AI/ML & Generative AI Fundamentals

Overview

AI/ML & Generative AI Fundamentals focuses on the concepts and hands-on skills behind modern AI. You will work through problem framing, data exploration, a churn…

What you will learn

• Define AI, ML, deep learning, and generative AI and frame business problems as ML problems (Module 1).
• Clean, explore, and feature-engineer a dataset and apply core statistics (Module 2).
• Train and evaluate classification and regression models correctly (Module 3).
• Apply clustering, dimensionality reduction, and recommendation techniques (Module 4).

Prerequisites

• Required: Basic mathematics and comfort with spreadsheets. A laptop with a modern browser; notebooks run in the cloud [confirm platform].
• Helpful: Any Python exposure. Python essentials are introduced in Module 2 and starter code is provided for every lab.

Curriculum

1. Module 1: Introduction to AI and Machine Learning

   • Topics: AI, ML, deep learning, and generative AI; history and inflection points; types of learning; problem framing; data to model to deployment; roles in AI teams.
   • Deliverable: Problem statements. Completion check: Module 1 assessment.

   Hands-on:

   • Practice: Frame a Problem. Three business problems as ML problem statements with metrics.

2. Module 2: Data Foundations

   • Topics: Data types and quality; exploration and visualization; cleaning and feature engineering; distributions and correlation; sampling and bias; train, validation, and test splits.
   • Deliverable: Prepared dataset notebook. Completion check: Module 2 assessment.

   Hands-on:

   • Practice: Explore and Prepare. Clean and feature-engineer a dataset in a notebook.

3. Module 3: Supervised Learning

   • Topics: Linear and logistic regression; decision trees and ensembles; model selection; accuracy, precision, recall, F1; confusion matrix and ROC; overfitting and cross-validation.
   • Deliverable: Model notebook with evaluation. Completion check: Module 3 assessment.

   Hands-on:

   • Practice: Churn Predictor. Classification models on a churn dataset.

4. Module 4: Unsupervised Learning and Recommendation

   • Topics: K-means and hierarchical clustering; PCA; anomaly detection; collaborative and content-based filtering; evaluation; business applications.
   • Deliverable: Segmentation notebook. Completion check: Module 4 assessment.

   Hands-on:

   • Practice: Customer Segmentation. Clusters plus a recommendation prototype.

5. Module 5: Neural Networks and Deep Learning

   • Topics: Neurons, layers, and activation; training and backpropagation; loss and optimization; CNNs for images; RNNs and sequence models; transfer learning.
   • Deliverable: Classifier notebook. Completion check: Module 5 assessment.

   Hands-on:

   • Practice: Image Classifier. Transfer learning in a notebook.

6. Module 6: Transformers and Large Language Models

   • Topics: Attention and embeddings; pretraining and fine-tuning; scaling and model families; prompting and parameters; open models with Hugging Face; limitations and hallucination.
   • Deliverable: Comparison notes. Completion check: Module 6 assessment.

   Hands-on:

   • Practice: LLM Playground. Open and hosted models compared on text tasks.

7. Module 7: Generative AI Applications

   • Topics: Text, image, audio, and video generation; multimodal models; code generation; RAG and embeddings; agents and tool use; fine-tuning vs prompting.
   • Deliverable: RAG notebook. Completion check: Module 7 assessment.

   Hands-on:

   • Practice: Mini RAG App. Document question answering with embeddings and an LLM.

8. Module 8: Responsible AI, Deployment, and Careers

   • Topics: Bias, fairness, and explainability; privacy and security; regulation overview; MLOps and LLMOps basics; monitoring; AI career paths and learning roadmap.
   • Deliverable: Model card and roadmap. Completion check: Module 8 assessment.
   • Capstone (independent work, [CAPSTONE_HOURS] hours): An end-to-end AI project: data preparation, a supervised model with evaluation, a generative AI component, a model card, and a business presentation.

   Hands-on:

   • Practice: Model Card and Roadmap. Model card for a lab model and a personal roadmap.

Course fee

$699 $399

What's included

• Live cohort + recordings: Attend every session live and revisit the recordings for one year.
• Real lab environment: Practise on a hands-on environment that mirrors real projects.
• Live doubt-clearing support: Ask questions and get answers in real time during every session.
• Assessments and lab activities: Apply what you learn through graded assessments and guided labs.
• Learning assets: Keep the slides, notes, and reference material for good.
• Role-readiness guidance: Mentor guidance to get you ready for the role you are targeting.
• Verifiable certificate: Earn a shareable certificate on completing the hands-on capstone.
• Global peer community: Learn with a global cohort and stay connected after the program.

In short

AI/ML & Generative AI Fundamentals is a 24-hour instructor-led course covering Module 1: Introduction to AI and Machine Learning, Module 2: Data Foundations, Module 3: Supervised Learning. It includes guided practical labs.

Key facts

• Course: AI/ML & Generative AI Fundamentals
• Duration: 24 hours
• Modules: 8
• Format: instructor-led live training

Frequently asked questions

Topics covered

AI/ML & Generative AI Fundamentals, Artificial intelligence, Corporate training