AI Builders
Basic Course & Advanced Courses Guide 1-5
Age: 12–Adult
AI Builders – Advanced Course Guide I
- Target Group: Intermediate (graduates of AI Builders Basic)
- Duration: 3 Months
- Lessons: 12 (1 per week)
- Course Type: Applied Development / Model Training
- Track: AI Builders
Course Overview
The AI Builders – Advanced Course I takes students beyond theory into practical, project-driven AI development.
Students will work with real datasets, explore data preprocessing, train their first small machine learning models, and understand evaluation metrics that measure performance.
By the end of this course, learners will be capable of creating AI systems that can learn, predict, and adapt — built ethically and responsibly.
Learning Objectives
By the end of this course, students will:
Develop proficiency in Python for AI development
Understand the structure of datasets and how to clean and prepare them
Build supervised learning models from scratch
Use libraries such as NumPy, pandas, scikit-learn, and Matplotlib
Evaluate and refine AI models ethically and effectively
Lesson Breakdown (12 Weeks)
Lesson 1: From Code to Intelligence
Students revisit how code becomes “intelligent” through algorithms. They explore linear regression — one of the simplest learning models.
Activity: Build a basic linear regression model to predict numbers.
Lesson 2: Understanding Data — The Core of AI
Introduces real-world datasets and teaches how to read, inspect, and clean data.
Activity: Load a dataset using pandas, fix missing values, and explore data visually.
Lesson 3: Supervised Learning — How AI Learns from Labels
Explains labeled data and model training using examples.
Activity: Train a classification model (e.g., classifying fruit types based on features).
Lesson 4: Feature Engineering and Data Preprocessing
Teaches the importance of preparing data for accuracy.
Activity: Normalize, scale, and split data into training and testing sets.
Lesson 5: Building Your First Predictive Model
Students construct their first end-to-end model using scikit-learn.
Activity: Build and test a decision tree classifier with visual output.
Lesson 6: Evaluating Model Performance
Introduces key evaluation metrics — accuracy, precision, recall, and confusion matrices.
Activity: Compare two models and evaluate which performs better and why.
Lesson 7: Working with Real-World Datasets
Students analyze publicly available datasets (weather, housing, or environment).
Activity: Choose a dataset and perform full cleaning, training, and testing.
Lesson 8: Overfitting and Model Improvement
Teaches what happens when AI “memorizes” instead of “learns.”
Activity: Demonstrate overfitting and fix it by adjusting training parameters.
Lesson 9: Visualization and Storytelling with Data
Shows how to interpret and present AI results visually.
Activity: Create charts and graphs that explain your model’s performance.
Lesson 10: Ethics in Machine Learning
Students explore how model bias forms and how to design ethical AI.
Activity: Discuss fairness case studies and simulate removing bias from data.
Lesson 11: Mini Project – “My Predictive AI”
Students begin designing their own predictive project — applying all previous lessons to a chosen dataset.
Activity: Build, train, test, and document your AI project.
Lesson 12: The AI Builders Advanced I Challenge
Final presentation week — students share projects, reflect on results, and discuss what they would improve.
Activity: Present model and receive peer/instructor feedback.
Final Project: “My Predictive AI”
Students build and document a functioning AI model capable of making predictions from real data.
They submit code, visuals, results, and an ethical reflection report.
Certificate Awarded
Certificate of Completion – AI Builders Advanced Course I
Includes student name, date, and verified course credential ID.
AI Builders – Advanced Course Guide II
- Target Group: Intermediate / Upper-Level AI Students
- Duration: 3 Months
- Lessons: 12 (1 per week)
- Course Type: Deep Learning & Computer Vision
- Track: AI Builders
Course Overview
The AI Builders – Advanced Course II brings students into the world of Deep Learning — the heart of modern AI.
Students learn how neural networks mimic the brain, how convolutional layers help AI “see” images, and how to train systems that recognize and interpret the world visually.
By the end of this course, students will have built and tested their first image-recognition and computer vision models, gaining practical experience with real neural architectures.
Learning Objectives
By the end of this course, students will:
Understand how neural networks process and learn complex patterns
Master the basics of TensorFlow and Keras frameworks
Learn to build, train, and optimize deep learning models
Explore convolutional neural networks (CNNs) for image recognition
Create visual AI systems with ethics and accuracy in mind
Lesson Breakdown (12 Weeks)
Lesson 1: Introduction to Deep Learning
Students learn what differentiates deep learning from traditional machine learning.
