🤖 AI Masterclass *coming soon
- 1. 1.1 – What Artificial Intelligence Really Is (and Isn’t) 00:00:00
- 2. 1.2 – History of AI: From Logic Machines to Neural Networks 00:00:00
- 3. 1.3 – Core AI Categories: Narrow, General, and Superintelligence 00:00:00
- 4. 1.4 – The Difference Between AI, Machine Learning, and Deep Learning 00:00:00
- 5. 1.5 – Key AI Terms Every Beginner Must Know 00:00:00
- 6. 1.6 – Understanding Data: The Fuel for AI 00:00:00
- 7. 1.7 – How AI Learns: Algorithms and Models Explained Simply 00:00:00
- 8. 1.8 – Why Big Data Matters for AI Accuracy 00:00:00
- 9. 1.9 – Introduction to Neural Networks 00:00:00
- 10. 1.10 – Activation Functions: The Brain Signals of AI 00:00:00
- 11. 1.11 – What Makes AI “Smart”? Pattern Recognition Basics 00:00:00
- 12. 1.12 – The Role of Mathematics in AI 00:00:00
- 13. 1.13 – Understanding Linear Algebra for AI Applications 00:00:00
- 14. 1.14 – Probability & Statistics in AI Predictions 00:00:00
- 15. 1.15 – How AI Uses Logic to Make Decisions 00:00:00
- 16. 1.16 – The Training Process: Feeding Data to AI Models 00:00:00
- 17. 1.17 – Bias in AI: How It Happens and Why It’s Dangerous 00:00:00
- 18. 1.18 – Common AI Myths Debunked 00:00:00
- 19. 1.19 – AI vs. Human Intelligence: A Comparison 00:00:00
- 20. 1.20 – The Role of Cloud Computing in AI 00:00:00
- 21. 1.21 – Understanding AI Pipelines and Workflows 00:00:00
- 22. 1.22 – How AI Models Are Stored and Retrieved 00:00:00
- 23. 1.23 – The Importance of Clean Data 00:00:00
- 24. 1.24 – What is Overfitting and Underfitting? 00:00:00
- 25. 1.25 – AI’s Relationship with the Internet of Things (IoT) 00:00:00
- 26. 1.26 – Open-Source AI vs. Proprietary AI Systems 00:00:00
- 27. 1.27 – Understanding AI APIs and How They Work 00:00:00
- 28. 1.28 – How AI Processes Natural Language 00:00:00
- 29. 1.29 – Machine Vision: How AI Sees Images 00:00:00
- 30. 1.30 – Reinforcement Learning Basics 00:00:00
- 31. 1.31 – AI in Everyday Life: Hidden Uses Around You 00:00:00
- 32. 1.32 – AI’s Role in Predictive Analytics 00:00:00
- 33. 1.33 – Introduction to AI Ethics and Safety 00:00:00
- 34. 1.34 – AI’s Impact on Different Industries 00:00:00
- 35. 1.35 – Common AI Tools and Platforms for Beginners 00:00:00
- 36. 1.36 – The Cost of Building AI Models 00:00:00
- 37. 1.37 – How AI Models Are Updated and Improved 00:00:00
- 38. 1.38 – Edge AI: Running AI on Small Devices 00:00:00
- 39. 1.39 – Understanding AI Benchmarks and Performance 00:00:00
- 40. 1.40 – AI Model Deployment in Real-World Systems 00:00:00
- 41. 1.41 – The Role of Data Annotation in AI 00:00:00
- 42. 1.42 – Understanding AI Scalability 00:00:00
- 43. 1.43 – AI for Accessibility and Inclusion 00:00:00
- 44. 1.44 – Understanding Tokenization in Language Models 00:00:00
- 45. 1.45 – Transfer Learning Basics 00:00:00
- 46. 1.46 – AI Research Papers: How to Read and Understand Them 00:00:00
- 47. 1.47 – Current AI Limitations and Challenges 00:00:00
- 48. 1.48 – The Future of AI: Trends and Predictions 00:00:00
- 49. 1.49 – How to Choose Your AI Learning Path 00:00:00
- 50. 1.50 – Building Your First Simple AI Model 00:00:00
- 1. 2.1 – What is Machine Learning? 00:00:00
- 2. 2.2 – Supervised vs. Unsupervised Learning 00:00:00
- 3. 2.3 – Introduction to Reinforcement Learning 00:00:00
- 4. 2.4 – How to Prepare Data for Machine Learning 00:00:00
- 5. 2.5 – Data Cleaning and Preprocessing Techniques 00:00:00
