• Gain a complete understanding of the Applied AI & Data science program
• Understand the course modules and the topics covered
• Get to know the program learning path and program faculty

• Understand the fundamentals of statistics and basic categories of data science
• Learn about the background of Python and its functionalities
• Dive deep into various Python libraries like NumPy and pandas

• Acquire knowledge about machine learning, including a comprehensive understanding of the diverse algorithms employed within this domain
• Discover the process of collecting data from multiple sources and transforming it into a practical and usable format.
• Explore and study the various algorithms utilized in machine learning to develop expertise in this area.
• Dive deep into the intricacies of exploratory data analysis, mastering the techniques and methodologies used to examine, visualize, and interpret data sets to extract meaningful insights and make informed decisions

• Learn how feature engineering is used to prepare a machine learning model
• Dive deep into various supervised learning techniques, including but not limited to linear and logistic regression, decision trees, and random forests
• Delve into unsupervised learning and gain proficiency in various techniques, including K-means clustering, hierarchical clustering, and dimensionality reduction

• Learn the theoretical and practical aspects of model training, evaluation and fine tuning.
• Gain proficiency in assessing the performance of models by utilizing established evaluation measures and matrices commonly employed in the industry
• Analyze and interpret the results of your models, making informed decisions about their effectiveness and suitability for real-world applications
• Master the skill of model fine-tuning to improve efficiency

• Gain a solid understanding of deep learning, training artificial neural networks to perform complex tasks.
• Understand artificial neural networks to design, train, and utilize neural networks effectively.
• Learn how deep learning enables the automatic learning of hierarchical representations of data
• Use CNNs for image recognition, ensuring that participants can apply this architecture to tasks like object detection, classification, and image analysis
• Become familiar with RNNs
• Learn how to create and utilize GANs for content generation.

• Learn about the taxonomy of generative model families and the ways to build
• generative models
• Understand the concepts of variational autoencoders, generative adversarial networks (GANs), diffusion models, and transformers
• Master the training and analysis of GPT Understand the broader applications of generative AI in various fields

This program covers topics that will help prepare you for what lies ahead. By completing this program, you will acquire valuable insights and knowledge that will equip you to thrive in the rapidly evolving field of data science.

The capstone project will allow you to implement the skills you will learn throughout this program. You will solve industry-specific challenges by leveraging various AI and data science techniques learned across various modules in the program. This project will help you showcase your expertise to potential employers.

• Master Classes by Brown Faculty
  • Attend live, online master classes led by Brown faculty to interact with peers and gain insights into the current trends and future outlook of data science. 
• Doubt Clarification and Project Mentoring
  • The purpose of this course is to help you clarify any questions or concerns you may have about the curriculum and projects you've completed this far.