This program provides a comprehensive understanding of AI and machine learning, including supervised and unsupervised learning, computer vision, and natural language processing.
Get ready to embark on an exciting learning journey
Hello Future AI Innovator! :emoji:
Are you ready to embark on an incredible journey into the world of Artificial Intelligence? You're about to discover how machines can learn, see, understand language, and even recognize faces - just like in your favorite sci-fi movies! This program will transform you from a curious learner into an AI creator who can build amazing projects that solve real-world problems.
🚀 Start Here
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📚 Foundation (Lessons 1-4)
→ Introduction to AI & Machine Learning
→ Understanding How Machines Learn
→ Mastering Data Preparation
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🔨 Build Your First Projects (Lessons 5-6)
→ Predict Instagram Reach
→ Solve the Titanic Mystery
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🧩 Advanced Techniques (Lessons 7-10)
→ Customer Segmentation
→ Natural Language Processing
→ Create Your Own Chatbot
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👁️ Computer Vision Mastery (Lessons 13-18)
→ Make Computers "See"
→ Face Detection
→ Object Recognition with YOLO
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🏆 Final Project & Graduation (Lessons 21-24)
→ Design Your Own AI Solution
→ Present Your Innovation
→ Celebrate Your Achievement!
Before starting this exciting journey, make sure you have:
Don't worry if you're not an expert programmer yet - we'll guide you every step of the way! :muscle:
This comprehensive program is designed to introduce students to the fascinating world of Artificial Intelligence and its practical applications. Through a carefully structured curriculum, students will progress from fundamental AI concepts to advanced machine learning techniques, computer vision, and natural language processing, culminating in real-world AI projects that demonstrate their acquired expertise.
This isn't just another course - it's your gateway to becoming an AI creator! Here's what makes our program special:
| Lesson | Topic | Objectives | Notes/File Link | Activities Link | Exit Ticket | Submission Link |
|---|---|---|---|---|---|---|
| 1 | Introduction to AI | • Understanding the definition of AI and its techniques• Learning about the history of AI and AI winters• Exploring AI tools in daily life• Understanding AI ethics, benefits, and limitations | Introduction to AI | L1 Exercise (Intro to AI) | ET-1 | Submit |
| 2 | Machine Learning + Supervised and Unsupervised Machine Learning | • Understanding Machine Learning definitions and advantages/disadvantages• Exploring Big Data and its role in ML• Learning different types of ML algorithms and categories• Distinguishing between Machine Learning and Robotics• Understanding Supervised vs Unsupervised Learning approaches | Machine Learning + Supervised & Unsupervised Learning | L2 Exercise (Machine Learning)L2 Exercise (Supervised & Unsupervised ML) | ET-2ET-3 | Submit |
| 3 | Machine Learning Process + Data Preparation | • Understanding key procedures in ML process• Learning data preparation techniques• Model training and testing methodologies• Introduction to scikit-learn for ML• Data preprocessing and dataset splitting techniques | Machine Learning Process + Data Preparation | L3 Setup Up Google Colab FolderL3 Exercise (Machine Learning Process)L3 Exercise (Data Preparation) | ET-4ET-5 | Submit |
| 4 | Regression + Classification | • Understanding regression concepts and linear regression• Implementing simple and multiple linear regression models• Understanding classification problems and algorithms• Exploring K-Nearest Neighbors (KNN), Decision Trees, and Naive Bayes• Implementing classification models• Comparing regression vs classification approaches | Regression + Classification | L4 Exercise (Regression)L4 Exercise (Classification) | ET-6ET-7 | Submit |
| 5 | Project One: Instagram Reach Analysis | • Practical application: Instagram engagement rate prediction• Hands-on project with real social media data• Applying regression and classification techniques• Data analysis and model evaluation | Project One: Instagram Reach Analysis | L5 Instagram Reach Analysis Project | Project-1 | Submit |
| 6 | Project 2: Titanic Survivors Classification | • Historical context of the Titanic incident• Applying classification algorithms to predict survival• Comparing KNN, Decision Tree, and Naive Bayes performance• Real-world data analysis and model evaluation | Project 2: Titanic Survivors Classification | L6 Titanic Survivors Classification Project | Project-2 | Submit |
| 7 | Clustering | • Understanding unsupervised learning and clustering concepts• Exploring K-Means Clustering algorithm• Learning cluster formation and optimization• Selecting optimal number of clusters | Clustering | L7 Exercise (Clustering) | ET-8 | Submit |
| 8 | Project 3: Mall Customer Segmentation Project | • Understanding customer behavior analysis• Applying K-Means clustering for customer segmentation• Marketing applications of clustering results• Business insights from clustering analysis | Project 3: Mall Customer Segmentation Project | L8 Mall Customer Segmentation Project | Project-3 | Submit |
