Course Objective
Prepare students to use the integration of Embedded Systems and Artificial Intelligence to resolve challenges in industrial systems. Students will learn how incorporating machine learning and deep learning algorithms with embedded systems can streamline processes and automate tasks. Focus will be on developing system approach through integrated projects to master specific methods and tools applied in the aeronautics, space, automobiles, and multimedia domains. The course will impart an in-depth knowledge focussing on both theoretical and practical aspects.
Fee Structure
The course comprises of 6 modules mentioned under the Course Syllabus below. Student can enrol for the full course (6 modules) or is free to enrol for individual modules.
Registration Fee: Rs.500/-
Full Course Fees: Rs.94500/-
Each Module Course Fees: Rs.17500/-
Registration fees and course fees
should to be paid online or by Demand Draft.
Online Payment Details: Account Name – ICIT PVT LTD
Account Number: 27205000514
IFSC Code: SCBL0036107
Bank: Standard Chartered Bank
Bank Branch: Aundh Branch, 163, Harsh Orchid, New DP Road, Nagras RoadMall, Ward no 8, Aundh, Pune 411007
Demand Draft: To be drawn on any nationalized bank in favour of “ICIT Pvt. Ltd., Pune” and should
be payable at par.
Note that registration and course fees once paid are non refundable.
Big Data Engineering with Hadoop and Spark
- Introduction to Big Data and Distributed Computing
- Overview of Hadoop Ecosystem
- Hadoop Distributed File System (HDFS)
- MapReduce Programming Paradigm
- Hadoop Installation and Configuration
- Hadoop YARN Architecture
- Hadoop MapReduce Optimization Techniques
- Introduction to Apache Spark
- Spark RDDs (Resilient Distributed Datasets)
- Spark DataFrame and Dataset APIs
- Spark SQL for Data Processing
- Spark Streaming for Real-time Analytics
- Spark MLlib for Machine Learning
- Spark GraphX for Graph Processing
- Integration of Hadoop and Spark
- Performance Tuning and Optimization in Spark
- Best Practices for Big Data Engineering
- Real-world Use Cases of Hadoop and Spark
Computer Vision
- Introduction to Computer Vision
- Image Formation and Representation
- Image Filtering and Enhancement
- Edge Detection
- Feature Detection and Description
- Image Segmentation
- Object Detection and Recognition
- Deep Learning for Computer Vision
- Convolutional Neural Networks (CNNs)
- Transfer Learning in Computer Vision
- Semantic Segmentation
- Instance Segmentation
- Object Tracking
- Pose Estimation
- 3D Computer Vision
- Image Registration and Alignment
- Image Retrieval and Similarity Matching
- Face Recognition
- Biometric Systems
- Medical Image Analysis
- Applications of Computer Vision in Industry and Research
Natural Language Processing
- Introduction to Natural Language Processing
- Text Preprocessing Techniques (Tokenization, Stemming, Lemmatization)
- Part-of-Speech Tagging
- Named Entity Recognition
- Text Classification
- Sentiment Analysis
- Language Modeling
- Word Embeddings (Word2Vec, GloVe)
- Seq2Seq Models
- Attention Mechanisms
- Transformer Models
- Pretrained Language Models (BERT, GPT)
- Text Generation Techniques
- Machine Translation
- Question Answering Systems
- Text Summarization
- Coreference Resolution
- Dependency Parsing
- Discourse Analysis
- Ethical Considerations in NLP
- Real-world Applications of NLP
Advanced Cloud Computing – AWS/Azure
- Introduction to Cloud Computing
- Overview of AWS/Azure Services
- Virtual Machines (EC2 for AWS, VMs for Azure)
- Containerization (Docker, Kubernetes)
- Serverless Computing (AWS Lambda, Azure Functions)
- Networking in the Cloud (VPCs, Virtual Networks)
- Storage Options (S3, EBS for AWS; Blob Storage, Disk Storage for Azure)
- Database Services (RDS, DynamoDB for AWS; Azure SQL Database, Cosmos DB for Azure)
- Identity and Access Management (IAM, Azure Active Directory)
- Security Best Practices in the Cloud
- Monitoring and Logging (CloudWatch, CloudTrail for AWS; Azure Monitor, Log Analytics for Azure)
- DevOps and Continuous Integration/Continuous Deployment (CI/CD) in the Cloud
- Cost Management and Optimization Strategies
- Hybrid Cloud and Multi-Cloud Architectures
- Advanced Networking Features (Load Balancers, CDN)
- Machine Learning and AI Services (AWS SageMaker, Azure Machine Learning)
- Big Data and Analytics Services (AWS EMR, Azure HDInsight)
- IoT Services (AWS IoT Core, Azure IoT Hub)
- Blockchain Services (AWS Blockchain Templates, Azure Blockchain Service)
- Real-world Use Cases and Case Studies
