CERTIFIED COMPUTER VISION EXPERT COURSE

• Evolution of Human Intelligence
• What is Artificial Intelligence?
• History of Artificial Intelligence
• Why Artificial Intelligence now?
• AI Terminologies
• Areas of Artificial Intelligence
• AI vs Data Science vs Machine Learning

• Foundation of AI Data
• Data Lake
• Four Stages of Building and Integrating Data Lakes within Technology Architectures

• Issues and Concerns around AI
• AI and Ethical Concerns
• AI and Bias
• AI: Ethics, Bias, and Trust

• Challenges of AI Implementation
• Pitfalls and Lessons from the Industry
• Usecases from top AI Implementation
• Future with AI
• The Journey for adopting AI successfully

• Introduction to Tensorflow 2.X
• Tensor + Flow = Tensorflow
• Components and Basis Vectors
• Sequential and Functional APIs

• Creating a Tensor
• Tensor Rank /Degree
• Shape of a Tensor
• Create Flow for Tensor Operation
• Usability-Related Changes
• Performance-Related Changes

• Tensorflow 2.X Installation and Setup
• Anaconda Distribution Installation
• Colab – Free Powerful Lab from Google
• Databricks
• Tensorflow V1.X  Vs Tensorflow V2.X
• Tensorflow Architecture
• Tf 2.0 Basic Syntax

• Tensorflow Graphs
• Variables and Placeholders
• Operations and Control Statements
• Tf 2.0 Eager Execution Mode
• Tf 2.0 Autograph Tf.Function
• Application of Tensorflow Platform

• Keras Package Introduction
• Inbuilt Keras in Tensorflow2.X
• Using Keras Modules for Nn Modelling

• Neural Networks - Inspiration from the Human Brain
• Introduction to Perceptron
• Binary Classification Using Perceptron
• Perceptrons - Training
• Multiclass Classification using Perceptrons
• Working of a Neuron 

• Inputs and Outputs of a Neural Network
• Parameters and Hyperparameters of Neural Networks
• Activation Functions
• Flow of Information in Neural Networks - Between 2 Layers
• Learning the Dimensions Weight Matrices 

• Feedforward Algorithm
• Vectorized Feedforward Implementation
• Understanding Vectorized Feedforward Implementation
• What does training a Network mean?
• Complexity of the Loss Function
• Comprehension - Training a Neural Network
• Updating the Weights and Biases

• Sigmoid Backpropagation
• Batch in Backpropagation
• Training in Batches
• Regularization
• Batch Normalization 

• Imports and Setups
• Defining Network Variables
• Creating Feed Forward Module
• Creating Back Propagation Module
• Integrating all Modules for Complete Neural Network
• Predictions using the Network Model

• Introduction To CNNs
• Image Processing Basics
• Understanding Mammals Eye Perception
• Understanding Convolutions
• Stride and Padding
• Important Formulas
• Weights of a CNN
• Feature Maps
• Pooling
• Putting the Components Together

• Building CNNs In Keras - Mnist
• Comprehension - Vgg16 Architecture
• Cifar-10 Classification with Python
• Overview of CNN Architectures
• Alexnet and Vggnet
• Googlenet
• Residual Net

• Introduction to Transfer Learning
• Use Cases of Transfer Learning
• Transfer Learning with Pre-Trained CNNs
• Practical Implementation of Transfer Learning
• Transfer Learning in Python
• An analysis of Deep Learning Models

• Introduction to Style Transfer
• Style Loss and the Gram Matrix
• Loss Function
• Style Transfer Notebook
• Object Detection

• Examining the Flowers Dataset
• Data Preprocessing: Shape, Size and Form
• Data Preprocessing: Normalisation
• Data Preprocessing: Augmentation
• Data Preprocessing: Practice Exercise Solutions

• Resnet: Original Architecture and Improvements
• Building the Network
• Ablation Experiments
• Hyperparameter Tuning
• Training and Evaluating the Model

• Examining X-Ray Images
• Cxr Data Preprocessing – Augmentation
• Cxr: Network Building

To equip the candidate with the knowledge of Computer Vision.

• No mandatory prerequisites
• Completed the course on Certified Deep Learning Expert.
• Knowledge of Deep Learning.

• Any individual who is involved in the computer vision domain.
• A fresher who possesses a good amount of knowledge in Computer Vision.

• Wide Recognition.
• Higher Pay.
• Designed in tune with the current industry requirements.

DataMites™ is an authorized institute by the International Association of Business Analytics Certifications (IABAC™) providing global data science certification courses

• DataMites™ is founded by Data Science/Analytics Experts, who had deep roots in the industry in the USA and Europe for decades.

• You benefit from course syllabus of DataMites™ as designed inline with job requirement in the current market

• You can choose from flexible learning options: Traditional classroom, Live Virtual classroom, and Self-learning.

• DataMites™ has a dedicated Placement Assistance Team (PAT), which assists you to find the right data science job

• If you are a worthy candidate, you can get an opportunity to work in global data science consulting projects

• You can treat your DataMites™ relationship manager as your pal. We value your every request.

Deep Learning is a branch of machine learning that utilizes artificial neural networks to imitate the learning capabilities of the human brain. It involves training neural networks with multiple layers to extract complex patterns and achieve precise predictions or classifications.

Deep Learning operates by constructing and training neural networks with multiple layers of interconnected nodes called neurons. Each layer processes and transforms data, passing it to the next layer for further computation. Through backpropagation, the network adjusts connection weights to minimize errors and enhance predictions.

Deep Learning finds applications in diverse industries, including image and speech recognition, natural language processing, autonomous vehicles, healthcare diagnostics, recommendation systems, and fraud detection.

Noteworthy Deep Learning frameworks include TensorFlow, PyTorch, Keras, Caffe, and Theano. These frameworks provide efficient tools and libraries for building, training, and deploying deep neural networks.

Deep Learning is a subset of Machine Learning that concentrates on training deep neural networks with multiple layers. Unlike traditional Machine Learning, Deep Learning models automatically learn hierarchical representations from raw data, reducing the need for manual feature engineering.

The trainers for the Computer Vision Expert  course have experienced Data Scientists, AI and Machine Learning experts who possess good knowledge of the subject matter.

 No. Coding is not required to learn Computer Vision Expert courses.

This course is perfectly aligned to the current industry requirements and gives exposure to all the latest techniques and tools. The course curriculum is designed by specialists in this field and monitored and improved by industry practitioners on a continual basis.

You can enrol for the Computer Vision Expert Course by visiting our website, and doing the payment through Debit/Credit card, Visa. The receipt for the payment done will be sent to your registered E-mail id.

Yes. You will be given a certificate after the completion of the Computer Vision Expert course.

You will receive certification from IABAC® - International Association of Business Analytics Certification.

No, the exam fees are already included in the course fee and you will not be charged extra.

Course fee needs to be paid in one payment as it is required to block your seat for the entire course as well as book the certification exams with IABAC™. In case, if you have any specific constraints, your relation manager at DataMites™ shall assist you with part payment agreements

DataMites™ has a dedicated Placement Assistance Team(PAT), who work with candidates on an individual basis in assisting for the right Data Science job.

You get a 100% refund training fee if the training is not to your satisfaction but the exam fee will not be refunded as we pay to accreditation bodies. If the refund is due to your availability concerns, you may need to talk to the relationship manager and will be sorted out on case to case basis