| 9 | Natural Language Processing (NLP) + Text Processing in NLP | • Understanding Natural Language Processing fundamentals• Exploring NLP applications and techniques• Learning sentiment analysis concepts• Text preprocessing and feature extraction methods• Converting text to numerical representations | Natural Language Processing (NLP) + Text Processing | L9 Exercise (NLP)L9 Exercise (Text Processing) | ET-9ET-10 | Submit |
| 10 | Project 4: Chatbot | • Understanding intents dataset structure• Building conversational AI systems• Applying NLP and text processing in chatbot development• Creating engaging conversational experiences | Project 4: Chatbot | L10 Chatbot Project | Project-4 | Submit |
| 11 | Streamlit | • Understanding Streamlit framework for AI applications• Building interactive user interfaces• Creating web applications for AI models• Streamlit user input and UI design | Streamlit | L11 Setup Python InterpreterL11 Setup VSCodeL11 Exercise (Streamlit) | ET-11 | Submit |
| 12 | Quiz 1 | • Comprehensive assessment of AI and ML fundamentals• Evaluation of concepts from Lessons 1-11 | Quiz 1 - Revision | L12 Quiz 1 | - | - |
| 13 | Computer Vision + OpenCV | • Understanding computer vision fundamentals• Learning OpenCV library for image processing• Exploring computer vision applications• Image reading and processing techniques | Computer Vision + OpenCV | L13 Exercise (Computer Vision)L13 Exercise (OpenCV) | ET-12ET-13 | Submit |
| 14 | Image Processing with OpenCV + Drawing with OpenCV | • Advanced image processing techniques• Learning to draw and annotate images with OpenCV• Understanding image manipulation for AI applications• Preparing images for computer vision tasks | Image Processing with OpenCV + Drawing with OpenCV | L14 Exercise (Image Processing)L14 Exercise (Drawing with OpenCV) | ET-14ET-15 | Submit |
| 15 | Object Detection using Haar Cascades Classifier | • Understanding Haar features and cascades• Implementing face detection algorithms | Object Detection using Haar Cascades Classifier | L15 Face Detector Project (Photo)L15 Face Detector Project (Webcam) | ET-16 | Submit |
| 16 | Project 5: Face Detector | • Building face detection systems for photos and webcams• Exploring real-time face recognition applications | Project 5: Face Detector | L15 Face Detector Project (Photo)L15 Face Detector Project (Webcam) | Project-5 | Submit |
| 17 | Object Detection with YOLO Algorithm | • Understanding modern object detection techniques• Learning YOLO (You Only Look Once) algorithm• Comparing YOLO with traditional detection methods• Implementing YOLOv8 for object recognition | Object Detection with YOLO Algorithm | L17 Exercise (YOLO Algorithm) | ET-17 | Submit |
| 18 | Project 6: Custom Object Detection with YOLOv8 | • Building custom object detection models• Training models for specific use cases (rock-paper-scissors detector)• Understanding custom dataset preparation• Testing and validating custom detection models | Project 6: Custom Object Detection with YOLOv8 | L18 Custom Object Detection with YOLOv8 project | Project-6 | Submit |
| 19 | Deep Learning + Image Classification | • Understanding deep learning fundamentals• Exploring deep learning frameworks• Distinguishing AI, ML, and DL relationships• Building image classification projects using Teachable Machine• Shape classification practical implementation | Deep Learning + Image Classification | L18 Shape Classification Project | ET-17 | Submit |
| 20 | Quiz 2 | • Comprehensive assessment of computer vision and deep learning• Evaluation of concepts from Lessons 13-18 | Quiz 2 - Revision | L19 Quiz 2 | - | - |
| 21 | Final Project Proposal | • Project planning and proposal development• Applying previous lessons to design comprehensive AI projects• Research and problem definition | Final Project Proposal | - | - | - |
| 22 | Final Project Prototype | • Building functional AI project prototypes• Implementing core features and algorithms• Testing and iterating on project components | Final Project Prototype | - | - | - |
| 23 | Final Project Presentation Preparation | • Preparing comprehensive project presentations• Documenting project development process• Creating effective demonstrations of AI applications | Final Project Presentation Preparation | - | Project-7 | Submit |
| 24 | Graduation | • Project evaluation• Course completion | Download Graduation Background | Complete Feedback Form | Submit Testimonial | - |
By the end of this amazing journey, you'll be able to:
Remember, every AI expert started exactly where you are now. With dedication, curiosity, and this comprehensive program, you'll be building incredible AI applications in no time!
Let's start this amazing adventure together! :rocket:
P.S. Don't forget to celebrate your progress along the way - every lesson completed is a step closer to becoming an AI innovator!
What you'll achieve by the end of this course
Implement generative AI models
Build creative AI applications
Work with cutting-edge AI models
Build innovative AI solutions