LLM
Become an LLM Engineer in 6 months with 4 months of hands-on experience on live projects. This program will help you understand the basics and advanced levels of knowledge to succeed in this field.
Hybrid Mode - Choose your own choice
Office Environment for both offline as well as Online Classes
One to one mentorship
Job Assistance, Excellent Past record of 94% success
Become an LLM (large Language Models) expert or Prompt Engineer with our premier course. Covering foundational principles to advanced techniques, our hands-on projects and expert guidance ensure a deep understanding. Prepare to tackle complex challenges and drive innovation in LLM.
#LLM Concepts, #Prompt Engineering, #Applied Statistics
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Discover what awaits you as an LLM Expert!
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Unlock the potential earning opportunities in this field!
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Explore the vast horizons of LLM careers!
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Unveil the promising future prospects!
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Envision how this course will empower you!
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Gain insights into the comprehensive curriculum and skills you'll master!
LLM Opportunity Insights
3 Million Jobs by 2026
As per Experts, In India, LLM & Prompt Engineering will generate 3 Mn+ job by 2026
Earn upto 20 Lac+ per annum
With 2 years of experience, participants can earn upto 20 LPA
Skill Gap
Currently only 32% jobs in LLM are being full-filled due to lack of skills
Enroll Now
Call us to reserve your spot in upcoming batches.
Next Batches
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Curriculum Designed by Industry Experts
1. Introduction to Deep Learning
Overview of Deep Learning: Understanding the basics and the significance of deep learning in various applications.
4. Deep Learning Optimization Techniques
Backpropagation and Gradient Descent: Detailed exploration of how neural networks learn and how to optimize this process.
7. Natural Language Processing with Deep Learning
Text Preprocessing and Vectorization: Techniques for preparing text data for modeling, including tokenization and embedding.
10. Building a Chatbot with RNNs and Transformers
Designing a Chatbot: Overview of the components and design decisions involved in building a chatbot.
13. Capstone Project
Creating a ChatGPT-like Application: Final project to consolidate learning by building a comprehensive language-based AI application.
2. Fundamentals of Machine Learning
Basic Concepts: Covering essential machine learning concepts such as supervised vs. unsupervised learning, regression, classification, overfitting, and underfitting.
5. Convolutional Neural Networks (CNNs)
Fundamentals of CNNs: Learning how CNNs work and their applications in image and video processing.
8. Transformer Models
Architecture of Transformers: Detailed study of the transformer model, focusing on self-attention mechanisms.
11. Deployment of Deep Learning Models
Model Deployment Strategies: Best practices for deploying models in production environments.
14. Career Preparation in AI
Career Paths in AI: Guidance on different career paths and opportunities in AI and deep learning.
3. Introduction to Neural Networks
Neural Network Architecture: Understanding the structure and function of neural networks, including neurons, weights, biases, and activation functions.
6. Recurrent Neural Networks (RNNs) and LSTM
Understanding RNNs: Exploring the architecture that makes RNNs suited for sequence data like text and audio.
9. Advanced NLP with Transformers
BERT and GPT Models: Exploring pre-trained models like BERT for understanding and GPT for generation tasks.
12. Ethics and Future of AI
Ethical Considerations: Discussing the ethical implications of AI and machine learning, including bias, fairness, and transparency.