National Level FDP on Advanced Deep Learning, Ensemble Learning & Research-Oriented AI Systems
Type: FDP
National Level FDP
E-Certificate Included
MEVI TECHNOLOGIES LLP
National Level Faculty Development Program
Advanced Deep Learning, Ensemble Learning & Research-Oriented AI Systems
Intensive Research-Focused Faculty Development Program on Deep Learning, Ensemble Learning, Hybrid AI Systems & Modern Generative AI Technologies
96+
Advanced AI Topics
42+
Hands-on Activities
6
Research Modules
Program Vision
Artificial Intelligence is rapidly transforming research, innovation, industry, and academia. Modern AI systems now combine Deep Learning, Ensemble Learning, Hybrid Architectures, and Generative Models to solve complex real-world challenges.
This Faculty Development Program is designed to empower faculty members, researchers, PhD scholars, and industry professionals with advanced AI methodologies, research-driven model development strategies, optimization techniques, and emerging intelligent systems.
Program Details
Dates
15th June 2026 – 21st June 2026
19th June - Holiday - Assignment Day
Mode
Online Live Sessions
Timings
7.00 PM to 8.30 PM
Registration Fee
₹350 Only
Certification
National Level E-Certificate
Audience
Faculty, Researchers, PhD Scholars & Industry Professionals
FDP Highlights
Deep Learning
ANN, CNN, Autoencoders, VAE, GANs and Diffusion Models.
Ensemble Learning
XGBoost, LightGBM, CatBoost, Voting & Stacking Ensembles.
Generative AI
Foundation Models, Autoencoders, GANs & Modern AI Systems.
Research Focus
Research Methodologies, Publication Insights & AI Case Studies.
Research Focus Highlights
- Advanced AI Research Methodologies
- Hybrid AI System Development
- Deep Learning Optimization Techniques
- Generative AI Research Trends
- Research Publication Opportunities
- Research-Oriented Case Studies
- Modern Foundation Models & LLMs
Day 1 – AI Evolution, Python Libraries & Data Analytics Foundations
Core Concepts
- Evolution of Artificial Intelligence
- AI, Machine Learning, Deep Learning & Generative AI
- Current AI Research Trends
- Python Ecosystem for AI Research
- Jupyter Notebook & Google Colab
- NumPy Arrays & Matrix Operations
- Vectorization & Broadcasting
- Data Analytics using Pandas
- Data Cleaning Techniques
Analytics & Machine Learning
- Missing Value Handling
- Feature Engineering Techniques
- Exploratory Data Analysis (EDA)
- Matplotlib Visualization
- Statistical Visualization using Seaborn
- Image Processing using Pillow (PIL)
- Machine Learning Fundamentals
- Linear Regression Implementation
Hands-on Activities
Google Colab Environment Setup
NumPy Array Operations
Data Cleaning using Pandas
Exploratory Data Analysis (EDA)
Seaborn Statistical Visualization
Image Processing using Pillow
Linear Regression Model Development
Research Outcome & Learning Focus
Participants will gain a strong understanding of modern AI evolution, research-driven data analytics, feature engineering methodologies, and machine learning workflows used in academic research and industrial applications.
