Generative AI

Information

Our course on Generative AI and Large Language Models (LLMs) offers an in-depth exploration of state-of-the-art generative techniques and their applications. Participants will study various LLMs, such as GPT-4, BERT, and Transformer-based architectures, understanding their underlying mechanisms and capabilities. The curriculum includes hands-on projects where students will build and fine-tune their own generative models for tasks like text generation, summarization, and creative content creation. Emphasis is placed on practical skills, including data preprocessing, model training, and deployment strategies. By the end of the course, learners will be proficient in leveraging generative AI and LLMs to develop innovative solutions across diverse domains.

$999.99

What you'll learn:

Learn to integrate AI/ML techniques into traditional ETL pipelines to build intelligent data workflows.
Gain proficiency in machine learning algorithms such as regression, classification, and clustering using Python libraries.
Build a strong foundation in linear algebra and statistics tailored for real-world machine learning applications.
Perform exploratory data analysis (EDA) using Matplotlib, Seaborn, and Plotly to uncover actionable insights.
Apply advanced feature engineering techniques to enhance model input quality and improve performance.

Design scalable training and testing pipelines that prevent data leakage and are ready for production deployment.
Develop and validate ML models using frameworks like Scikit-Learn, TensorFlow, and PyTorch.
Optimize model performance with evaluation metrics, hyperparameter tuning, and interpretability tools like SHAP and LIME.
Deploy ML models using MLOps best practices, including continuous integration, monitoring, and drift detection.
Explore deep learning applications in NLP, computer vision, and agentic AI with hands-on experience in transformers and LLMs.

Why Choose Our Program?

Intensive, hands-on labs with real-world projects.
Mentorship from seasoned AI practitioners Step-by-step guidance to build a standout.
AI portfolio Networking with industry leaders and peers
Mentorship from seasoned AI practitioners Step-by-step guidance to build a standout.
AI portfolio Networking with industry leaders and peers

This course includes

20 hours of LIVE classes
2 articles to help you onboard
5 downloadable resources

Course Content:

Introduction to ML/AI for Data Engineers

Overview of ML/AI Concepts
Mapping Data Engineering to ML
Course Roadmap & Expectations
Weekly Mini Project

Fundamentals of ML, Linear Algebra and Statistics

ML Basics & Common Algorithms
Linear Algebra Essentials
Statistics & Hypothesis Testing
Integration and Wrap-up

EDA for ML

Data Visualization Techniques
Statistical Summaries & Descriptive Analytics
Outlier Detection & Data Quality Assessment

Feature Engineering for ML

Feature Extraction Techniques
Automated Feature Engineering Tools
Practical Feature Engineering Applications
Weekly Mini Project

Data Pipelines for ML

Designing ETL/ELT Processes for ML
Advanced Data Cleaning & Transformation
Integrating Real-Time Data Flows
Weekly Mini Project

Model Selection & Algorithm overview

Model Selection & Algorithm Overview
Hands-On Model Training
Best Practices in Training

Hyper-parameter Tuning and Model Evaluation

Hyper-parameter Tuning Techniques
Model Evaluation Metrics Part 1
Practical Evaluation Exercise

ML Ops & Model Deployment

Deployment Strategies & CI/CD for ML
Monitoring & Maintenance
Hands-On Deployment Exercise
Weekly Mini Project assignment & conclusion

Introduction to Deep Learning Concepts

Deep Learning Fundamentals
Building and Training a Simple Neural Network
Deep Learning vs. Traditional ML
Privacy and Data Governance in ML

Deep Learning Domain Applications

Introduction to Domain Applications
Case Studies and Practical Examples
Weekly Mini Project Introduction and Wrap-up

Specialized NLP, Transformers, LLM and GenAI

Advanced NLP Techniques
Transformers & Attention Mechanisms
Applications of Large Language Models
Weekly Mini-Project Introduction

Specialized NLP

NLP Fundamentals Recap
Sequence Modeling & Embedding
NLP with Transformers
Mini-Project Introduction

Agentic AI

Introduction to Agentic AI
Core Architectures & Planning Strategies
Agent Frameworks in Practice
Mini-Project Introduction

Who Should Apply:

Engineers seeking to pivot into ML/AI roles.
Professionals aiming to deepen their AI expertise.
Anyone passionate about leveraging AI in real-world scenarios