Long Term Certification Course in Scalable Agentic AI
Master the Future of Artificial Intelligence
Transform Your Career with Next-Generation AI Skills
Join the elite ranks of AI professionals who are shaping the future of technology
🚀 Course Overview
Our comprehensive 4-semester program is designed to take you from data analytics fundamentals to advanced agentic AI applications. You'll master the complete AI/ML pipeline, from data preprocessing to building sophisticated AI agents that can solve real-world problems.
Industry-Ready Skills
Master cutting-edge technologies used by top tech companies
Career Advancement
Transition to high-paying AI/ML roles with confidence
Expert Mentorship
Learn from industry veterans and AI researchers
Certification
Earn recognized certification in Scalable Agentic AI
📅 Program Structure: 4 Semesters | 18 Months
Each semester focuses on specific competencies, building upon previous knowledge
🎓 Learning Journey - 4 Progressive Parts
Part 1: Data Analytics Foundation
Master Python, SQL, data visualization, and big data processing with PySpark and Power BI
Part 2: Machine Learning Mastery
Comprehensive ML algorithms, cloud deployment, and Azure ML platform expertise
Part 3: Deep Learning & AI
Neural networks, computer vision, GANs, NLP, and introduction to Large Language Models
Part 4: Agentic AI & LLMs
Advanced LLMs, reinforcement learning, LangChain, and building autonomous AI applications
Career Outcomes & Placement Excellence
🎯 Master Technology & Land Your Dream Job
🏆 Program Outcomes
Upon completion of all 4 parts, you'll be a certified expert in Scalable Agentic AI with the skills to:
- Build end-to-end AI solutions from data collection to deployment
- Develop sophisticated AI agents using LangChain and advanced frameworks
- Deploy scalable ML models on cloud platforms (Azure, AWS)
- Create computer vision applications with state-of-the-art models
- Design and implement Large Language Model applications
- Build autonomous AI systems for real-world applications
95%
Placement Success Rate
₹8-25L
Average Salary Range
200+
Hiring Partners
🚀 Comprehensive Placement Support
📋 Interview Preparation
- Mock technical interviews
- System design workshops
- Behavioral interview training
- Resume optimization
🎯 Placement Training
- Industry-specific skill development
- Portfolio building guidance
- LinkedIn profile optimization
- Networking strategies
🏢 Our Hiring Partners
💡 Career Opportunities
Technical Roles:
- AI/ML Engineer
- Data Scientist
- LLM Engineer
- AI Research Scientist
Specialized Positions:
- AI Solutions Architect
- Computer Vision Engineer
- NLP Engineer
- AI Product Manager
🎓 Certification & Recognition
Receive industry-recognized certification in Scalable Agentic AI • Portfolio of 12+ real-world projects • LinkedIn skill endorsements • Industry mentor recommendations
📊 PART 1: DATA ANALYTICS FOUNDATION
Semester 1 Foundation: Build a rock-solid foundation in data analytics, the cornerstone of all AI/ML applications. Master the essential tools and techniques used by data professionals worldwide. Learn to extract meaningful insights from raw data, create compelling visualizations, and work with big data technologies that power modern enterprises.