AI Foundations
Data Analytics
Research Skills
Day 2 – Ensemble Learning Foundations
Ensemble Concepts
- Introduction to Ensemble Learning
- Bias Variance Tradeoff
- Ensemble Learning Architecture
- Bootstrap Sampling
- Bagging Techniques
- Random Forest Architecture
- Feature Importance Analysis
- Extra Trees Classifier
Boosting & Optimization
- Sequential Learning Concepts
- Weak Learners & Strong Learners
- Boosting Fundamentals
- AdaBoost Algorithm
- Gradient Boosting
- Ensemble Model Optimization
- Research Applications
- Performance Comparison Methods
Hands-on Activities
- Random Forest Model Development
- Feature Importance Visualization
- Extra Trees Implementation
- AdaBoost Classifier Development
- Gradient Boosting Implementation
- Ensemble Performance Comparison
- Hyperparameter Tuning
Day 3 – Advanced Ensemble Models & Hybrid AI Systems
Advanced Ensemble Frameworks
- XGBoost Architecture
- Regularization in XGBoost
- Tree Pruning Techniques
- Hyperparameter Optimization
- LightGBM Framework
- Histogram-Based Learning
- CatBoost Architecture
- Handling Categorical Features
Hybrid AI Systems
- Hard Voting Ensembles
- Soft Voting Ensembles
- Stacking Ensembles
- Blending Techniques
- Meta Learners
- Hybrid AI Architectures
- Machine Learning + Deep Learning Integration
- Research Case Studies
Hands-on Activities
- XGBoost Model Development
- LightGBM Implementation
- CatBoost Model Training
- Voting Classifier Development
- Stacking Ensemble Creation
- Blending Ensemble Demonstration
- Hybrid AI Workflow Demonstration
Research Focus Highlights
- Research-Oriented Ensemble Optimization
- Publication-Ready Hybrid AI Frameworks
- XGBoost, LightGBM & CatBoost Applications
- Model Fusion & Meta Learning
- Advanced AI Architectures
- State-of-the-Art Ensemble Systems
Day 4 – Deep Learning Foundations, ANN & Evaluation Metrics
Deep Learning Foundations
- Introduction to Deep Learning
- Biological vs Artificial Neurons
- Artificial Neural Networks
- Input Layer
- Hidden Layer
- Output Layer
- Weights and Biases
- Learning Rate Concepts
Training & Evaluation
- Epochs & Batch Size
- Hyperparameters
- Sigmoid Function
- Tanh Function
- ReLU & Leaky ReLU
- Softmax Function
- Forward Propagation
- Backpropagation
Hands-on Activities
- ANN Model Development using Keras
- Binary Classification using ANN
- Multi-Class Classification
- Hyperparameter Tuning
- Activation Function Comparison
- Confusion Matrix Analysis
- Precision, Recall & F1 Score Evaluation
Day 5 – CNN Architecture, Optimization & Training Strategies
CNN Architecture
- Convolutional Neural Networks
- Convolution Operations
- Kernels and Filters
- Feature Maps
- Pooling Layers
- Flatten Layer
- Fully Connected Layers
- Transfer Learning Concepts
Optimization Strategies
- Gradient Descent
- Binary Cross Entropy
- Categorical Cross Entropy
- SGD Optimizer
- Adam Optimizer
- RMSProp Optimizer
- Learning Rate Scheduling
- Early Stopping Techniques
Hands-on Activities
- CNN Model Development
- Image Dataset Preparation
- CNN Image Classification
- Optimizer Comparison
- Learning Rate Scheduler Implementation
- Early Stopping Demonstration
- Transfer Learning Implementation
Day 6 – Research-Oriented AI Systems & Generative AI
Generative AI Concepts
- Autoencoders
- Encoder–Decoder Architecture
- Latent Space Representation
- Variational Autoencoders
- KL Divergence
- Probabilistic Deep Learning
- Foundation Models
- Large Language Models
Advanced AI Systems
- Generative Adversarial Networks
- Generator Network
- Discriminator Network
- Adversarial Training
- Diffusion Models
- Stable Diffusion Concepts
- Self-Supervised Learning
- CNN + XGBoost Frameworks
Hands-on Activities
- Autoencoder Implementation
- VAE Latent Space Visualization
- GAN-Based Image Generation
- Generator & Discriminator Training
- Diffusion Model Demonstration
- CNN + XGBoost Hybrid Model
- CNN + Random Forest Hybrid Model
FDP Outcomes
Technical Outcomes
- Advanced Deep Learning Expertise
- Research-Oriented AI Development
- Ensemble Learning Implementation
- Hybrid AI System Design
- Generative AI Understanding
- Model Optimization Techniques
Research Outcomes
- Research Methodology Insights
- Publication-Oriented Knowledge
- Case Study Analysis
- Modern AI Architectures
- Academic & Industry Applications
- Research Proposal Development
National Level E-Certificate
E-Certificate will be provided to all eligible participants upon successful completion of the FDP.
Faculty Members • Researchers • PhD Scholars • PG Students • Industry Professionals
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