Artificial Intelligence ( ML/DL)
Artificial Intelligence (AI) enables machines to perform tasks requiring human-like intelligence. Machine Learning (ML) and Deep Learning (DL) are AI subsets where systems learn from data, with DL using neural networks for more complex tasks.
Our Featured Courses
Machine Learning with Python
Machine Learning with python course delves deeper into the Machine Learning fundamental concepts further explaining Machine Learning algorithms and their implementation.
AI ML Fundamentals
Learn Machine Learning principles with in-depth practical exposure to how projects are implemented at organizations in this Machine Learning course. You learn all about real-world applications of ML & the essentials of statistics and ML models
Deep Generative AI
we will delve into the fundamental concepts, techniques, and applications of generative AI. From understanding the underlying neural networks and deep learning architectures to leveraging advanced algorithms such as generative adversarial networks (GANs) and variational autoencoders (VAEs)
Deep Learning with Computer Vision
In this training session you will build deep learning models for Computer Vision. One the detailed case study will be using attention based mechanism to do image segmentation and object recognition. Another detailed case study would be image captioning and understanding using a combination attention based CNN and sequence based model(LSTM).
Deep Learning Internals
In this training session you will build deep learning models using neural networks, explore what they are, what they do, and how. To remove the barrier introduced by designing, training, and tuning networks, and to be able to achieve high performance with less labeled data, you will also build deep learning classifiers tailored to your specific task using pretrained models, which we call deep features.
Machine Learning Internals
This trainig session provides a deep dive into machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/nonparametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI)
AI ML Advanced
• Master advanced feature engineering and ensemble techniques
• Gain proficiency in advanced deep learning architectures and GANs
• Learn about deploying ML models to production and
maintaining them
• Complete advanced case studies in image generation and
machine translation
Generative AI deep-dive
This intensive 5-day course provides a comprehensive exploration of advanced machine learning (ML) and deep learning (DL) concepts with a focus on generative AI techniques, including diffusion models and text generation. Participants will gain in-depth knowledge of various neural network architectures, embedding techniques, generative modeling, and the latest advancements in large language models (LLMs). The course also covers practical aspects of model optimization and MLOps for efficient deployment and scaling of AI systems
