Generative AI with Large Language Models | Generative AI Course | infyni

Gen AI with Language Models: Unleashing ML and NLP with Python

This course is designed to equip participants with a robust understanding of Generative AI utilizing Language Models (LLM). Over 30 hours, participants will delve into the realms of Machine Learning (ML), Natural Language Processing (NLP), and Python, developing skills.

Live Course

Live Class: Friday, 08 Mar

Duration: 30 Hours

Enrolled: 0

Offered by: infyni

Live Course
$94 60% off


About Course

Explore the cutting-edge realm of Artificial Intelligence with this comprehensive course on leveraging Generative AI techniques through language models. Dive deep into Natural Language Processing (NLP) and Machine Learning (ML) using Python. Learn to harness the power of language models to create, analyze, and deploy intelligent systems that understand and generate human-like text. This course offers hands-on experience and practical skills in manipulating large datasets, training models, and developing applications that push the boundaries of AI-driven capabilities.

Skills You Will Gain

Python for AI NLP Fundamentals Machine Learning Basic Language Models Text Generation Sentiment Analysis Named Entity Recognition Text Summarization Model Deployment Ethical AI

Course Offerings

  • Instructor-led interactive classes
  • Clarify your doubts during class
  • Access recordings of the class
  • Attend on mobile or tablet
  • Live projects to practice
  • Case studies to learn from
  • Lifetime mentorship support
  • Industry specific curriculum
  • Certificate of completion
  • Employability opportunity
  • Topics
  • Instructor (1)
  • Overview of Generative AI and its Applications
  • ¬†Introduction to Language Models (LLM)
  • ¬†Understanding the synergy of ML, NLP, and Python
  • ¬†Basics of Python for ML and NLP
  • Overview of Machine Learning
  • Supervised vs. Unsupervised Learning
  • Feature Engineering and Selection
  • Model Training and Evaluation
  • Basics of Natural Language Processing
  • Tokenization and Text Preprocessing
  • Named Entity Recognition (NER) and Part-of-Speech Tagging
  • Sentiment Analysis with ML
  • Utilizing pre-trained language models (e.g., BERT, GPT) in Python
  • Fine-tuning language models for specific tasks
  • Text generation with language models
  • Evaluating language model performance
  • Sequence-to-Sequence Models
  • Attention Mechanisms
  • Transfer Learning in NLP
  • Handling Imbalanced Text Data
  • Text summarization
  • Question Answering Systems
  • Dialogue Systems and Chatbots
  • Creative Writing with Language Models