Online & Classroom

TensorFlow Training Course

Months Icon 3 Months
38 Modules
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Key Features

OSACAD is committed to bringing you the best learning experience with high-standard features including

Key Features
Real-time Practice Labs

Learning by doing is what we believe. State-of-the-art labs to facilitate competent training.

Key Features
Physical & Virtual Online Classrooms

Providing the flexibility to learn from our classrooms or anywhere you wish considering these turbulent times.

Key Features
24/7 Support On Slack

Technical or Technological, we give you assistance for every challenge you face round-the-clock.

Key Features
Job Interview & Assistance

Guiding in & out, until you get placed in your dream job.

Key Features
Live projects with our industry partners

An inside look & feel at industry environments by handling real-time projects.

Key Features
Internship after course

Opportunity to prove your talent as an intern at our partner firms and rope for permanent jobs.

Why TensorFlow ?

Why Data Science
Responsive Construct

With TensorFlow we can easily visualize each and every part of the graph which is not an option while using Numpy or SciKit. (edited)

Why Data Science
Parallel Neural Network Training

TensorFlow offers pipelining in the sense that you can train multiple neural networks and multiple GPUs which makes the models very efficient on large-scale systems

Why Data Science
Visualizer (with TensorBoard)

With TensorBoard you can inspect a totally different representation of a model and make the changed necessary while debugging it

Who is This program for

  • Those interested in computer vision, image restoration, text classification etc.
  • Those working with large datasets who want to simplify the effective analysis
  • Software or Data Engineers interested in learning about Deep Learning
  • Data Scientists,Data Analysts,Machine Learning Engineers
Who is this program


Best-in-class content by leading faculty and industry leaders in the form of videos,
cases and projects, assignments and live sessions.

  • What is Deep learning?
  • Deep learning Process
  • Classification of Neural Networks
  • Types of Deep Learning Networks
  • Feed-forward neural networks
  • Recurrent neural networks (RNNs)
  • Convolutional neural networks (CNN)
  • What is AI?
  • What is ML?
  • What is Deep Learning?
  • Machine Learning Process
  • Deep Learning Process
  • Automate Feature Extraction using DL
  • Difference between Machine Learning and Deep Learning
  • When to use ML or DL?
  • What is TensorFlow?
  • History of TensorFlow
  • TensorFlow Architecture
  • Where can Tensorflow run?
  • Introduction to Components of TensorFlow
  • Why is TensorFlow popular?
  • List of Prominent Algorithms supported by TensorFlow
  • 8 Best Deep learning Libraries /Framework
  • TenserFlow Vs Theano Vs Torch Vs Keras Vs Vs CNTK Vs MXNet Vs Caffe: Key Differences
  • TensorFlow Versions
  • Install Anaconda
  • Create .yml file to install Tensorflow and dependencies
  • Launch Jupyter Notebook
  • Jupyter with the main conda environment
  • What is Jupyter Notebook?
  • Jupyter Notebook App
  • How to use Jupyter
  • Set up a key pair
  • Set up a security group
  • Launch your instance (Windows users)
  • Install Docker
  • Install Jupyter
  • Close connection
  • What is a Tensor?
  • Representation of a Tensor
  • Types of Tensor
  • Shape of tensor
  • Type of data
  • Creating operator
  • Variables
  • What is NumPy?
  • Why use NumPy?
  • How to install NumPy
  • Mathematical Operations on an Array
  • Shape of Array
  • np.zeros and np.ones
  • Reshape and Flatten Data
  • hstack and vstack
  • What is Pandas?
  • Why use Pandas?
  • How to install Pandas?
  • What is a data frame?
  • What is a Series?
  • Concatenation
  • What is Scikit-learn?
  • Download and Install scikit-learn
  • Machine learning with scikit-learn
  • Import the data
  • Create the train/test set
  • Build the pipeline
  • Using our pipeline in a grid search
  • Linear regression
  • How to train a linear regression model
  • How to train a Linear Regression with TensorFlow
  • Pandas
  • Numpy Solution
  • Tensorflow solution
  • Summary statistics
  • Facets Overview
  • Facets Deep Dive
  • Install Facet
  • Overview
  • Graph
  • Facets Deep Dive
  • What is Linear Classifier?
  • How Binary classifier works?
  • How to Measure the performance of Linear Classifier?
  • Linear Classifier with TensorFlow
  • Why do you need Kernel Methods?
  • What is a Kernel in machine learning?
  • Type of Kernel Methods
  • Train Gaussian Kernel classifier with TensorFlow
  • What is Artificial Neural Network?
  • Neural Network Architecture
  • Limitations of Neural Network
  • Example Neural Network in TensorFlow
  • Train a neural network with TensorFlow
  • What is Convolutional Neural Network?
  • Architecture of a Convolutional Neural Network
  • Components of Convnets
  • Train CNN with TensorFlow
  • What is an Autoencoder?
  • How does Autoencoder work?
  • Stacked Autoencoder Example
  • Build an Autoencoder with TensorFlow
  • What do we need an RNN?
  • What is RNN?
  • Build an RNN to predict Time Series in TensorFlow
Hours of Content
Case Study & Projects
Live Sessions
Coding Assignments
Capstone Projects to Choose From
Tools, Languages & Libraries

Languages and Tools covered

Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered

Hands On Projects

Person Detection with TensorFlow and Arduino

In this Project Using the TensorFlow Lite library, we can build flash tiny machine learning models on an Arduino to detect humans from a camera

Image Noise Reduction with Auto-encoders using TensorFlow

In this project, you will learn the basics of image noise reduction with auto-encoders. Auto-encoding is an algorithm to help reduce dimensionality of data with the help of neural networks.

Basic Sentimental Analysis With TensorFlow

In this project, you will learn the basics of using Keras with TensorFlow as its backend and you will learn to use it to solve a basic sentiment analysis problem


Our training is based on latest cutting-edge infrastructure technology which makes you ready for the industry.Osacad will Present this certificate to students or employee trainees upon successful completion of the course which will encourage and add to trainee’s resume to explore a lot of opportunities beyond position

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Learn From Home

First-Ever Hybrid Learning System

Enjoy the flexibility of selecting online or offline classes with Osacad first-ever hybrid learning model.
Get the fruitful chance of choosing between the privilege of learning from home or the
advantage of one-on-one knowledge gaining - all in one place.

Learn From Home

Learn from Home

Why leave the comfort and safety of your home when you can learn the eminent non-technical courses right at your fingertips? Gig up to upskill yourself from home with Osacad online courses.

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Exploit the high-tech face-to-face learning experience with esteemed professional educators at Osacad. Our well-equipped, safe, and secure classrooms are waiting to get you on board!

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There are a number of courses, which are either 3 months or 6 months long. To become a cyber security consultant we recommend at least 6 to 9 months of training followed by 6 months of actual project work.During project work you will be working under a mentor and experiencing real life customer scenarios.

You can get started by enrolling yourself. The enrollment can be initiated from this website by clicking on "ENROLL NOW". If you are having questions or difficulties regarding this, you can talk to our counselors and they can help you with the same.

Once you enroll with us you will receive access to our Learning Center. All online classrooms, recordings, assignments, etc. can be accessed here.

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What do you benefit from this programs
  • Comprehensive Hands-on with TensorFlow
  • Covers Artificial Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks using TensorFlow
  • Gain knowledge on Transfer Learning For Computer Vision

I’m Interested

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