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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 focuses on advanced AI concepts like image processing, object detection, and deep learning models. Gain hands-on experience with real-world datasets, build intelligent vision systems, and understand how computer vision powers modern applications across industries.
- Computer Vision Engineer
- Junior Computer Vision Engineer
- Image Processing Engineer
- Video Analytics Engineer
- AI Vision Engineer
- Automation Engineer (Vision Systems)
- 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 - Work directly on image data, videos, and real-world vision problems.
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Industry-Level Projects - Develop applications like object detection, face recognition, and video analysis.
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Expert Mentorship - Learn optimization techniques and best practices from industry professionals.
Why Choose This
Course?
Build Strong Foundations in Deep Learning
Start your journey into Deep Learning with a structured approach to neural networks, activation functions, and model training. This course is designed to help you understand core concepts, build deep learning models, and apply techniques like backpropagation, optimization, and overfitting prevention using real datasets.
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Placement Assistance Program
Get career support with resume building, interview preparation, and job guidance.
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Hands-On Neural Network Projects
Build and train deep learning models using datasets like MNIST and real-world examples.
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Expert Mentorship
Learn core concepts like backpropagation, optimizers, and overfitting with guided support.
Skills acquired from Deep Learning Course
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Strong understanding of Deep Learning fundamentals and neural network architecture.
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Understand perceptrons, forward propagation, and backpropagation in neural networks.
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Apply activation functions and understand their impact on model performance.
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Use optimizers to improve model accuracy and training efficiency.
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Build and train deep neural networks for classification problems.
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Apply techniques like early stopping and dropout to prevent overfitting.
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Work with real datasets like MNIST for digit and image classification tasks.
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Tune hyperparameters to optimize deep learning model performance.
Tools & Technologies Covered In This Course
The Course Syllabus
The course covers important topics.
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 Deep Learning 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 Machine Learning Students
Certification For This
Course
Advance your AI career with our Deep Learning Course. Build real-world neural network models and earn a recognized Deep Learning Certification that validates your skills.
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Get in touch today
What Our Students Say
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Personal Training from experienced faculty helped me to gain proper knowledge and skills.
Nikita Akhade
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Enrolling at MIT was a game-changer for me. I secured a job placement in just 8 months!
Ameya Rasam
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Joined MIT for Master's on my friend's suggestion, and it was the best decision i ever made.
Pratik Dubal
Frequently Asked Questions
Have questions about Deep Learning? Explore key details about neural networks, training methods, hands-on projects, and how this course helps you build a strong AI foundation.
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Do I need prior Machine Learning knowledge?
Basic understanding of programming is helpful, but not mandatory. The course starts with fundamentals and gradually introduces deep learning concepts step by step.
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What makes Deep Learning different from Machine Learning?
Deep Learning focuses on neural networks that automatically learn patterns from data, especially for complex tasks like image and pattern recognition, unlike traditional machine learning methods.
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What kind of projects will I work on?
You will build neural network models for classification tasks such as digit recognition using MNIST and similar real-world datasets.
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Will I learn how neural networks actually work?
Yes, you will understand core concepts like perceptrons, forward propagation, backpropagation, activation functions, and optimizers in a practical way.
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How does this course help in career growth?
You gain practical deep learning skills, project experience, and guidance that prepares you for roles in AI and advanced machine learning domains.