📋 Comprehensive Syllabus
🐍 Python for Data Science
• Python fundamentals and advanced concepts
• NumPy for numerical computing
• Pandas for data manipulation and analysis
• Matplotlib & Seaborn for data visualization
• Jupyter notebooks and development environment
• File handling and data input/output operations
• NumPy for numerical computing
• Pandas for data manipulation and analysis
• Matplotlib & Seaborn for data visualization
• Jupyter notebooks and development environment
• File handling and data input/output operations
🔍 Exploratory Data Analysis (EDA)
• Statistical analysis and descriptive statistics
• Data distribution analysis and hypothesis testing
• Correlation analysis and feature relationships
• Data profiling and quality assessment
• Advanced visualization techniques
• Outlier detection and treatment strategies
• Data distribution analysis and hypothesis testing
• Correlation analysis and feature relationships
• Data profiling and quality assessment
• Advanced visualization techniques
• Outlier detection and treatment strategies
🧹 Data Cleaning & Preprocessing
• Missing data handling and imputation techniques
• Data type conversion and standardization
• Duplicate detection and removal
• Data validation and quality checks
• Feature engineering and transformation
• Data normalization and scaling methods
• Data type conversion and standardization
• Duplicate detection and removal
• Data validation and quality checks
• Feature engineering and transformation
• Data normalization and scaling methods
⚡ PySpark for Big Data
• Apache Spark fundamentals and architecture
• PySpark DataFrame operations
• Spark SQL for large-scale data processing
• RDD (Resilient Distributed Datasets) operations
• Spark MLlib for distributed machine learning
• Performance optimization and cluster management
• PySpark DataFrame operations
• Spark SQL for large-scale data processing
• RDD (Resilient Distributed Datasets) operations
• Spark MLlib for distributed machine learning
• Performance optimization and cluster management
🗄️ SQL & Database Management
• Advanced SQL queries and joins
• Window functions and common table expressions
• Database design and normalization
• Stored procedures and functions
• NoSQL databases (MongoDB, Cassandra)
• Data warehousing concepts and ETL processes
• Window functions and common table expressions
• Database design and normalization
• Stored procedures and functions
• NoSQL databases (MongoDB, Cassandra)
• Data warehousing concepts and ETL processes
📊 Power BI & Business Intelligence
• Power BI Desktop and service architecture
• Data modeling and DAX (Data Analysis Expressions)
• Interactive dashboard creation
• Advanced visualizations and custom visuals
• Power Query for data transformation
• Report publishing and sharing strategies
• Data modeling and DAX (Data Analysis Expressions)
• Interactive dashboard creation
• Advanced visualizations and custom visuals
• Power Query for data transformation
• Report publishing and sharing strategies
🛠️ Hands-On Projects
Project 1: E-commerce Sales Analysis
• Complete EDA pipeline with Python
• Interactive Power BI dashboard
• Business insights and recommendations
• Complete EDA pipeline with Python
• Interactive Power BI dashboard
• Business insights and recommendations
Project 2: Big Data Processing Pipeline
• PySpark for large dataset processing
• Real-time data streaming
• Performance optimization techniques
• PySpark for large dataset processing
• Real-time data streaming
• Performance optimization techniques
Project 3: Customer Behavior Analytics
• Advanced SQL queries and analysis
• Customer segmentation insights
• Predictive analytics foundation
• Advanced SQL queries and analysis
• Customer segmentation insights
• Predictive analytics foundation
Project 4: Financial Data Dashboard
• Real-time financial metrics
• Multi-source data integration
• Executive-level reporting
• Real-time financial metrics
• Multi-source data integration
• Executive-level reporting
🤖 PART 2: MACHINE LEARNING FOR DATA SCIENCE
Semester 2 Advanced Skills: Dive deep into machine learning algorithms and methodologies. Master both theoretical concepts and practical implementation across all major ML paradigms. Learn to build, deploy, and scale ML models using cloud platforms, with special focus on Microsoft Azure ecosystem. Develop expertise in model selection, hyperparameter tuning, and production-ready ML systems.
