Keras is a powerful, high-level Deep Learning framework built on top of TensorFlow that enables developers, data scientists, and AI engineers to quickly build, train, evaluate, and deploy neural networks. With its intuitive API, Keras simplifies the development of machine learning models for computer vision, natural language processing (NLP), time series forecasting, recommendation systems, and generative AI applications.

This instructor-led online training provides comprehensive hands-on experience in Deep Learning fundamentals, TensorFlow & Keras, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Transformers, Transfer Learning, Model Deployment, MLOps fundamentals, and real-world AI projects.

• Basic Python Programming
• Basic Mathematics (Algebra & Statistics)
• Basic Machine Learning Concepts (Recommended)
• NumPy & Pandas Fundamentals
• No prior Deep Learning experience required

Module 1: Introduction to Artificial Intelligence & Deep Learning

• Artificial Intelligence Overview
• Machine Learning vs Deep Learning
• History of Neural Networks
• AI Applications
• Industry Use Cases
• Deep Learning Workflow
• TensorFlow & Keras Ecosystem

Module 2: Python for Deep Learning

• Python Refresher
• NumPy
• Pandas
• Matplotlib
• Data Preprocessing
• Feature Engineering
• Data Visualization

Module 3: TensorFlow Fundamentals

• TensorFlow Architecture
• Installation
• Tensors
• Variables
• Operations
• TensorFlow Execution
• GPU Support
• TensorBoard Introduction

Module 4: Introduction to Keras

• Keras Architecture
• Sequential API
• Functional API
• Model Subclassing
• Layers
• Activation Functions
• Optimizers
• Loss Functions
• Metrics

Module 5: Building Artificial Neural Networks (ANN)

• Perceptrons
• Feedforward Neural Networks
• Hidden Layers
• Backpropagation
• Gradient Descent
• Batch Training
• Mini-Batch Training
• Hyperparameter Tuning

Module 6: Data Preprocessing

• Data Cleaning
• Missing Values
• Normalization
• Standardization
• One-Hot Encoding
• Label Encoding
• Train/Test Split
• Data Pipelines

Module 7: Model Training & Evaluation

• Model Compilation
• Model Fitting
• Validation
• Cross Validation
• Performance Metrics
• Confusion Matrix
• Precision
• Recall
• F1 Score
• ROC-AUC

Module 8: Regularization Techniques

• Dropout
• Early Stopping
• Batch Normalization
• Weight Regularization
• Learning Rate Scheduling
• Callbacks

Module 9: Convolutional Neural Networks (CNN)

• CNN Fundamentals
• Image Classification
• Feature Maps
• Pooling
• CNN Layers
• Image Augmentation
• Image Preprocessing
• CNN Architectures

Module 10: Transfer Learning

• Pretrained Models
• VGG16
• VGG19
• ResNet
• MobileNet
• EfficientNet
• Fine Tuning
• Feature Extraction

Module 11: Image Processing Projects

• Object Classification
• Medical Image Analysis
• Face Recognition
• OCR Introduction
• Image Segmentation Basics
• Industrial Defect Detection

Module 12: Recurrent Neural Networks (RNN)

• Sequential Data
• RNN Architecture
• Time Series Data
• Vanishing Gradient Problem
• Practical Examples

Module 13: Long Short-Term Memory (LSTM)

• LSTM Cells
• Sequence Prediction
• Stock Price Prediction
• Weather Forecasting
• Time Series Forecasting
• Sentiment Analysis

Module 14: Gated Recurrent Units (GRU)

• GRU Fundamentals
• Sequence Modeling
• Forecasting
• NLP Applications
• Performance Optimization

Module 15: Natural Language Processing (NLP)

• Text Processing
• Tokenization
• Word Embeddings
• Text Vectorization
• Sentiment Analysis
• Text Classification
• Sequence Modeling

Module 16: Transformers with Keras

• Transformer Architecture
• Attention Mechanism
• Positional Encoding
• Encoder-Decoder Models
• Text Generation
• Fine-Tuning Pretrained Models

Module 17: Autoencoders

• Encoder & Decoder Networks
• Anomaly Detection
• Dimensionality Reduction
• Image Compression
• Denoising Autoencoders

Module 18: Generative AI Fundamentals

• GANs (Generative Adversarial Networks)
• Variational Autoencoders (VAE)
• Image Generation
• Synthetic Data
• AI Content Generation
• Responsible AI Overview

Module 19: Hyperparameter Optimization

• Grid Search
• Random Search
• KerasTuner
• Bayesian Optimization
• Model Selection