Activity: Build a simple multilayer perceptron using Keras.
Lesson 2: Neurons, Layers, and Activations
Explores how neural networks process data through layers and activation functions.
Activity: Visualize how different activations affect learning outcomes.
Lesson 3: Forward and Backward Propagation
Explains how AI learns from errors by adjusting weights (backpropagation).
Activity: Run a step-by-step simulation of how weights update in training.
Lesson 4: Introduction to TensorFlow and Keras
Hands-on setup of key libraries and frameworks used in deep learning.
Activity: Install, configure, and test your first TensorFlow model.
Lesson 5: Creating Your First Neural Network
Students build a neural network to classify simple numeric or categorical data.
Activity: Build a handwritten digit recognizer using the MNIST dataset.
Lesson 6: Understanding Convolutional Neural Networks (CNNs)
Introduces the architecture that allows AI to “see.”
Activity: Explore convolutional layers visually using image filters and pooling.
Lesson 7: Building a CNN for Image Recognition
Guided construction of a CNN that classifies real images.
Activity: Train a model to recognize objects from a small custom dataset.
Lesson 8: Improving Accuracy — Tuning and Dropout
Students learn techniques to prevent overfitting and improve accuracy.
Activity: Adjust layers and parameters to increase model performance.
Lesson 9: Visualizing and Interpreting Results
Focuses on explaining what the AI “sees.”
Activity: Create visualizations of activation maps and prediction results.
Lesson 10: Real-World Computer Vision
Applies computer vision to real use cases — such as agriculture, safety, and health.
Activity: Design an AI concept that uses vision to solve a human problem.
Lesson 11: Mini Project – “AI That Sees”
Students design and train a small vision-based AI prototype using a chosen dataset.
Activity: Build and document your image classifier.
Lesson 12: The AI Builders Advanced II Challenge
Students present their final projects, analyzing results and discussing improvements.
Activity: Showcase + ethical reflection on bias and use of visual data.
Final Project: “AI That Sees”
Students build a custom computer vision project using CNNs — demonstrating full understanding of deep learning pipelines.
They submit their trained model, dataset, and ethical use case documentation.
Certificate Awarded
Certificate of Completion – AI Builders Advanced Course II
Includes student name, credential ID, and Technology Institute seal
AI Builders – Advanced Course Guide III
- Target Group: Intermediate to Advanced AI Students
- Duration: 3 Months
- Lessons: 12 (1 per week)
- Course Type: Natural Language Processing (NLP)
- Track: AI Builders
Course Overview
The AI Builders – Advanced Course III immerses students in the world of language and meaning — how machines read, interpret, and generate human communication.
Students will explore text processing, sentiment analysis, and basic chatbot development using real-world data and libraries like NLTK, spaCy, and Hugging Face.
By the end of this course, learners will have created their first language-aware AI capable of responding intelligently to human input.
Learning Objectives
By the end of this course, students will:
Understand how machines represent and process human language
Learn key NLP techniques: tokenization, stemming, lemmatization, and vectorization
Build text classification and sentiment analysis models
Explore transformer-based models and embeddings
Create a simple chatbot that can understand and respond to text
Lesson Breakdown (12 Weeks)
Lesson 1: Introduction to Natural Language Processing
What NLP is, and why it powers nearly every AI system today.
Activity: Explore real examples of NLP — chatbots, translators, and AI writers.
Lesson 2: How Machines Read Text
Students learn tokenization — how text is broken into words and sentences for AI understanding.
Activity: Tokenize text using Python and the NLTK library.
Lesson 3: Cleaning and Preparing Text Data
Explains stemming, lemmatization, and stop-word removal to make text usable for training.
Activity: Clean a real-world text dataset (e.g., reviews or tweets).
Lesson 4: From Words to Numbers — Word Embeddings
Students discover how AI represents words as mathematical vectors.
Activity: Visualize word embeddings and semantic similarity.
Lesson 5: Building Your First Text Classifier
Students train a basic model to classify text into categories.
Activity: Create a spam detector using logistic regression or Naive Bayes.
Lesson 6: Sentiment Analysis — Reading Emotions
Explores how AI detects positive or negative tone in text.
Activity: Build a sentiment analyzer using pre-labeled movie reviews.
Lesson 7: Understanding Context — Sequence Models
Introduces Recurrent Neural Networks (RNNs) and LSTMs for handling sequences of words.
Activity: Build a simple LSTM model to predict the next word in a sentence.
