SCHOOLVILLE BOOTCAMP PROGRAM (BCP)
Artificial Intelligence & Automation
Course Code: BCP-AIA-101
Course Description
Learn machine learning concepts and build AI applications that solve real-world problems.
Course Overview
Artificial Intelligence & Automation continues to be one of the fastest-growing areas in technology, as organisations increasingly seek skilled professionals who can build and deploy real machine learning solutions. This BootCamp provides participants with practical, hands-on skills in Python, TensorFlow, and modern ML tooling - delivered through Project-Based Learning (PBL) combining instructor-led training, guided labs, and industry mentorship.
Course Prerequisite
No previous machine learning experience is required.
RECOMMENDED REQUIREMENTS:
- • Basic computer literacy
- • Comfort with basic Python syntax (preferred but not mandatory)
- • Logical and analytical thinking
- • Personal laptop computer
- • Reliable internet connection
Course Delivery Method
Instructor-Led
- • Physical Classroom
- • Virtual Live Classroom
- • Hybrid Learning
Course Language: English Language
Course Outcomes
Upon successful completion, participants will be able to:
Explain core machine learning concepts, terminology, and best practices.
Clean, prepare, and explore real-world datasets using Python.
Build, train, and evaluate supervised and unsupervised learning models.
Design and train neural networks using TensorFlow.
Apply natural language processing techniques to text data.
Deploy and monitor a machine learning model in a realistic setting.
Communicate model results and limitations clearly to stakeholders.
Build and present a professional AI project portfolio.
Course Duration
DURATION
12 Weeks
Intensive Study
FREQUENCY
5 Days / Week
Regular Tracks
SESSION LENGTH
6 Hours / Day
Accelerated Format
TOTAL LEARNING
360 Hours
Practical & Theory
Daily Schedule Breakdown
• 3 Hours Guided Instructor-Led Practical Training
• 3 Hours Independent (Unguided) Practical Session
Course Structure
The course is delivered through three progressive learning modules. Each module builds on the last - from foundations, to applied practice, to advanced work and a capstone project.
Foundations Module
Core Practice Module
Advanced & Capstone Module
Module 1: Foundations Module (SBM)
Duration: 2 Weeks (60 Hours)Python for Data Science Refresher
Working with NumPy & Pandas
Data Cleaning & Preprocessing
Supervised vs Unsupervised Learning
Regression & Classification Basics
Model Evaluation Metrics
Module 2: Core Practice Module (SIM)
Duration: 2 Weeks (60 Hours)Introduction to Neural Networks
Building Models with TensorFlow
Training, Validation & Overfitting
Text Preprocessing & Tokenization
Word Embeddings
Building a Simple NLP Classifier
Module 3: Advanced & Capstone Module (SAM)
Duration: 2 Weeks (60 Hours)Automating Workflows with AI
Model Deployment Basics
Monitoring Model Performance
Capstone Planning
Building the AI Capstone Project
Presentation Preparation
Total Lessons Summary
Module
Lessons
Foundations Module (SBM)
6
Core Practice Module (SIM)
6
Advanced & Capstone Module (SAM)
6
Total Lessons
18
Practical Laboratory Activities
Assessments, Capstone Project & Project Presentation
Assessment is competency-based and measures technical knowledge, analytical ability, practical skills, documentation, and professional communication.
Assessment Component
Weight
Foundations Module (SBM)
10%
Core Practice Module (SIM)
20%
Advanced & Capstone Module (SAM)
30%
Capstone Project
30%
Project Presentation
10%
Total
100%
Capstone Project
Each participant or project team will complete a practical AI & Automation project, applying machine learning to a real-world problem end to end.
Capstone Planning & Proposal
Requirements & Needs Analysis
Solution Design & Architecture
Technical Implementation
Testing & Quality Assurance
Documentation & Reporting
Stakeholder Presentation
Portfolio Packaging
Peer & Mentor Review
Projects should include:
Problem identification and analysis
Solution design
Technical implementation or demonstration
Testing and validation
Recommendations
Professional documentation
Project Presentation (Demo Day)
Participants will present their completed projects before a panel comprising:
Projects will be evaluated based on:
Certification
Participants who successfully complete all programme requirements will receive the Schoolville BootCamp Certificate in Artificial Intelligence & Automation, with classifications of Pass, Credit, Merit, or Distinction based on overall performance.
Graduates will also complete the programme with a professional portfolio demonstrating practical laboratory work, applied projects, and a capstone project suitable for employment, consulting engagements, or further professional certification pathways.
Ready to Launch Your Artificial Intelligence & Automation Career?
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