📋 Comprehensive Syllabus
📈 Regression Algorithms
• Linear and Multiple Regression
• Polynomial Regression and Ridge/Lasso Regression
• Logistic Regression for classification
• Support Vector Regression (SVR)
• Decision Tree and Random Forest Regression
• Gradient Boosting Regression (XGBoost, LightGBM)
• Polynomial Regression and Ridge/Lasso Regression
• Logistic Regression for classification
• Support Vector Regression (SVR)
• Decision Tree and Random Forest Regression
• Gradient Boosting Regression (XGBoost, LightGBM)
🎯 Classification Algorithms
• k-Nearest Neighbors (KNN)
• Decision Trees and Random Forest
• Support Vector Machines (SVM)
• Naive Bayes classification
• Ensemble methods (Bagging, Boosting)
• Multi-class and multi-label classification
• Decision Trees and Random Forest
• Support Vector Machines (SVM)
• Naive Bayes classification
• Ensemble methods (Bagging, Boosting)
• Multi-class and multi-label classification
🔍 Unsupervised Learning
• K-Means and Hierarchical Clustering
• DBSCAN and density-based clustering
• Principal Component Analysis (PCA)
• t-SNE and UMAP for dimensionality reduction
• Association Rule Mining (Apriori, FP-Growth)
• Anomaly detection techniques
• DBSCAN and density-based clustering
• Principal Component Analysis (PCA)
• t-SNE and UMAP for dimensionality reduction
• Association Rule Mining (Apriori, FP-Growth)
• Anomaly detection techniques
🚀 Advanced Machine Learning
• Feature selection and engineering
• Cross-validation and model selection
• Hyperparameter tuning (Grid Search, Random Search)
• Model interpretability (SHAP, LIME)
• Ensemble learning and stacking
• Time series forecasting (ARIMA, Prophet)
• Cross-validation and model selection
• Hyperparameter tuning (Grid Search, Random Search)
• Model interpretability (SHAP, LIME)
• Ensemble learning and stacking
• Time series forecasting (ARIMA, Prophet)
☁️ Machine Learning on Cloud
• Cloud computing fundamentals
• Docker containerization for ML
• Model deployment strategies
• MLOps and CI/CD pipelines
• Monitoring and model performance tracking
• Scalable ML infrastructure design
• Docker containerization for ML
• Model deployment strategies
• MLOps and CI/CD pipelines
• Monitoring and model performance tracking
• Scalable ML infrastructure design
🔷 Azure Machine Learning
• Azure ML Studio and workspace setup
• Azure ML pipelines and experiments
• Automated Machine Learning (AutoML)
• Model deployment and endpoints
• Azure ML SDK and CLI
• Integration with Azure services (Data Factory, Synapse)
• Azure ML pipelines and experiments
• Automated Machine Learning (AutoML)
• Model deployment and endpoints
• Azure ML SDK and CLI
• Integration with Azure services (Data Factory, Synapse)
🛠️ Hands-On Projects
Project 1: Customer Churn Prediction
• Complete ML pipeline development
• Model comparison and selection
• Business impact analysis
• Complete ML pipeline development
• Model comparison and selection
• Business impact analysis
Project 2: Recommendation System
• Collaborative and content-based filtering
• Hybrid recommendation models
• Real-time recommendation API
• Collaborative and content-based filtering
• Hybrid recommendation models
• Real-time recommendation API
Project 3: Stock Price Prediction
• Time series analysis and forecasting
• Feature engineering from financial data
• Model deployment on Azure
• Time series analysis and forecasting
• Feature engineering from financial data
• Model deployment on Azure
Project 4: Fraud Detection System
• Anomaly detection implementation
• Real-time fraud scoring
• Model interpretability and explainability
• Anomaly detection implementation
• Real-time fraud scoring
• Model interpretability and explainability
🧠 PART 3: DEEP LEARNING & ADVANCED AI
Semester 3 Deep Learning Mastery: Enter the world of neural networks and deep learning. Master computer vision, natural language processing, and generative AI models. Build sophisticated AI applications using state-of-the-art architectures like CNNs, RNNs, and Transformers. Develop expertise in GANs, object detection, and language models while preparing for advanced AI interviews.
