Master Computer Vision With Industry Experts
Advanced Computer Vision specialization designed for AI professionals, covering state-of-the-art computer vision techniques with hands-on projects and practical implementation, leading to the Global Computer Vision Expert Certification (Advanced Level) issued by IABAC®.
Choose Your Learning Path
Flexible Plans Designed to Fit Your Schedule & Learning Style
Select the learning mode that works best for you — self-paced, live online, or in-person classroom training
Blended Learning
Self Learning + Live Mentoring
- Self Learning + Live Mentoring
- NASSCOM & IBM & AAAI Certification
- 1 Year Access to E-learning
- 25 Capstone & 1 Client Project
- Job Assistance
- 24x7 Learner Assistance
Live Virtual
Instructor Led Live Online
- NASSCOM & IBM & AAAI Certification
- 7-Month | 700 Learning Hours
- 120-Hour Live Online Training
- 25 Capstone & 1 Client Project
- 365 Days Flexi Pass + Cloud Lab
- Placement Assurance
Classroom
In-Person Classroom Training
- NASSCOM & IBM & AAAI Certification
- 7-Month | 700 Learning Hours
- 120-Hour Live Online Training
- 25 Capstone & 1 Client Project
- Cloud Lab Access
- Placement Assurance
Tools & Technologies
Master Industry Tools
Hands-on training on the tools and technologies used by top data teams
worldwide.
Certification
NASSCOM & IBM & AAAI Certification
Flexible Financing Options
Pay in Easy Installments at 0% Interest
We’re dedicated to making our programs accessible. No hidden costs, transparent pricing
EMI Available
ShopSe
No Cost EMI & Scholarships Available
Our Partners
Companies That Hire Our Graduates
200+ leading companies trust Data Sprint graduates to drive their data
initiatives.
Why Data Sprint
Why Thousands Choose Data Sprint
We’re committed to transforming careers with quality education and real-
world experience.
Course Syllabus
Comprehensive Data Science Curriculum
A structured learning path covering foundations to advanced topics across data science, machine learning, and AI.
Module 1: Introduction to Artificial Intelligence (AI)
• 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
Module 2: AI Data Strategy
• Foundation of AI Data
• Data Lake
• Four Stages of Building and Integrating Data Lakes within Technology Architectures
Module 3: AI Ethics, Issues and Concerns
• Issues and Concerns around AI
• AI and Ethical Concerns
• AI and Bias
• AI: Ethics, Bias, and Trust
Module 4: AI Challenges, Usecases and Adoption
• Challenges of AI Implementation
• Pitfalls and Lessons from the Industry
• Usecases from top AI Implementation
• Future with AI
• The Journey for adopting AI successfully
Module 5: Tensorflow Introduction
• Introduction to Tensorflow 2.X
• Tensor + Flow = Tensorflow
• Components and Basis Vectors
• Sequential and Functional APIs
Module 6: Tensorflow Basic Concepts
• Creating a Tensor
• Tensor Rank /Degree
• Shape of a Tensor
• Create Flow for Tensor Operation
• Usability-Related Changes
• Performance-Related Changes
Module 7: Installation and Basic Operations in Tf 2.X
• 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
Module 8: Tf 2.0 Eager Mode
• 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
Module 9: Tensorflow 2.X – Keras
• Keras Package Introduction
• Inbuilt Keras in Tensorflow2.X
• Using Keras Modules for Nn Modelling
Module 10: Structure of Neural Networks
• Neural Networks – Inspiration from the Human Brain
• Introduction to Perceptron
• Binary Classification Using Perceptron
• Perceptrons – Training
• Multiclass Classification using Perceptrons
Module 11: Neural Network - Core Concepts
• 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
Module 12: Feed Forward Algorithm
• 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
Module 13: Backpropagation
• Sigmoid Backpropagation
• Batch in Backpropagation
• Training in Batches
• Regularization
Module 14: Building Neural Network from Scratch using Numpy
• Imports and Setups
• Defining Network Variables
• Creating Feed Forward Module
• Creating Back Propagation Module
• Integrating all Modules for Complete Neural Network
Module 15: Convolutional Neural Networks (CNNs) Introduction
• Introduction To CNNs
• Image Processing Basics
• Understanding Mammals Eye Perception
• Understanding Convolutions
• Stride and Padding
• Important Formulas
• Weights of a CNN
• Feature Maps
• Pooling
Module 16: CNNs with KERAS – Tf 2.X(CNNs) Introduction
• Building CNNs In Keras – Mnist
• Comprehension – Vgg16 Architecture
• Cifar-10 Classification with Python
• Overview of CNN Architectures
• Alexnet and Vggnet
• Googlenet
Module 17: Transfer Learning in CNN
• Introduction to Transfer Learning
• Use Cases of Transfer Learning
• Transfer Learning with Pre-Trained CNNs
• Practical Implementation of Transfer Learning
• Transfer Learning in Python
Module 18: Style Transfer
• Introduction to Style Transfer
• Style Loss and the Gram Matrix
• Loss Function
• Style Transfer Notebook
Module 19: Flowers Dataset with Tf 2.X
• Examining the Flowers Dataset
• Data Preprocessing: Shape, Size and Form
• Data Preprocessing: Normalisation
• Data Preprocessing: Augmentation
Module 20: Resnet Modeling
• Resnet: Original Architecture and Improvements
• Building the Network
• Ablation Experiments
• Hyperparameter Tuning
• Training and Evaluating the Model
Module 21: Examing X-Ray with CNN Model
• Examining X-Ray Images
• Cxr Data Preprocessing – Augmentation
• Cxr: Network Building
Not Sure Which Course Is Right For You?
Book a free one-on-one career counselling session and get a personalized AI & Data career roadmap.