Application 201: Artificial Intelligence & ChatBots

• 1

  Section 1: Introduction

  • Lesson 1: Welcome to Your Learning Journey Free Preview
  • Lesson 2: Programming as Philosophy Free Preview
  • Lesson 3: How Your Learning Journey Unfolds
  • Lesson 4: Why We Choose Thonny for Learning Python
  • Lesson 5: Thonny Python IDE Installation Guide
  • Lesson 6: Important Information About This Technical Guide
• 2

  Section 2: Introduction to NLP and Development Environment

  • Lesson 1: Introduction to Natural Language Processing
  • Lesson 2: Understanding Computational Approaches to Language Processing
  • Lesson 3: Development Environment Setup
  • Lesson 4: Introduction to Computational Linguistics
  • Lesson 5: Environment Configuration Practice
  • Lesson 6: First NLP Exploration
  • Lesson 7: Project Foundation
• 3

  Section 3: JSON Foundations for NLP Data

  • Lesson 1: What is JSON and Why NLP Uses It
  • Lesson 2: Basic JSON Syntax
  • Lesson 3: Reading JSON Files in Python
  • Lesson 4: Creating Simple JSON Structure
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  Section 4: Dataset Creation and Structure

  • Lesson 1: Understanding Intents in Conversational AI
  • Lesson 2: Patterns vs. Responses Structure
  • Lesson 3: JSON Data Format for NLP
  • Lesson 4: Data Quality Principles
  • Lesson 5: Creating Your First Intents Dataset
  • Lesson 6: Dataset Expansion Techniques
  • Lesson 7: Quality Assurance Process
  • Lesson 8: Exploring Existing Datasets
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  Section 5: Text Preprocessing and NLTK Fundamentals

  • Lesson 1: The Need for Text Preprocessing
  • Lesson 2: Tokenization Fundamentals
  • Lesson 3: Lemmatization Process
  • Lesson 4: Text Normalization Techniques
  • Lesson 5: Building a Text Preprocessing Pipeline
  • Lesson 6: NLTK Library Integration
  • Lesson 7: Preprocessing Function Development
  • Lesson 8: Systematic Debugging and Testing
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  Section 6: Feature Engineering and Bag-of-Words Model

  • Lesson 1: The Text-to-Numbers Challenge
  • Lesson 2: Bag-of-Words Model Concept
  • Lesson 3: Vocabulary Creation Process
  • Lesson 4: Word Frequency Analysis
  • Lesson 5: Manual Bag-of-Words Implementation
  • Lesson 6: Vocabulary Analysis Tools
  • Lesson 7: Feature Vector Creation
  • Lesson 8: Debugging and Validation
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  Section 7: Automated Feature Extraction with Scikit-learn

  • Lesson 1: Introduction to Scikit-learn for NLP
  • Lesson 2: CountVectorizer Fundamentals
  • Lesson 3: Professional vs. Manual Implementation
  • Lesson 4: Feature Matrix Creation
  • Lesson 5: Parameter Exploration and Tuning
  • Lesson 6: Feature Matrix Analysis
  • Lesson 7: Pipeline Integration
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  Section 8: Label Encoding and Training Data Preparation

  • Lesson 1: Understanding Labels in Supervised Learning
  • Lesson 2: Label Encoding Fundamentals
  • Lesson 3: Training Data Assembly
  • Lesson 4: Data Quality Assurance
  • Lesson 5: Implementing Label Encoding
  • Lesson 6: Training Data Pipeline Creation
  • Lesson 7: Dataset Assembly and Validation
  • Lesson 8: Complete Data Pipeline Integration
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  Section 9: First Mid-Term

  • First Mid-Term
• 10

  Section 10: Supervised Learning and Classifier Training

  • Lesson 1: Supervised Learning Fundamentals
  • Lesson 2: Classification vs. Other ML Tasks
  • Lesson 3: Naive Bayes Classifier Concepts
  • Lesson 4: Training Process Understanding
  • Lesson 5: Implementing Naive Bayes Training
  • Lesson 6: First Chatbot Predictions
  • Lesson 7: Training Validation and Testing
  • Lesson 8: End-to-End Prediction Pipeline
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  Section 11: Model Evaluation and Performance Analysis

