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Post Graduate Program in Generative AI and ML with Edureka and Illinois Tech: Apply Now!

Apoorva

Jan 9, 2025
3 min read

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Registrations are invited for the Post Graduate Program in Generative AI and ML with Edureka and Illinois Tech. Check all the details below and secure your spot now!

About PGP in Gen AI and ML

The Post Graduate Program in Generative AI and ML, a collaborative offering from Edureka and Illinois Tech, is a great opportunity for generative AI and ML enthusiasts to enter the domain. As Chicago’s leading tech university, Illinois Tech stands out as a premier institution for technological and scientific innovation. Renowned for its groundbreaking research, advanced degree programs in STEM fields, and one-of-a-kind experiential learning, Illinois Tech ranks among the top universities in the nation for ensuring students graduate with the future-focused skills they need to be uniquely career-ready.

Program Highlights

Comprehensive Curriculum: Covers essential tools and practices in Generative AI and Machine Learning.
Industry-Relevant Projects: Engage in over 9 projects and 15+ practical use cases that mirror real-world challenges.
Expert Instructors: Learn from industry professionals and esteemed faculty members.
Flexible Learning: Live, instructor-led online classes conducted on weekends.
Career Assistance: Benefit from placement support and access to Illinois Tech’s Alumni Association.

Post Graduate Program in Generative AI and ML with Edureka and Illinois Tech

Program Structure

Python Programming for AI and ML

Module 1: Introduction to Python
Module 2: Sequences and File Operations
Module 3: Functions and Object-oriented Programming
Module 4: Working with Modules and Handling Exceptions
Module 5: Array Manipulation using NumPy
Module 6: Data Manipulation using Pandas
Module 7: Data Visualization using Matplotlib and Seaborn
Module 8 : GUI Programming
Module 9 : Developing Web Maps and Representing Information using Plots (Self-paced)
Module 10: Web Scraping and Computer Vision using OpenCV (Self-Paced)
Module 11: Database Integration with Python (Self-Paced)

Statistical Methods for Predictive Analysis

Module 1: Probability and Statistics
Module 2: Inferential Statistics
Module 3: Regression
Module 4: Predictive Analytics

Applied Machine Learning Techniques

Module 1: Introduction to Machine Learning
Module 2: Supervised Learning – Regression
Module 3: Evaluating Regression Models
Module 4: Supervised Learning – Classification
Module 5: Decision Tree and Random Forest Models
Module 6: Mathematical and Bayesian Models
Module 7 : Dimensionality Reduction
Module 8: Unsupervised Learning using Clustering
Module 9: Model Evaluation and Hyperparameter Tuning
Module 10: Model Boosting and Optimization
Module 11: Association Rule Mining and Recommendation Engines (Self-paced)
Module 12: Time Series Analysis (Self-paced)

Artificial Intelligence and Deep Learning

Module 1: Introduction to Text Mining and NLP
Module 2: Extracting, Cleaning, and Preprocessing Text
Module 3: Analyzing Sentence Structure
Module 4: Text Classification-I
Module 5: Text Classification-II
Module 6: Introduction to Deep Learning
Module 7: Getting Started with TensorFlow 2.0
Module 8: Convolution Neural Network
Module 9: Regional CNN
Module 10: Boltzmann Machine and Autoencoder
Module 11: Developing a Criminal Identification and Detection Application Using OpenCV
Module 12: TensorFlow for Deployment
Module 13: Project
Module 14: Emotion and Gender Detection (Self-paced)
Module 15: Introduction to RNN and GRU (Self-paced)
Module 16: LSTM (Self-paced)
Module 17: Auto Image Captioning Using CNN LSTM (Self-paced)

Generative AI and Large Language Models (LLMs)

Module 1: Generative AI and Its Industry Applications
Module 2: Autoencoders and GANs
Module 3: Language Models and Transformer-based Generative Models
Module 4: Working with ChatGPT
Module 5: Prompting Techniques for Generative Models
Module 6: Generative AI with LLMs
Module 7: LLMs for Search, Prediction, and Generation
Module 8 : Interacting with Data using Retrieval-Augmented Generation
Module 9: LLMs for Word Embedding and Chunking Mechanism
Module 10: LangChain and LlamaIndex for LLM Application Development
Module 11 : Fine Tuning and Evaluating Generative Models
Module 12: Generative AI on Cloud
Module 13: Generative AI with Python
Module 14: Industry Case Studies and Project
Module 15: Machine Learning with Generative AI (Self-paced)
Module 16: Generative AI Tools (Self-paced)

Capstone Project

Why Choose This Program?

Prestigious Certification: Receive a certificate jointly awarded by Edureka and Illinois Institute of Technology.
High Demand for AI Professionals: The World Economic Forum reports that AI and ML will generate 97 million new jobs by 2025.
Lucrative Career Opportunities: The average annual salary for a generative AI engineer is $170,062.

Eligibility

Suitable for any undergraduate degree holder with basic knowledge of at least one programming language.

Program Duration

6 months, requiring a commitment of 8-10 hours per week.

Enrollment Process

Application: Submit a detailed application form.
Interview: Attend an interview conducted by a subject matter expert or program faculty member.
Admission: Upon selection, receive an admission offer letter from Edureka.

For more details and to enrol, visit Edureka’s official website.