with the essential concepts such as Deep Learning, Neural Networks, Python, and Machine Learning models. You will also gain some useful skills through this course, including the TensorFlow library, Convolutional Neural Networks, Tensors, and Deep Learning models. 

 

Will I get a certificate after completing this TensorFlow Python course?

Yes, once you finish all the course modules, you need to take the quiz that will help you gain a course completion certificate. The quiz will be active only after you finish the chapters of the course. So, you need to complete the modules and then go for the quiz to get a certificate. 

 

What jobs demand that you learn TensorFlow Python?

TensorFlow is one of the most important skills in the Machine Learning domain, which is why most Machine Learning jobs require TensorFlow as a primary skill. Several jobs demand TensorFlow skills that include:

  • Machine Learning Engineer
  • ML Algorithms Engineer
  • Computer Vision Engineer
  • Python Engineer – TensorFlow
  • TensorFlow Software Engineer

Why is TensorFlow so popular?

TensorFlow has made the implementation of Machine Learning easier as it provides pre-trained models, data, and high-end APIs, which speeds up the process of developing workflows. TensorFlow has given huge contributions to Machine Learning, and that’s the main reason for its popularity and high demand. 

What is TensorFlow used for?

TensorFlow is used to create and train Machine Learning models. These models automate most of the manual processes and enable efficient workflows. You can use Python or JavaScript to develop workflows using TensorFlow.

Is it worth learning TensorFlow?

Yes, learning TensorFlow will help you to understand the workflows of training ML models using Python. Machine Learning is the fastest growing technology, and you can understand the basics of ML with TensorFlow Python in this course. 

What are my next learning options after this TensorFlow Python course?

Once you finish this free TensorFlow course, you can opt for a professional Machine Learning course to build a career in this domain. The paid program will help you clear all the important concepts that revolve around TensorFlow and Python. 

Recommended Free AI courses

Free
Computer Vision Essentials
course card image

Free

Beginner

Free
Neural Network in R
course card image

Free

INTERMEDIATE

Free
Convolutional Neural Networks
course card image

Free

INTERMEDIATE

Similar courses you might like

Free
Introduction to Neural Networks and Deep Learning
course card image

Free

INTERMEDIATE

Free
Batch Normalization
course card image

Free

INTERMEDIATE

Free
Introduction to Artificial Intelligence
course card image

Free

Beginner

Free
Computer Vision Projects
course card image

Free

INTERMEDIATE

Related Artificial Intelligence Courses

50% Average salary hike
Explore degree and certificate programs from world-class universities that take your career forward.
Personalized Recommendations
checkmark icon
Placement assistance
checkmark icon
Personalized mentorship
checkmark icon
Detailed curriculum
checkmark icon
Learn from world-class faculties

Other Artificial Intelligence tutorials for you

Why TensorFlow?

TensorFlow is an open-source machine learning framework that is accessible to all programmers. Machine learning and deep learning applications are implemented using it. TensorFlow was designed by the Google team to help them develop and investigate fascinating artificial intelligence ideas. Because TensorFlow is written in Python, it is a simple framework to grasp.

 

TensorFlow is a Google-developed software library or framework for quickly implementing machine learning and deep learning principles. It combines computational algebra and optimization approaches to make many mathematical equations simple to calculate.

 

TensorFlow has a lot of machine learning libraries and is well-documented. It provides a few key functions and ways for doing so.

TensorFlow is sometimes referred to as a "Google" product. A wide range of machine learning and deep learning algorithms are included. For handwritten digit classification, image recognition, word embedding, and the generation of other sequence models, TensorFlow can train and run deep neural networks.

 

TensorFlow Python:

To give a specific example, AI can help Google users have a faster and more refined search experience. When a user types a keyword into Google's search field, the search engine makes a suggestion for the next word. Google intends to employ machine learning to make the most of their vast datasets in order to provide the best possible experience for their users. Machine learning is used by three different groups:

  • Researchers

  • Data Scientists

  • Programmers

 

They may all collaborate and enhance their efficiency by using the same tools.

