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All Levels
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26 Weeks
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MIT Certification
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Industry Immersion
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Capstone Projects
Overview
Our Computer Vision Course in Mumbai is designed to provide a strong foundation in advanced AI concepts such as image processing, object detection, and deep learning models. Through hands-on training with real-world datasets, you will learn to build intelligent vision systems and understand how computer vision is transforming industries like healthcare, automotive, security, and automation.
- Junior Computer Vision Engineer
- Computer Vision Engineer
- AI Vision Engineer
- Image Processing Engineer
- Vision Systems Automation Engineer
- Video Analytics Engineer
- AI Research Assistant
- Computer Vision Intern
Targeted Job
Roles
Training and Methodology
Experience structured and practical learning in Computer Vision with our focused training approach -
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Hands-On Training - Work directly with image datasets and solve real-world computer vision problems through practical exercises.
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Industry-Level Projects - Develop real-world applications such as object detection, face recognition, and video analytics systems.
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Expert Mentorship - Learn advanced techniques, optimization strategies, and industry best practices from experienced professionals.
Why Choose This
Course?
Build Job-Ready Skills in Computer Vision
This Computer Vision program is designed to take you from fundamentals to advanced applications with a practical, project-based approach. You will learn how machines interpret images, extract meaningful features, and use deep learning models for tasks like detection, recognition, and classification using real-world datasets and industry methods.
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Career Support & Placement Guidance
Receive complete career support including resume preparation, interview training, and job placement assistance in the AI domain.
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Real-Time Computer Vision Projects
Build practical applications such as object detection, face recognition, image classification, and video analytics systems using real datasets.
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Expert Mentorship & Industry Support
Gain deep insights into computer vision concepts including CNNs, model training, optimization techniques, and best practices from industry experts.
Key Skills You Will Gain
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Develop a solid foundation in computer vision concepts and visual data interpretation.
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Learn advanced techniques like feature detection, segmentation, and pattern recognition in images.
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Implement deep learning models for solving real-world image classification and detection problems.
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Gain expertise in CNN architectures used for modern computer vision applications.
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Create real-time solutions such as object tracking, face detection, and video processing systems.
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Work with industry tools and libraries for building and deploying computer vision models.
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Handle real-world datasets for training models on image and video-based applications.
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Improve model accuracy using performance tuning, validation, and evaluation techniques.
Tools & Technologies Covered in the Computer Vision Course
Course Curriculum Overview
Get a complete breakdown of topics and modules designed to build your expertise in Computer Vision.
Introduction to Machine Learning
- What is Machine Learning
- Applications of Machine Learning
- Supervised Vs Unsupervised Machine Learning
- Regression vs classification
Exploratory Data Analysis (EDA)
- Introduction to MongoDB
- Detecting and removal of outliers
- Feature scaling – : Standardization and normalization
Introduction to Linear Regression
- What is regression?
- What is linear regression?
- Building First ML model for marks prediction
- Simple linear regression
- Multiple Regression
- Polynomial Regression
- Error functions in Regression (MAE, MSE, RMSE)
- Calculating accuracy using R2Score
Introduction to Overfitting and underfitting
- Overfitting Vs underfitting
- Bias-Variance Tradeoff
- Regularization Techniques -: Ridge and Lasso
- Understanding and demonstrating Ridge and lasso regression techniques
- Cross Validation Techniques
Introduction to Logistic Regression
- Sigmoid function
- Understanding parameters of logistic regression
- ROC AUC Curve
- Confusion Matrix -: Precision, Recall, accuracy, f1 Score
Introduction to KNN
- Understanding working of K – Nearest Neighbors
- Advantages and drawbacks of using KNN
- KNN for regression
Introduction to SVM
- Understanding Support Vector Machine
- Hard and soft margin
- Understanding Support Vectors , Hyperplane
- Kernel technique
- SVM for regression
Naive Bayes Classifier
- Understanding Naive Bayes Theorem
- Introduction to text classification
- NLP pipeline
- Vectorization of text data
- Case Study -: Spam mail classification using naive bayes
Decision Tree classifier
- Working of DT
- Gini Index and Entropy
- Pruning techniques
- Advantages and disadvantages of Decision Tree
- Decision Tree for regression
Introduction to Ensemble learning
- What is Bagging?
- Random Forest Classifier
- ADA Boost, XGboost, Gradient Boost
- Unsupervised Machine Learning Algorithm
Project deployment using Flask Framework
- Clustering
- K-means Clustering
- Hierarchical clustering
- Association rules
- PCA (principle component analysis)
Projects & Case Study
- CASE STUDY ON BREAST CANCER DETECTION USING CLASSIFICATION ALGORITHMS
- CASE STUDY ON FRAUD DETECTION USING CLASSIFICATION ALGORITHMS
Ready to create
smart
vision systems?
Step into Computer Vision with expert-led training, real-time projects, and career-focused guidance in Mumbai. Book your free demo and start building AI-powered solutions today!
Book Free Demo
Top Recruiters hiring Computer Vision Professionals
Certification For This
Course
Boost your career in AI with our Computer Vision Course. Gain hands-on experience in building real-world vision models and earn a recognized certification that showcases your expertise in image processing and deep learning.
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Get in touch today
What Our Students Say
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Data Analytics training boosted my skills and confidence, helping me secure placement at Technimont.
Amruta Pal
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As a beginner, I was scared at first, but MIT’s training program and structure made me confident.
Nikita Chaudhary
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I feel highly confident in my skills and would definitely recommend MIT for a Data Analytics Course.
Rushikesh Kale
Frequently Asked Questions
Got queries about our Computer Vision program? Find answers about course structure, practical learning, tools covered, and how this training prepares you for real-world AI roles.
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Is this course suitable for beginners?
Yes, the course is designed for beginners as well as professionals. It starts with the basics and gradually moves to advanced computer vision and deep learning concepts.
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What tools and technologies will I learn?
You will work with popular tools and frameworks used in computer vision, including libraries for image processing, deep learning, and model development.
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Will there be hands-on practice during the course?
Absolutely. The course includes practical sessions, real-time assignments, and projects to help you gain strong hands-on experience.
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What kind of career opportunities can I expect?
After completing the course, you can explore roles such as Computer Vision Engineer, AI Engineer, Image Processing Engineer, and related AI-based positions.
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Do you provide placement assistance?
Yes, we offer placement support including resume building, interview preparation, and guidance to help you secure job opportunities in the AI and tech industry.
