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