Lesson 8: Introduction to Transformers
Explains the architecture behind BERT and GPT.
Activity: Use a pretrained transformer from Hugging Face for text classification.
Lesson 9: Building a Basic Chatbot
Students create a chatbot that can recognize greetings and respond contextually.
Activity: Program an interactive chatbot with rule-based and learned responses.
Lesson 10: Ethical and Responsible Language AI
Explores misinformation, bias, and harmful content in language models.
Activity: Discuss ethical boundaries for generative AI and create a “safety rule set” for bots.
Lesson 11: Mini Project – “AI That Understands”
Students choose a practical NLP application — summarizer, translator, or question-answering system — and build it using learned methods.
Activity: Develop and test a working prototype.
Lesson 12: The AI Builders Advanced III Challenge
Presentation and discussion week. Students showcase their NLP project, explaining how their AI “understands” and what ethical precautions they built in.
Activity: Final presentation + peer review.
Final Project: “AI That Understands”
Students create a functional natural language AI model capable of interpreting and responding to text-based input.
Projects may include chatbots, sentiment analyzers, or translation tools, with emphasis on transparency and ethics.
Certificate Awarded
Certificate of Completion – AI Builders Advanced Course III
Includes student name, credential ID, and Technology Institute authenticity seal
AI Builders – Advanced Course Guide IV
- Target Group: Advanced AI Developers
- Duration: 3 Months
- Lessons: 12 (1 per week)
- Course Type: Reinforcement Learning & Decision Systems
- Track: AI Builders
Course Overview
The AI Builders – Advanced Course IV introduces students to the art of creating AI that learns through trial and error.
Using reinforcement learning, students teach intelligent agents to make decisions by rewarding success and penalizing mistakes — just as humans and animals learn.
This hands-on course blends simulation environments, Python coding, and deep Q-learning techniques to empower students to design adaptive, goal-driven systems that learn and improve over time.
Learning Objectives
By the end of this course, students will:
Understand the core concepts of Reinforcement Learning (agents, environments, states, actions, rewards)
Implement classical RL algorithms such as Q-learning and Deep Q-Networks (DQN)
Use environments like OpenAI Gym to simulate AI behavior
Develop problem-solving AIs that adapt over time
Explore ethical considerations in autonomous decision-making
Lesson Breakdown (12 Weeks)
Lesson 1: What Is Reinforcement Learning?
Introduces RL as the science of decision-making. Students understand agents, environments, states, and rewards.
Activity: Create a visual map showing how an agent learns from choices and feedback.
Lesson 2: The Reward System — Teaching AI to Learn
Explains how rewards shape behavior. Students learn to define goals and measure progress through feedback loops.
Activity: Build a simple Python simulation that rewards correct moves in a grid.
Lesson 3: States, Actions, and Policies
Students explore how AI chooses actions based on its current state and stored knowledge.
Activity: Program a basic decision policy for a game-like environment.
Lesson 4: Q-Learning Fundamentals
Introduces Q-values — the backbone of reinforcement learning.
Activity: Code a basic Q-learning algorithm for a 2D navigation challenge.
Lesson 5: Exploration vs. Exploitation
Teaches the balance between trying new things and sticking to what works.
Activity: Modify your RL agent to explore new paths while optimizing rewards.
Lesson 6: Using OpenAI Gym
Students install and use OpenAI Gym, a simulation platform for RL experiments.
Activity: Run your agent inside a simple Gym environment like “FrozenLake” or “CartPole.”
Lesson 7: Deep Q-Learning (DQN)
Introduces deep learning to reinforcement learning using neural networks.
Activity: Build and train a DQN that learns from visual or numeric input.
Lesson 8: Reward Shaping and Long-Term Planning
Students learn to adjust reward functions for complex goals.
Activity: Improve your AI’s performance by designing smarter reward systems.
Lesson 9: Multi-Agent Systems
Explores environments where multiple AIs learn and compete or cooperate.
Activity: Simulate two agents learning to work together to complete a shared goal.
Lesson 10: Real-World Reinforcement Learning
Applies RL to self-driving cars, robotics, and smart environments.
Activity: Research a real-world RL success story and explain its architecture.
Lesson 11: Mini Project – “AI That Learns to Act”
Students build an RL agent that learns to master a game or task through self-training.
Activity: Design, train, and test your RL agent in a chosen environment.
Lesson 12: The AI Builders Advanced IV Challenge
Students showcase their RL project, present their design process, and discuss lessons from experimentation and ethics.