📋 Comprehensive Syllabus
🧠 Artificial Neural Networks (ANN)
• Perceptron and Multi-layer Perceptron
• Backpropagation algorithm and optimization
• Activation functions and their applications
• Regularization techniques (Dropout, Batch Normalization)
• TensorFlow and PyTorch frameworks
• Neural network architectures and design patterns
• Backpropagation algorithm and optimization
• Activation functions and their applications
• Regularization techniques (Dropout, Batch Normalization)
• TensorFlow and PyTorch frameworks
• Neural network architectures and design patterns
👁️ Computer Vision with OpenCV
• Image processing fundamentals
• Feature detection and matching
• Object detection and tracking
• Image segmentation techniques
• Video processing and analysis
• Real-time computer vision applications
• Feature detection and matching
• Object detection and tracking
• Image segmentation techniques
• Video processing and analysis
• Real-time computer vision applications
🖼️ Convolutional Neural Networks (CNN)
• CNN architecture and convolution operations
• Pooling layers and feature maps
• Popular CNN architectures (LeNet, AlexNet, VGG, ResNet)
• Transfer learning and pre-trained models
• Image classification and object detection
• Advanced architectures (Inception, MobileNet, EfficientNet)
• Pooling layers and feature maps
• Popular CNN architectures (LeNet, AlexNet, VGG, ResNet)
• Transfer learning and pre-trained models
• Image classification and object detection
• Advanced architectures (Inception, MobileNet, EfficientNet)
🔄 Recurrent Neural Networks (RNN)
• RNN fundamentals and sequence modeling
• LSTM and GRU architectures
• Bidirectional RNNs and attention mechanisms
• Sequence-to-sequence models
• Time series forecasting with RNNs
• Text generation and language modeling
• LSTM and GRU architectures
• Bidirectional RNNs and attention mechanisms
• Sequence-to-sequence models
• Time series forecasting with RNNs
• Text generation and language modeling
🎨 Generative Adversarial Networks (GAN)
• GAN fundamentals and training dynamics
• DCGAN, StyleGAN, and CycleGAN
• Conditional GANs and controlled generation
• Image-to-image translation
• GAN applications in art and design
• Stable diffusion and modern generative models
• DCGAN, StyleGAN, and CycleGAN
• Conditional GANs and controlled generation
• Image-to-image translation
• GAN applications in art and design
• Stable diffusion and modern generative models
🗣️ Natural Language Processing (NLP)
• Text preprocessing and tokenization
• Word embeddings (Word2Vec, GloVe)
• Named Entity Recognition (NER)
• Sentiment analysis and text classification
• Topic modeling and document similarity
• Introduction to Transformer architecture
• Word embeddings (Word2Vec, GloVe)
• Named Entity Recognition (NER)
• Sentiment analysis and text classification
• Topic modeling and document similarity
• Introduction to Transformer architecture
🛠️ Hands-On Projects
Project 1: Image Classification System
• CNN model development and training
• Transfer learning implementation
• Web application deployment
• CNN model development and training
• Transfer learning implementation
• Web application deployment
Project 2: Object Detection Application
• YOLO and R-CNN implementation
• Real-time object detection
• Mobile app integration
• YOLO and R-CNN implementation
• Real-time object detection
• Mobile app integration
Project 3: Generative Art Platform
• GAN model training and deployment
• Style transfer applications
• Interactive web interface
• GAN model training and deployment
• Style transfer applications
• Interactive web interface
Project 4: Sentiment Analysis Tool
• NLP pipeline development
• Social media data processing
• Real-time sentiment monitoring
• NLP pipeline development
• Social media data processing
• Real-time sentiment monitoring
🚀 PART 4: AGENTIC AI & LARGE LANGUAGE MODELS
Semester 4 Cutting-Edge AI: Master the most advanced AI technologies shaping the future. Deep dive into Large Language Models, build autonomous AI agents, and create sophisticated AI applications. Learn reinforcement learning, advanced prompt engineering, and how to build production-ready AI systems. This is where you become an AI expert ready to tackle any challenge.