- 6. 2.6 – Feature Engineering: Making Data Useful 00:00:00
- 7. 2.7 – Feature Selection: Choosing the Most Important Data 00:00:00
- 8. 2.8 – Label Encoding vs. One-Hot Encoding 00:00:00
- 9. 2.9 – Understanding Training, Validation, and Test Sets 00:00:00
- 10. 2.10 – Cross-Validation for Reliable Models 00:00:00
- 11. 2.11 – Choosing the Right Machine Learning Algorithm 00:00:00
- 12. 2.12 – Linear Regression: Predicting Continuous Values 00:00:00
- 13. 2.13 – Logistic Regression: Predicting Categories 00:00:00
- 14. 2.14 – Decision Trees and How They Work 00:00:00
- 15. 2.15 – Random Forests: Combining Multiple Trees 00:00:00
- 16. 2.16 – Gradient Boosting Machines (GBM) 00:00:00
- 17. 2.17 – XGBoost and LightGBM for Speed and Accuracy 00:00:00
- 18. 2.18 – k-Nearest Neighbors (k-NN) Algorithm 00:00:00
- 19. 2.19 – Naïve Bayes Classifier and Its Applications 00:00:00
- 20. 2.20 – Support Vector Machines (SVM) 00:00:00
- 21. 2.21 – k-Means Clustering for Grouping Data 00:00:00
- 22. 2.22 – Hierarchical Clustering Techniques 00:00:00
- 23. 2.23 – Principal Component Analysis (PCA) for Dimensionality Reduction 00:00:00
- 24. 2.24 – t-SNE for Visualizing High-Dimensional Data 00:00:00
- 25. 2.25 – Neural Networks in Machine Learning 00:00:00
- 26. 2.26 – Handling Missing Data in Datasets 00:00:00
- 27. 2.27 – Handling Imbalanced Datasets 00:00:00
- 28. 2.28 – Understanding Model Bias and Variance 00:00:00
- 29. 2.29 – Regularization Techniques (L1, L2) 00:00:00
- 30. 2.30 – Hyperparameter Tuning Basics 00:00:00
- 31. 2.31 – Grid Search vs. Random Search for Model Optimization 00:00:00
- 32. 2.32 – Automated Machine Learning (AutoML) Tools 00:00:00
- 33. 2.33 – Model Evaluation Metrics for Classification 00:00:00
- 34. 2.34 – Model Evaluation Metrics for Regression 00:00:00
- 35. 2.35 – Confusion Matrix and How to Read It 00:00:00
- 36. 2.36 – ROC Curve and AUC Score 00:00:00
- 37. 2.37 – Precision, Recall, and F1-Score 00:00:00
- 38. 2.38 – Mean Absolute Error (MAE) and RMSE 00:00:00
- 39. 2.39 – Deploying a Machine Learning Model 00:00:00
- 40. 2.40 – Saving and Loading Models in Python 00:00:00
- 41. 2.41 – Using scikit-learn for Quick Model Building 00:00:00
- 42. 2.42 – Using TensorFlow for Machine Learning 00:00:00
- 43. 2.43 – Using PyTorch for Machine Learning 00:00:00
- 44. 2.44 – Building a Machine Learning API 00:00:00
- 45. 2.45 – Monitoring Models in Production 00:00:00
- 46. 2.46 – Updating Models with New Data 00:00:00
- 47. 2.47 – Avoiding Data Leakage in Machine Learning 00:00:00
- 48. 2.48 – Case Study: Predicting Housing Prices 00:00:00
- 49. 2.49 – Case Study: Classifying Emails as Spam or Not Spam 00:00:00
- 50. 2.50 – Best Practices for Machine Learning Projects 00:00:00
- 1. 3.1 – What is Deep Learning and How It Differs from Machine Learning 00:00:00
- 2. 3.2 – The Structure of a Neural Network 00:00:00
- 3. 3.3 – Neurons, Layers, and Weights Explained 00:00:00
- 4. 3.4 – Forward Propagation: How Data Moves Through a Network 00:00:00
- 5. 3.5 – Backpropagation: How Networks Learn from Mistakes 00:00:00
- 6. 3.6 – Activation Functions and Their Roles 00:00:00
- 7. 3.7 – Sigmoid, Tanh, and ReLU Functions Compared 00:00:00
- 8. 3.8 – The Vanishing Gradient Problem 00:00:00
- 9. 3.9 – Batch Normalization for Stable Learning 00:00:00
- 10. 3.10 – Dropout for Preventing Overfitting 00:00:00
- 11. 3.11 – Initializing Weights for Faster Training 00:00:00
- 12. 3.12 – Loss Functions for Classification Models 00:00:00
