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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
OOur Deep Learning Course in Thane is designed to help you master advanced AI concepts such as neural networks, deep learning architectures, and model optimization techniques. Gain practical experience by working with real-world datasets, develop intelligent models, and understand how deep learning is transforming industries like healthcare, finance, and automation.
- Deep Learning Engineer
- AI & Machine Learning Engineer
- Computer Vision Engineer
- Natural Language Processing (NLP) Engineer
- Neural Network Specialist
- AI Research Assistant
- Deep Learning Analyst
- AI Intern / Deep Learning Intern
Targeted Job
Roles
Training and Methodology
Master advanced AI concepts with our structured Deep Learning Course in Thane -
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Practical Model Development - Learn by building and training deep learning models using real-world datasets.
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Real-World AI Projects - Work on industry-relevant applications like computer vision and data-driven solutions.
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Expert-Led Guidance - Learn from experienced mentors and gain insights into model optimization and performance tuning.
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Placement-Focused Training - Prepare for job roles with interview support, resume building, and career guidance.
Why Choose This
Course?
Master Deep Learning with Practical Expertise
Gain a strong foundation in Deep Learning through a well-structured training approach. Learn key concepts such as neural networks, activation functions, and model training techniques. This course helps you build and optimize deep learning models while applying concepts like backpropagation, regularization, and performance tuning using real-world datasets.
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Placement Assistance Program
Get career support with resume building, interview prep, and job guidance.
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Hands-On Deep Learning Projects
Build and train models using datasets like MNIST and real-world examples.
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Expert Mentorship
Learn core concepts like backpropagation, optimizers, and overfitting with support.
Skills acquired from Deep Learning Course in Thane
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Strong understanding of deep learning basics and neural network architecture.
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Understand perceptrons, forward pass, and backpropagation in neural networks.
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Apply activation functions and analyze their impact on model performance.
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Use optimizers to boost model accuracy and training efficiency.
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Build and train deep neural networks for classification tasks.
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Apply techniques like early stopping and dropout to avoid overfitting.
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Work with 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
Deep Learning Course Syllabus
Explore a well-structured curriculum covering essential concepts and practical applications of deep learning.
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 start
building
Deep Learning Models?
Learn Deep Learning in Thane with expert guidance, hands-on neural network projects, and career-focused training. Gain practical skills and start building intelligent AI systems with confidence. Book your free demo today!
Enroll for Free Demo
Top Recruiters Hiring Machine Learning Talent
Certification in Deep Learning Course
Enhance your AI career with our Deep Learning Course in Thane. Gain hands-on experience in building neural network models and earn a recognized certification that validates your practical skills and industry readiness.
Get Certified Now
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
Get clarity on Deep Learning concepts, training process, projects, and career outcomes. These FAQs will help you understand how the course builds your AI foundation step by step.
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Do I need prior Machine Learning knowledge?
No prior Machine Learning experience is required. The course begins with basics and gradually builds your understanding of deep learning concepts in a structured way.
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What makes Deep Learning different from Machine Learning?
Deep Learning uses multi-layer neural networks that automatically extract patterns from large datasets, making it more powerful for complex tasks like image, speech, and text recognition.
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What kind of projects will I work on?
You will work on real-world AI projects such as image classification, digit recognition, and neural network-based prediction models using actual datasets.
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Will I learn how neural networks actually work?
Yes, you will gain a clear understanding of how neural networks function, including forward propagation, backpropagation, activation functions, and optimization techniques.
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How does this course help in career growth?
This course builds strong practical skills in deep learning, provides hands-on project experience, and prepares you for roles in AI, machine learning, and data-driven industries.