📋 Comprehensive Syllabus
🎯 Reinforcement Learning
• Markov Decision Processes (MDP)
• Q-Learning and Deep Q-Networks (DQN)
• Policy Gradient Methods
• Actor-Critic algorithms
• Multi-agent reinforcement learning
• Applications in robotics and game AI
• Q-Learning and Deep Q-Networks (DQN)
• Policy Gradient Methods
• Actor-Critic algorithms
• Multi-agent reinforcement learning
• Applications in robotics and game AI
🔗 LangChain Framework
• LangChain architecture and components
• Chains, agents, and memory systems
• Vector databases and retrieval systems
• Custom tool creation and integration
• Multi-modal AI applications
• Production deployment strategies
• Chains, agents, and memory systems
• Vector databases and retrieval systems
• Custom tool creation and integration
• Multi-modal AI applications
• Production deployment strategies
🤖 Large Language Models (LLM)
• Transformer architecture deep dive
• Pre-training and fine-tuning strategies
• GPT, BERT, and T5 model families
• Prompt engineering and optimization
• Model quantization and optimization
• Custom LLM development and training
• Pre-training and fine-tuning strategies
• GPT, BERT, and T5 model families
• Prompt engineering and optimization
• Model quantization and optimization
• Custom LLM development and training
🧠 Advanced Prompt Engineering
• Zero-shot and few-shot learning
• Chain-of-thought prompting
• Prompt templates and optimization
• Constitutional AI and safety measures
• Multi-turn conversation design
• Prompt injection prevention
• Chain-of-thought prompting
• Prompt templates and optimization
• Constitutional AI and safety measures
• Multi-turn conversation design
• Prompt injection prevention
🔧 RAG (Retrieval-Augmented Generation)
• Vector embeddings and similarity search
• Document chunking and preprocessing
• Hybrid search strategies
• RAG pipeline optimization
• Knowledge graph integration
• Multi-modal RAG systems
• Document chunking and preprocessing
• Hybrid search strategies
• RAG pipeline optimization
• Knowledge graph integration
• Multi-modal RAG systems
🎯 Agentic AI Applications
• Autonomous agent architectures
• Multi-agent systems and coordination
• Tool-using AI agents
• Decision-making frameworks
• AI agent deployment and monitoring
• Ethical AI and responsible development
• Multi-agent systems and coordination
• Tool-using AI agents
• Decision-making frameworks
• AI agent deployment and monitoring
• Ethical AI and responsible development
🛠️ Capstone Projects
Project 1: AI-Powered Assistant
• Multi-modal AI agent development
• RAG system implementation
• Production deployment on cloud
• Multi-modal AI agent development
• RAG system implementation
• Production deployment on cloud
Project 2: Autonomous Trading Bot
• Reinforcement learning implementation
• Real-time market data integration
• Risk management and monitoring
• Reinforcement learning implementation
• Real-time market data integration
• Risk management and monitoring
Project 3: Custom LLM Application
• Fine-tuning for specific domain
• Custom tool integration
• Scalable inference pipeline
• Fine-tuning for specific domain
• Custom tool integration
• Scalable inference pipeline
Project 4: AI Research Platform
• Multi-agent research system
• Knowledge discovery automation
• Scientific paper analysis
• Multi-agent research system
• Knowledge discovery automation
• Scientific paper analysis
Program Benefits
🎓 Why Choose Abeyaantrix?
🎯 Industry-Aligned Curriculum
Our curriculum is designed with input from leading AI companies and updated quarterly to reflect the latest industry trends and requirements.
👨🏫 Expert Faculty
Learn from industry veterans with 10+ years of experience in AI/ML, including former employees of Google, Microsoft, and OpenAI.
🛠️ Hands-On Learning
Build 12+ real-world projects throughout the program, creating a portfolio that showcases your expertise to potential employers.
🌐 Global Recognition
Our certification is recognized by major tech companies worldwide and is aligned with international AI/ML standards.
🚀 Ready to Transform Your Career?
Join Abeyaantrix and become a leader in the next generation of AI technology • Limited seats available • Early bird discount of 20% • Industry Expert Trainers • 100% placement assistance