- 13. 3.13 – Loss Functions for Regression Models 00:00:00
- 14. 3.14 – Optimizers: SGD, Adam, RMSprop 00:00:00
- 15. 3.15 – Learning Rate and Learning Rate Scheduling 00:00:00
- 16. 3.16 – Epochs, Batches, and Iterations 00:00:00
- 17. 3.17 – Building Your First Neural Network in TensorFlow 00:00:00
- 18. 3.18 – Building Your First Neural Network in PyTorch 00:00:00
- 19. 3.19 – Image Recognition with Convolutional Neural Networks (CNNs) 00:00:00
- 20. 3.20 – CNN Layers: Convolution, Pooling, and Fully Connected Layers 00:00:00
- 21. 3.21 – Using CNNs for Object Detection 00:00:00
- 22. 3.22 – Transfer Learning with Pretrained Models 00:00:00
- 23. 3.23 – ResNet, VGG, and Inception Architectures 00:00:00
- 24. 3.24 – Natural Language Processing with Recurrent Neural Networks (RNNs) 00:00:00
- 25. 3.25 – Long Short-Term Memory (LSTM) Networks 00:00:00
- 26. 3.26 – Gated Recurrent Units (GRUs) 00:00:00
- 27. 3.27 – Attention Mechanisms in Neural Networks 00:00:00
- 28. 3.28 – Transformers: The Architecture Behind GPT Models 00:00:00
- 29. 3.29 – Building a Simple Transformer Model 00:00:00
- 30. 3.30 – Word Embeddings: Word2Vec, GloVe, and FastText 00:00:00
- 31. 3.31 – Generating Text with Sequence Models 00:00:00
- 32. 3.32 – Neural Machine Translation 00:00:00
- 33. 3.33 – Speech Recognition with Deep Learning 00:00:00
- 34. 3.34 – Audio Processing and Spectrograms 00:00:00
- 35. 3.35 – Generative Adversarial Networks (GANs) Basics 00:00:00
- 36. 3.36 – Building a GAN for Image Generation 00:00:00
- 37. 3.37 – Variational Autoencoders (VAEs) 00:00:00
- 38. 3.38 – Combining GANs and VAEs 00:00:00
- 39. 3.39 – Reinforcement Learning with Deep Q-Networks (DQNs) 00:00:00
- 40. 3.40 – Deep Learning for Game AI 00:00:00
- 41. 3.41 – AI for Medical Image Analysis 00:00:00
- 42. 3.42 – AI for Autonomous Vehicles 00:00:00
- 43. 3.43 – Ethical Concerns in Deep Learning 00:00:00
- 44. 3.44 – Reducing Bias in Deep Learning Models 00:00:00
- 45. 3.45 – Improving Model Explainability (XAI) 00:00:00
- 46. 3.46 – Model Compression for Edge Devices 00:00:00
- 47. 3.47 – Quantization and Pruning Techniques 00:00:00
- 48. 3.48 – Case Study: Building a Handwriting Recognition Model 00:00:00
- 49. 3.49 – Case Study: Chatbot with Deep Learning 00:00:00
- 50. 3.50 – Building a Complete Deep Learning Project from Scratch 00:00:00
- 1. 4.1 – What is Generative AI and How It Works 00:00:00
- 2. 4.2 – The Rise of GPT and Transformer Models 00:00:00
- 3. 4.3 – Understanding Large Language Models (LLMs) 00:00:00
- 4. 4.4 – How AI Generates Human-Like Text 00:00:00
- 5. 4.5 – The Role of Training Data in Generative AI 00:00:00
- 6. 4.6 – Tokenization and How AI Understands Words 00:00:00
- 7. 4.7 – Temperature, Top-k, and Top-p in Text Generation 00:00:00
- 8. 4.8 – Fine-Tuning vs. Pretraining in LLMs 00:00:00
- 9. 4.9 – Zero-Shot, One-Shot, and Few-Shot Learning 00:00:00
- 10. 4.10 – Building a Custom AI Model with OpenAI API 00:00:00
- 11. 4.11 – Using Hugging Face Transformers 00:00:00
- 12. 4.12 – LangChain for LLM Application Development 00:00:00
- 13. 4.13 – Chain-of-Thought Prompting for Better Reasoning 00:00:00
- 14. 4.14 – Role Prompting and Instruction Tuning 00:00:00
- 15. 4.15 – Structured Output Prompts for JSON or Tables 00:00:00
- 16. 4.16 – Using System, User, and Assistant Roles in Chat Models 00:00:00
- 17. 4.17 – Prompt Templates for Repeatable Results 00:00:00