TensorFlow was built to scale since Google has more than just data; they also have the world's most powerful computer. TensorFlow is a machine learning and deep neural network research library created by the Google Research Team.

 

It was designed to run on a variety of CPUs, GPUs, and even mobile operating systems, and it includes wrappers in Python and C++.

 

TensorFlow Components:

Tensor:

TensorFlow gets its name from its underlying framework, Tensor. Tensors are used in every computation of TensorFlow. A tensor is an n-dimensional vector or matrix that can represent any type of data. A tensor's values all have the same data type with a known (or partially known) shape. The dimensionality of the matrix or array determines the shape of the data.

 

A tensor might come from either the input data or the output of a computation. All operations in TensorFlow are carried out within a graph. The graph is a series of computations that happen in order. Each operation is referred to as an op node, and they are all linked together.

 

The graph depicts the operations and relationships that exist between the nodes. It does not, however, show the values. The tensor, or a mechanism to populate the operation with data, is the edge of the nodes.

 

Graphs:

A graph framework is used by TensorFlow. The graph collects and explains all of the training's series computations. The graph offers a lot of benefits:

  • It was designed to work on many CPUs or GPUs, as well as on mobile devices.

  • The graph's portability enables the computations to be saved for immediate or later usage. The graph can be saved and run at a later time.

  • The graph's computations are all done by joining tensors together.

 

TensorFlow Features:

  • It features a feature that uses multi-dimensional arrays called tensors to design, optimize, and calculate mathematical expressions.

  • Deep neural networks and machine learning approaches are supported by programming.

  • It has a highly scalable compute capability that works with a variety of data sets.

  • TensorFlow automates management by utilizing GPU computation. It also has a one-of-a-kind feature that optimizes the utilization of the same memory and data.

 

TensorFlow Keras:

Keras Python is a high-level Python library that runs on top of the TensorFlow framework and is small and easy to learn. It was created with the goal of better comprehending deep learning approaches, such as layering neural networks while keeping shape notions and mathematical intricacies. The following two types of framework creation exist:

  • Sequential API

  • Functional API

 

Consider these steps to build a deep learning model in Keras. 

  • The data is being loaded.

  • Pre-process the data that has been loaded.

  • The model is defined.

  • The model is being put together.

  • Fit the model you've chosen.

  • Evaluate it.

  • Make the necessary adjustments.

  • The model should be saved.

 

Installation of TensorFlow:

It is necessary to have "Python" installed on your machine in order to install TensorFlow. Python versions 3.4 and up are recommended for TensorFlow installation.

 

To install TensorFlow on a Windows computer, follow the steps below.

 

Step 1: Make sure you have the latest version of Python installed.

Step 2: To install TensorFlow in the system, a user can utilize any mechanism. "pip" and "Anaconda" are two words we recommend. Pip is a Python command for executing and installing modules.

 

We must first install the Anaconda framework on our PC before we can install TensorFlow.

Check in the command prompt with the "conda" command after the installation is complete. The command's execution is seen below.

 

Step 3: To start the TensorFlow installation, type the following command.

conda create --name tensorflow python = 3.5

 

It downloads the essential packages for TensorFlow installation.

 

Step 4: After completing the environmental configuration, the TensorFlow module must be activated.

activate tensorflow

 

Step 5: Install "Tensorflow" in the system with pip. The command that was used to install the software is listed below.

pip install tensorflow

 

And,

 

pip install tensorflow-gpu

 

After a successful installation, it's crucial to understand TensorFlow's sample program execution.

The following example demonstrates how to create a "Hello World" program in TensorFlow.

 

The code for the first programme

>> activate tensorflow

>> python (activating python shell)

>> import tensorflow as tf

>> hello = tf.constant(‘Hello, Tensorflow!’)

>> sess = tf.Session()

>> print(sess.run(hello))

Enrol for Free