  • Lesson 1: Why Model Evaluation is Critical
  • Lesson 2: Training vs. Testing Data Split
  • Lesson 3: Core Evaluation Metrics
  • Lesson 4: Confusion Matrix Fundamentals
  • Lesson 5: Implementing Data Splitting
  • Lesson 6: Computing Performance Metrics
  • Lesson 7: Creating Confusion Matrix Visualizations
  • Lesson 8: Systematic Performance Analysis
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  Section 12: Algorithm Comparison and Optimization

  • Lesson 1: Why Algorithm Comparison Matters
  • Lesson 2: Logistic Regression Fundamentals
  • Lesson 3: Algorithm Selection Principles
  • Lesson 4: Systematic Comparison Methodology
  • Lesson 5: Implementing Logistic Regression
  • Lesson 6: Side-by-Side Performance Comparison
  • Lesson 7: Hyperparameter Tuning Introduction
  • Lesson 8: Algorithm Selection Decision Making
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  Section 13: Prediction Pipeline and User Input Processing

  • Lesson 1: End-to-End Prediction Systems
  • Lesson 2: User Input Variability and Challenges
  • Lesson 3: Pipeline Modularity and Reusability
  • Lesson 4: Prediction Confidence and Reliability
  • Lesson 5: Building the Complete Prediction Pipeline
  • Lesson 6: Robust Input Processing Implementation
  • Lesson 7: Modular Function Design
  • Lesson 8: Real-Time Prediction Testing
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  Section 14: Response Generation and Conversation Logic

  • Lesson 1: Intent-to-Response Mapping
  • Lesson 2: Confidence-Based Decision Making
  • Lesson 3: Response Variation and Personality
  • Lesson 4: Fallback and Error Handling Strategies
  • Lesson 5: Implementing Response Selection Logic
  • Lesson 6: Confidence Threshold Optimization
  • Lesson 7: Fallback Response System Development
  • Lesson 8: Complete Conversation Logic Integration
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  Section 15: Interactive Interface Development

  • Lesson 1: User Interface Design Principles
  • Lesson 2: Conversation Loop Architecture
  • Lesson 3: Interactive System Requirements
  • Lesson 4: Command-Line Interface Fundamentals
  • Lesson 5: Building Command-Line Conversation Interface
  • Lesson 6: Session Management Implementation
  • Lesson 7: User Experience Enhancement
  • Lesson 8: Interactive Testing and Debugging
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  Section 16: Model Enhancement and Robustness

  • Lesson 1: Model Robustness Concepts
  • Lesson 2: Data Augmentation Principles
  • Lesson 3: Input Validation and Sanitization
  • Lesson 4: Error Handling and Recovery Strategies
  • Lesson 5: Data Augmentation Implementation
  • Lesson 6: Spelling and Typo Handling
  • Lesson 7: Input Validation and Sanitization Systems
  • Lesson 8: Systematic Testing Protocol Development
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  Section 17: Advanced Features and Integration

  • Lesson 1: Conversation Context and Memory
  • Lesson 2: Advanced Conversational AI Concepts
  • Lesson 3: System Integration Principles
  • Lesson 4: User Interface Evolution
  • Lesson 5: Implementing Conversation Context Features
  • Lesson 6: Simple GUI Development (Optional)
  • Lesson 7: Web Integration Foundations
  • Lesson 8: Performance Optimization and Scalability
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  Section 19: Second Mid-Term

  • Second Mid-Term
• 20

  Section 20: Final Exam

  • Final Exam
• 21

  Section 7.5: Bridge Section Lesson 4 → Lesson 10

  • Lesson 1: Speed Run - From Features to Working Chatbot

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