Artificial Intelligence Bootcamp for O/A Level

Introduction to AI

• • What is AI? Definitions and real-world examples.
  • Brief history of AI.
  • Applications of AI in daily life.

Understanding Data

• • What is data? Types of data: structured vs. unstructured.
  • Importance of data in AI.
  • Data collection methods.

AI Ethics and Bias

• • Ethical considerations in AI: fairness, accountability, transparency.
  • Understanding bias in AI models.

Introduction to Data Analysis

• • Understand different types of data and the importance of data analysis.
  • Explore real-world examples of data analysis across various fields.

Power BI

• • Import data from various sources and create basic visual reports.
  • Manipulate and filter data within reports, create measures and calculated columns.
  • Use DAX to solve complex data problems and enhance report functionality.

Python Concepts

• • Python programming fundamentals: expressions, operators, basic data structures (lists, tuples, dictionaries, sets, strings).
  • Introduction to IDEs and Jupyter NotebookControl structures: conditional statements, for and while loops, break/continue statements, and nested loops.
  • Functions: defining, calling, parameters, arguments, scope, and documentation.

Data Handling and Analysis with Python

• • Introduction to NumPy for numerical operations.
  • Data visualization with Matplotlib.
  • Basics of exploratory data analysis: handling missing values, data binning, outlier analysis, feature selection.
  • Practical EDA session using real datasets.

Machine Learning Fundamentals

• • Overview of machine learning algorithms: Naive Bayes, Logistic Regression, SVM, Decision Trees, and Random Forests.
  • Model evaluation metrics and performance tuning.
  • Principal Component Analysis (PCA) and Singular Value Decomposition (SVD).
  • Clustering techniques and evaluations.

Advanced Applications: NLP, Computer Vision, and MLOps

• • Core NLP techniques: tokenization, POS tagging, NER, text classification.
  • Introduction to LLMs: Transformer architecture, attention mechanisms, GPT, BERT.
  • Computer vision basics: object detection, tracking, and using convolutional neural networks.
  • Introduction to MLOps: setting up environments, deploying ML models using FastAPI and Docker, integrating with Google Cloud.
  • Developing and deploying a machine learning model for real-world application.

Introduction to Low-Code/No-Code Tools

• • Understand the benefits and limitations of low-code/no-code development.
  • Get acquainted with various platforms for different development needs.

Project-Based Learning with Low-Code/No-Code Platforms

• • Softr: Build web applications using a drag-and-drop interface.
  • Bubble: Develop web apps with visual editing, database functionality, and user authentication.
  • Glide & Draftbit: Create simple to complex mobile apps for multiple platforms.
  • Zapier & Bildr: Automate workflows and build flexible, customizable app interfaces.
  • Backendless & FlutterFlow: Manage databases, APIs, and create cross-platform apps.
  • Botpress & Manychat: Develop conversational AI bots using NLP and LLM-based tools.