Activity: Present final project + reflection on safe autonomous systems.
Final Project: “AI That Learns to Act”
Students will build a reinforcement learning agent capable of mastering a defined environment (game, puzzle, or control system).
They will document goals, training strategy, rewards, and outcomes, with a reflection on ethical learning design.
Certificate Awarded
Certificate of Completion – AI Builders Advanced Course IV
Includes student name, credential ID, and official Technology Institute seal
AI Builders – Advanced Course Guide V
- Target Group: Expert / Capstone-Level Students
- Duration: 3 Months
- Lessons: 12 (1 per week)
- Course Type: Integration, Ethics, and Real-World Deployment
- Track: AI Builders
Course Overview
The AI Builders – Advanced Course V unites all streams of AI learning — from data science and deep learning to reinforcement and language models — into a single advanced, real-world context.
Students will design, build, and deploy working AI systems while mastering large language models (LLMs), APIs, and ethical governance frameworks.
The focus is on leadership-level development: not just how to build AI, but how to build it responsibly, transparently, and for human good.
By the end of this capstone, graduates will have built a professional-grade AI project — ready for their portfolio or startup launch.
Learning Objectives
By the end of this course, students will:
Understand how to architect, deploy, and monitor full AI systems
Learn the fundamentals of cloud deployment and API integration
Work with and fine-tune Large Language Models (LLMs)
Create hybrid systems combining vision, language, and logic
Master AI ethics, data privacy, and responsible innovation
Produce a real-world capstone project with a moral and societal impact
Lesson Breakdown (12 Weeks)
Lesson 1: The Future of AI — From Model to Mission
Students reflect on the role of AI in society and their responsibility as creators.
Activity: Write a project mission statement linking technical excellence with ethical intent.
Lesson 2: Building Real-World AI Systems
Explains system architecture — connecting front-end, model, and back-end logic.
Activity: Design an AI system diagram showing data flow, user input, and output.
Lesson 3: APIs and AI Integration
Students learn to use APIs to connect AI with external data and tools.
Activity: Build a simple API connection to send and receive AI-generated results.
Lesson 4: Introduction to Large Language Models (LLMs)
Explores GPT-style architectures, tokenization, and transformer blocks.
Activity: Use a pre-trained LLM to perform a custom text generation task.
Lesson 5: Fine-Tuning and Customization
Students learn how to adapt existing models to specialized tasks.
Activity: Fine-tune a language or vision model on a small custom dataset.
Lesson 6: Combining AI Skills — Vision + Language + Logic
Integrates previous knowledge (NLP, CV, RL) into cohesive systems.
Activity: Design a multi-modal AI — for example, an AI that describes images or answers visual questions.
Lesson 7: AI Deployment and the Cloud
Covers deployment using platforms like Hugging Face, Google Cloud, or Streamlit.
Activity: Deploy a small interactive model online for user access.
Lesson 8: Monitoring, Security, and Scalability
Students learn how to maintain and secure AI systems post-deployment.
Activity: Set up logging, monitoring, and data security practices for a model.
Lesson 9: The Ethics of Power — AI and Humanity
Explores bias, privacy, misinformation, and moral design frameworks.
Activity: Conduct an ethics review of your capstone project using Technology Institute’s Moral AI Checklist.
Lesson 10: The Capstone Project — Design Phase
Students plan their final integrated project.
Activity: Submit full project proposal including concept, technical plan, and moral rationale.
Lesson 11: The Capstone Project — Build Phase
Students implement their AI project using all previous training.
Activity: Develop, test, and refine your full working AI system.
Lesson 12: The AI Builders Final Challenge — Global Impact
Final presentations and defense. Students showcase their AI, demonstrate function, and present its moral and social purpose.
Activity: Capstone showcase + peer and faculty review.
Final Project: “AI for Humanity”
Each student builds and deploys a complete AI system with a measurable social benefit — such as improving accessibility, education, health, sustainability, or public good.
They must demonstrate technical excellence and moral design alignment.
Certificate Awarded
Certificate of Mastery – AI Builders Advanced Course V
Includes student name, credential ID, and official Technology Institute Seal of Ethical Innovation.
Graduation Outcome
Students completing this course receive recognition as Certified AI Builders — fully capable of designing, developing, and deploying ethical AI solutions.
They are now prepared to enter professional AI fields or launch their own ventures with mastery, leadership, and moral integrity.
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