AI Academy

Foundational AI & Machine Learning with Python

A live, hands-on AI program designed to take beginners from Python fundamentals to predictive machine learning models — step by step.

📅 February 21 & 28 and March 7 & 14

📅 Every Saturday

🕕 6:00 PM – 10:00 PM (Live via Zoom)

No prior coding experience required.

Limited slots to keep sessions interactive.

Most people are introduced to AI through:

• Long explanations

• Abstract theory

• Tools they don’t fully understand

  
  

They know about AI, but can’t build one.

This program is different.

AI Academy is structured as a guided learning journey, with concepts introduced only when needed to build something.

You start with core programming.

You work with data early.

You build machine learning models before the course ends.

You evaluate what you built using proper metrics.

This is not about memorizing definitions.

It’s about understanding how AI is constructed.

COURSE CURRICULUM & SCHEDULE

SESSION 1

Python Foundations for Data

📅 February 21 | 6:00 PM – 10:00 PM

You begin by learning the logic and syntax required for data and AI work.

Topics

→ Variables and data types

→ Basic math and expressions

→ If/Else logic and loops

→ Lists, dictionaries, and functions

Hands-on Lab

Build a Grade Calculator that:

• Accepts student scores

• Computes averages using functions

• Assigns letter grades using logic

SESSION 2

Data Manipulation & Visualization

📅 February 28 | 6:00 PM – 10:00 PM

This is where AI starts to take shape.

Topics

→ NumPy arrays and basic operations

→ Pandas DataFrames and CSV handling

→ Filtering data and handling missing values

→ Creating charts to visualize patterns

Hands-on Lab

Exploratory Data Analysis project using a clean dataset to generate insights through visualizations.

SESSION 3

Introduction to Machine Learning

📅 March 7 | 6:00 PM – 10:00 PM

You transition from data analysis to prediction.

Topics

→ Supervised learning explained clearly

→ Features vs labels

→ Train/test splitting

→ Regression and basic classification models

Hands-on Lab

Build a guided Price Predictor using factors such as mileage and age.

SESSION 4

Model Evaluation & Capstone

📅 March 14 | 6:00 PM – 10:00 PM

You learn how to judge your model properly.

Topics

→ Accuracy and error metrics

→ Confusion matrix and MAE

→ Understanding overfitting

→ End-to-end ML workflow

Capstone

Build, evaluate, and explain a complete machine learning pipeline.

BY THE END OF THE PROGRAM, PARTICIPANTS WILL BE ABLE TO:

→ Write Python code for basic data tasks

→ Load, clean, and analyze datasets

→ Build simple machine learning models

→ Evaluate model performance using standard metrics

→ Explain how their model works and its limitations

This program builds AI literacy and foundational skills, preparing learners for advanced AI, data, or engineering pathways.

WHO THIS PROGRAM IS DESIGNED FOR

→ Beginners with no coding background

→ Engineers and technical professionals

→ Career shifters exploring AI

→ Students preparing for advanced AI studies

This program focuses on foundational AI skills.

Advanced topics such as deep learning and deployment are covered in higher-level programs.

LIVE FORMAT & LIMITED SLOTS

This is a live, instructor-led program conducted via Zoom.

To maintain:

• Hands-on guidance

• Interactive labs

• Meaningful Q&A

  
  

Enrollment is limited.

Once slots are filled, registration closes.

📅 Starts February 21

🕕 6 PM – 10 PM

📍 Live via Zoom

You can keep reading about AI.

Or you can finally understand how one is built.

Stop the introductions. Build an AI model.

AI INSTRUCTOR

Daniel S. Caparro

Daniel is an AI/ML Engineer specializing in Natural Language Processing (NLP), AI model creation, and data-driven solutions.

He is skilled in deep learning frameworks such as TensorFlow, PyTorch, and Keras, as well as MLOps and scalable AI architectures.

He has successfully developed and deployed AI-driven conversational agents and handled large-scale datasets. Daniel has a robust research background, with publications and contributions to AI solutions.