What you'll learn

AI Fundamentals

AI Applications - NLP, Speech recognition, Predictive analysis, Chatbots, Facial recognition, Expert systems, Gaming, Healthcare, etc

Machine Learning Essentials

Importance of Data and Data Processing

Model Training

Ethical Considerations

Advancements in AI

Supervised Learning Models: Classification

Various types of classification models for binary and multi-class classification

Performance metrics: Confusion Matrix, F1, Precision, Recall, Accuracy - which one to use in which case, with examples

Unsupervised Learning: Clustering, performance metrics

Model Improvement Methods

Feature Selection and Dimensionality reduction methods: Lasso Regression, Ridge Regression, PCA

Hyperparameter Tuning: Various methods, deep dive into Grid-search and Random-search

Market analysis of AI Products

AI Product Landscape

Defining business objectives

KPIs for AI Product

Customer centric approach to AI Product Strategy

Persona creation

AI in Product Roadmaps

Scalability and Flexibility

Risk management

Case study - OpenAI ChatGPT

Practice project

Requirements

No previous knowledge required!

Suitable for candidates from any domain.

We highly recommend you to complete the fundamental Product Management course first before starting this course. This course is focused on advanced AI concepts and Product Management. The foundation course focuses on all aspects of Product management which will be helpful to understand the advanced course. Nevertheless, you can also do this course if you are interested in learning more about AI concepts and models