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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 Thane is designed to build strong foundations 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 develop intelligent vision systems and understand how computer vision is transforming industries like healthcare, automotive, and automation.
- Junior Computer Vision Engineer
- Computer Vision Engineer
- AI Vision Engineer
- Image Processing Engineer
- Automation Engineer (Vision Systems)
- Video Analytics Engineer
- AI Research Assistant
- Computer Vision Intern
Targeted Job
Roles
Training and Methodology
Unlock advanced Computer Vision learning with our structured training approach -
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Hands-On Training - Gain practical experience by working with image datasets, real-world computer vision challenges.
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Industry-Level Projects - Build real applications like object detection, face recognition, and video analytics systems.
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Expert Mentorship - Learn optimization techniques and industry best practices from experienced mentors.
Why Choose This
Course?
Build Strong Foundations in Computer Vision
Begin your Computer Vision journey with a structured learning path covering image processing, feature extraction, and deep learning models. This course helps you understand key concepts, train vision models, and apply techniques like object detection, model optimization, and performance tuning using real-world datasets.
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Placement Assistance Program
Get dedicated career assistance including resume building, interview preparation, and job placement guidance.
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Hands-On Computer Vision Projects
Work on real datasets and build applications like object detection, face recognition, and image classification models.
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Expert Mentorship & Guidance
Understand deep learning concepts like neural networks, backpropagation, and optimization with expert-led support.
Skills acquired from Computer Vision Course
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Strong understanding of computer vision fundamentals, image processing, and visual data analysis.
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Understand feature extraction, edge detection, and object recognition techniques in images.
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Apply deep learning models for image classification, detection, and segmentation tasks.
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Work with convolutional neural networks (CNNs) for advanced vision applications.
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Build real-time applications like face recognition, object tracking, and video analytics.
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Use tools and frameworks for training and optimizing computer vision models.
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Work with real-world datasets for image and video-based machine learning tasks.
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Optimize model performance using tuning techniques and evaluation metrics.
Tools & Technologies Covered in the Computer Vision Course
Computer Vision Course Syllabus
Explore the key concepts and modules covered in this structured training program.
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 build
intelligent
AI models?
Step into Computer Vision with expert-led training, hands-on neural network projects, and career-focused guidance in Thane. Book your free demo today!
Book Free Demo
Recruiters looking for Computer Vision Students
Certification For This
Course
Advance your AI career with our Computer Vision Course. Build real-world neural network models and earn a recognized Computer Vision Certification that validates your skills.
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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
Have questions about the Computer Vision Course? Explore key details about image processing, Computer Vision models, hands-on projects, and how this program helps you build strong AI vision skills.
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Do I need prior AI or Machine Learning knowledge?
No prior experience is required. The course starts from basics and gradually builds your understanding of computer vision and Computer Vision concepts step by step.
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How is Computer Vision different from Machine Learning?
Computer Vision focuses on enabling machines to interpret images and videos, while Machine Learning is a broader field that includes learning patterns from different types of data.
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What kind of projects will I work on?
You will work on real-world projects like object detection, face recognition, image classification, and video analysis using modern AI techniques.
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Will I learn how computer vision models actually work?
Yes, you will understand key concepts like image processing, feature extraction, CNNs, training pipelines, and model optimization in a practical way.
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How does this course help in career growth?
You gain hands-on AI vision skills, project experience, and industry-ready knowledge that prepares you for roles in AI, computer vision, and data-driven technologies.
