AI Bootcamp Summary

Course Outcomes

By the end of the course, I will be proficient in:

• Python Programming:
  • Automating data cleanup and restructuring.
  • Interacting with APIs and parsing JSON.
• Data Visualization and Analysis:
  • Creating detailed graphs, charts, and tables using various data-driven programming languages and libraries.
• Machine Learning Techniques:
  • Applying unsupervised learning models to categorize data.
  • Using supervised learning models to make data predictions.
  • Evaluating and optimizing the performance of machine learning models.
• Deep Learning and AI Applications:
  • Implementing neural networks and deep learning models for data predictions.
  • Applying Natural Language Processing (NLP) and transformer models for tasks like sentiment analysis and generative content creation.
• AI Ethics and Data Regulations:
  • Understanding the ethical considerations and legal implications of AI technologies.

Curriculum Structure

The course is divided into three sections, each focusing on different aspects of AI and machine learning:

• Programming for AI (Modules 1–10):
  • Learning the fundamentals of AI and machine learning.
  • Building coding skills for AI model development.
  • Manipulating data for AI training.
• Machine Learning (Modules 11–17):
  • Creating and optimizing machine learning models.
  • Covering both supervised and unsupervised learning techniques.
  • Learning about AI ethics and data regulations.
• AI Innovations (Modules 18–24):
  • Exploring advanced topics like neural networks, deep learning, and NLP.
  • Studying pre-trained transformer models and their applications.
  • Investigating emerging AI research and development topics.