Module 20: TensorBoard

• Monitoring Training
• Performance Visualization
• Graph Analysis
• Debugging Models
• Profiling

Module 21: Model Deployment

• Saving Models
• Loading Models
• TensorFlow SavedModel
• TensorFlow Lite Overview
• TensorFlow Serving
• REST API Deployment
• Docker Basics

Module 22: MLOps Fundamentals

• Model Versioning
• Experiment Tracking
• CI/CD for ML
• Deployment Pipelines
• Monitoring Models
• Retraining Strategies

Module 23: Explainable AI (XAI)

• Model Interpretability
• SHAP Overview
• LIME Overview
• Feature Importance
• Bias Detection
• Fairness Concepts

Module 24: Performance Optimization

• GPU Acceleration
• Mixed Precision Training
• Distributed Training
• Data Pipeline Optimization
• Efficient Batch Processing

Module 25: Integration with Cloud Platforms

• Google Cloud AI Overview
• AWS AI Services Overview
• Microsoft Azure AI Overview
• TensorFlow on Cloud
• Model Deployment Options

Module 26: Real-Time Enterprise Projects

Participants will develop multiple end-to-end AI solutions, including:

• Customer Churn Prediction
• Loan Approval Prediction
• Image Classification System
• Face Mask Detection
• Medical Image Classification
• Sentiment Analysis
• Spam Email Detection
• Product Recommendation Engine
• Time Series Sales Forecasting
• Predictive Maintenance

Module 27: Industry Use Cases

• Healthcare
• Banking & Finance
• Manufacturing
• Retail & E-commerce
• Insurance
• Telecommunications
• Automotive
• Education
• Cybersecurity
• Agriculture

Module 28: Keras Certification Preparation

Certification Topics

• TensorFlow Fundamentals
• Keras APIs
• Neural Networks
• CNN
• RNN
• LSTM
• Transfer Learning
• NLP
• Deployment
• Model Optimization

Module 29: Interview Preparation

• Keras Interview Questions
• TensorFlow Coding Exercises
• Deep Learning Scenarios
• CNN & Computer Vision Questions
• NLP & LSTM Scenarios
• Model Optimization Questions
• Python Coding Challenges
• Resume Preparation
• Mock Interviews

Module 30: Resume & Placement Assistance

• Resume Building
• LinkedIn Profile Optimization
• GitHub Portfolio Development
• AI Project Documentation
• Career Guidance
• Mock Interviews
• Placement Assistance

Workday HCM Online Training – Course Content :

Module 1: Introduction to Workday HCM

• Overview of Workday and its Benefits

• Workday Architecture & Core Components

• Navigating the Workday Interface

• Understanding Workday Security & Permissions

Module 2: Staffing Models & Business Processes

• Introduction to Staffing Models: Position & Job-Based

• Configuring Business Processes in Workday

• Role-Based Security and Approvals in Business Processes

Module 3: Core HCM Setup

• Job Profiles & Positions

• Location Hierarchies and Organizational Structure

• Compensation Grades & Packages Setup

Module 4: Time Tracking & Absence Management

• Configuring Time Tracking and Absence Plans

• Creating and Managing Time Off Requests

• Time Tracking Integration with Payroll

Module 5: Payroll & Benefits

• Payroll Inputs, Processing, and Calculations

• Configuring Benefits Enrollment and Rules

• Integrating Payroll and Benefits with HCM

• Running Payroll in Workday

Module 6: Talent & Performance Management

• Setting Up Goal Management and Performance Reviews

• Implementing Talent Calibration and Succession Planning

• Managing Development and Career Plans

Module 7: Reporting & Analytics

• Workday Reporting Overview

• Creating Custom Reports and Dashboards

• Using Workday Analytics for Decision-Making

• Integrating Workday Reports with External Systems

Module 8: Workday Security & Integration

• Configuring Security Policies and Role Assignments

• Workday Integration Concepts

• Using Enterprise Interface Builder (EIB) for Data Integration

• Workday Web Services for System Connectivity

Module 9: Workday HCM Certification Preparation

• Mock Exams and Practice Questions

• Resume Preparation & Interview Tips

• Real-Time Case Studies and Best Practices

• Workday HCM Certification Guidance

Module 10: Real-Time Projects and Case Studies

• Implementation of Workday HCM in Real Business Scenarios

• Solving Complex HR Challenges with Workday

• Capstone Project: End-to-End Workday HCM Implementation