- 18. 4.18 – Multi-Turn Prompting Strategies 00:00:00
- 19. 4.19 – Embeddings and Vector Databases for AI Memory 00:00:00
- 20. 4.20 – Retrieval-Augmented Generation (RAG) Basics 00:00:00
- 21. 4.21 – Connecting AI to External Data Sources 00:00:00
- 22. 4.22 – Summarization with LLMs 00:00:00
- 23. 4.23 – Text Classification with LLMs 00:00:00
- 24. 4.24 – Question-Answering Systems with LLMs 00:00:00
- 25. 4.25 – Building Chatbots for Websites 00:00:00
- 26. 4.26 – Voice Assistants Powered by Generative AI 00:00:00
- 27. 4.27 – Generating Images with AI (Stable Diffusion, Midjourney, DALL·E) 00:00:00
- 28. 4.28 – Generating Music and Audio with AI Tools 00:00:00
- 29. 4.29 – Generating Video with AI (Runway, Pika, Synthesia) 00:00:00
- 30. 4.30 – Combining Text and Image Generation 00:00:00
- 31. 4.31 – Storytelling with Generative AI 00:00:00
- 32. 4.32 – Creative Writing Prompts for AI 00:00:00
- 33. 4.33 – Code Generation with AI (Copilot, Code Llama) 00:00:00
- 34. 4.34 – AI-Assisted Data Analysis 00:00:00
- 35. 4.35 – AI in Marketing and Copywriting 00:00:00
- 36. 4.36 – Generating Educational Content with AI 00:00:00
- 37. 4.37 – AI for Game Storyline Generation 00:00:00
- 38. 4.38 – Multi-Modal AI: Combining Text, Image, and Audio 00:00:00
- 39. 4.39 – The Ethics of Generative AI 00:00:00
- 40. 4.40 – Avoiding Hallucinations in AI Responses 00:00:00
- 41. 4.41 – Detecting AI-Generated Content 00:00:00
- 42. 4.42 – Watermarking and Authenticating AI Content 00:00:00
- 43. 4.43 – Fine-Tuning AI for Industry-Specific Needs 00:00:00
- 44. 4.44 – Building a Personal AI Assistant 00:00:00
- 45. 4.45 – Automating Workflows with AI 00:00:00
- 46. 4.46 – Using Plugins and Extensions for AI Models 00:00:00
- 47. 4.47 – Scaling Generative AI Applications 00:00:00
- 48. 4.48 – Case Study: AI-Generated E-Commerce Store Content 00:00:00
- 49. 4.49 – Case Study: AI-Generated Social Media Campaign 00:00:00
- 50. 4.50 – Future Trends in Generative AI 00:00:00
- 1. 5.1 – Introduction to AI Deployment 00:00:00
- 2. 5.2 – Choosing the Right Environment: Cloud, Edge, or On-Premise 00:00:00
- 3. 5.3 – Understanding AI Deployment Pipelines 00:00:00
- 4. 5.4 – Packaging Models for Deployment 00:00:00
- 5. 5.5 – Using Docker for AI Model Deployment 00:00:00
- 6. 5.6 – Deploying AI with Kubernetes for Scalability 00:00:00
- 7. 5.7 – Model Hosting Services (AWS Sagemaker, Azure ML, Google Vertex AI) 00:00:00
- 8. 5.8 – Serverless AI Deployments 00:00:00
- 9. 5.9 – Integrating AI into Web Applications 00:00:00
- 10. 5.10 – Integrating AI into Mobile Apps 00:00:00
- 11. 5.11 – Using APIs for AI Model Access 00:00:00
- 12. 5.12 – REST vs. GraphQL for AI Applications 00:00:00
- 13. 5.13 – Batch Processing vs. Real-Time Inference 00:00:00
- 14. 5.14 – Managing Model Versions in Production 00:00:00
- 15. 5.15 – Continuous Integration/Continuous Deployment (CI/CD) for AI 00:00:00
- 16. 5.16 – Monitoring AI Models in Production 00:00:00
- 17. 5.17 – Tracking Model Performance Over Time 00:00:00
- 18. 5.18 – Logging and Observability for AI Applications 00:00:00
- 19. 5.19 – Handling Model Drift and Data Drift 00:00:00
- 20. 5.20 – Automating Model Retraining 00:00:00
- 21. 5.21 – A/B Testing for AI Models 00:00:00
- 22. 5.22 – Load Testing for AI Applications 00:00:00
- 23. 5.23 – Scaling AI Applications to Handle Millions of Requests 00:00:00
- 24. 5.24 – Cost Optimization for AI in the Cloud 00:00:00
