TRAININGS

1. TensorFlow Training

Introduction to TensorFlow and its architecture.
Hands-on exercises on building and deploying machine learning models using TensorFlow.
Training on TensorFlow Extended (TFX) for end-to-end ML pipeline development.

2. PyTorch Training

Understanding PyTorch framework and its advantages for deep learning.
Practical sessions on building neural networks, implementing custom layers, and training models with PyTorch.
Training on PyTorch Lightning for scalable and efficient deep learning experiments.

3. scikit-learn Training

Comprehensive training on scikit-learn library for machine learning in Python.
Learning about various algorithms for classification, regression, clustering, and dimensionality reduction.
Hands-on exercises to master data preprocessing, model selection, and hyperparameter tuning with scikit-learn.

4. Apache Spark MLlib Training

Introduction to Apache Spark and MLlib for scalable machine learning on big data.
Training on Spark MLlib algorithms for distributed data processing, feature extraction, and model training.
Hands-on experience with Spark ML pipelines and integration with other big data tools.

5. Keras Training

Understanding Keras as a high-level neural networks API for building and training deep learning models.
Practical sessions on building convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) with Keras.
Training on Keras Tuner for hyperparameter optimization and model tuning.

6. AI with AWS Training

Hands-on AI Expertise: Explore machine learning algorithms, deploy models with AWS SageMaker, and seamlessly integrate AI into applications through practical labs.
AI Innovation with AWS: Empower AI practitioners, developers, and IT professionals to confidently leverage AWS for impactful business outcomes and industry-ready expertise.

7. OpenCV Training

Comprehensive training on OpenCV library for computer vision and image processing tasks.
Understanding image manipulation, object detection, and feature extraction techniques with OpenCV.
Practical sessions on building computer vision applications such as face recognition, object tracking, and image segmentation.

8.Apache Kafka Training

Introduction to Apache Kafka for real-time data streaming and event processing.
Training on Kafka Streams API for building scalable and fault-tolerant stream processing applications.
Hands-on exercises to implement data pipelines, event-driven architectures, and real-time analytics with Kafka.

9. Kubernetes Training

Training on ethical considerations in AI development, bias detection, fairness, and transparency in AI algorithms.
Discussion on responsible AI practices, regulatory compliance, and guidelines for ethical AI deployment.

10. Model Deployment and Productionization Training

Training on best practices for model deployment, serving, and monitoring in production environments.
Learning about container-based deployment strategies, model versioning, and A/B testing for machine learning models.
Practical sessions on deploying AI models as RESTful APIs, serverless functions, or microservices using platforms like TensorFlow Serving, Flask, and FastAPI.

11. Docker Training

Gain a thorough understanding of Docker with lectures, hands-on exercises, and real-world project-based learning.
Master containerized application development, shipping, and running, ensuring industry-ready skills for efficient application lifecycle management.

12. NLTK (Natural Language Toolkit) Training

Training on NLTK library for natural language processing tasks such as tokenization, stemming, and part-of-speech tagging.
Learning about sentiment analysis, text classification, and named entity recognition (NER) using NLTK.
Hands-on exercises to develop NLP applications and sentiment analysis models with NLTK.