- 25. 5.25 – Security Best Practices for AI Deployments 00:00:00
- 26. 5.26 – Protecting AI APIs from Abuse 00:00:00
- 27. 5.27 – Data Privacy Considerations in AI Deployment 00:00:00
- 28. 5.28 – Ensuring Compliance with GDPR, HIPAA, and Other Laws 00:00:00
- 29. 5.29 – Model Explainability in Production 00:00:00
- 30. 5.30 – Building a User Feedback Loop for AI Systems 00:00:00
- 31. 5.31 – Human-in-the-Loop (HITL) Systems for AI Oversight 00:00:00
- 32. 5.32 – Failover and Redundancy in AI Applications 00:00:00
- 33. 5.33 – Offline and Edge AI for Low-Connectivity Environments 00:00:00
- 34. 5.34 – Optimizing Models for Mobile Devices 00:00:00
- 35. 5.35 – Quantization for Lightweight Deployment 00:00:00
- 36. 5.36 – Pruning Models to Reduce Size and Cost 00:00:00
- 37. 5.37 – Federated Learning for Privacy-Preserving AI 00:00:00
- 38. 5.38 – Multi-Cloud AI Deployment Strategies 00:00:00
- 39. 5.39 – Using Content Delivery Networks (CDNs) for AI 00:00:00
- 40. 5.40 – AI Deployment Case Study: E-Commerce Recommendation Engine 00:00:00
- 41. 5.41 – AI Deployment Case Study: Chatbot in a Banking App 00:00:00
- 42. 5.42 – AI Deployment Case Study: Predictive Maintenance for Factories 00:00:00
- 43. 5.43 – Troubleshooting AI Deployment Failures 00:00:00
- 44. 5.44 – Optimizing Latency for AI Applications 00:00:00
- 45. 5.45 – Setting Up Auto-Scaling for AI Services 00:00:00
- 46. 5.46 – Integrating AI with IoT Devices 00:00:00
- 47. 5.47 – Disaster Recovery for AI Systems 00:00:00
- 48. 5.48 – Documenting AI Systems for Teams and Users 00:00:00
- 49. 5.49 – Handover and Knowledge Transfer in AI Projects 00:00:00
- 50. 5.50 – Building an End-to-End AI System from Development to Deployment 00:00:00
- 1. 6.1 – Why AI Ethics Matters in Every Project 00:00:00
- 2. 6.2 – Understanding Ethical AI Principles 00:00:00
- 3. 6.3 – Transparency and Explainability in AI Systems 00:00:00
- 4. 6.4 – Fairness and Avoiding Discrimination in AI 00:00:00
- 5. 6.5 – Bias in Training Data: Causes and Solutions 00:00:00
- 6. 6.6 – Accountability in AI Development 00:00:00
- 7. 6.7 – The Role of Human Oversight in AI 00:00:00
- 8. 6.8 – AI and the Right to Privacy 00:00:00
- 9. 6.9 – Consent and Data Collection in AI Projects 00:00:00
- 10. 6.10 – Avoiding Harm Through AI Design 00:00:00
- 11. 6.11 – AI and Human Rights Considerations 00:00:00
- 12. 6.12 – The Impact of AI on Employment and Workforce 00:00:00
- 13. 6.13 – AI in Law Enforcement: Risks and Safeguards 00:00:00
- 14. 6.14 – AI in Healthcare: Ethics and Safety Protocols 00:00:00
- 15. 6.15 – Ethical Issues in Autonomous Vehicles 00:00:00
- 16. 6.16 – AI in Education: Ensuring Fair Learning Tools 00:00:00
- 17. 6.17 – The Challenge of Deepfakes and Synthetic Media 00:00:00
- 18. 6.18 – Watermarking and Detecting AI-Generated Content 00:00:00
- 19. 6.19 – Intellectual Property in AI-Created Works 00:00:00
- 20. 6.20 – Open Source vs. Proprietary AI Ethics 00:00:00
- 21. 6.21 – Ethical Considerations in AI-Driven Marketing 00:00:00
- 22. 6.22 – Informed Consent in AI-Driven Decisions 00:00:00
- 23. 6.23 – Algorithmic Accountability Laws 00:00:00
- 24. 6.24 – Global AI Governance Initiatives 00:00:00
- 25. 6.25 – The EU AI Act and Its Implications 00:00:00
- 26. 6.26 – U.S. Federal and State AI Regulations 00:00:00
- 27. 6.27 – AI Standards from ISO and IEEE 00:00:00
- 28. 6.28 – Cross-Border Data Transfer and AI Compliance 00:00:00
- 29. 6.29 – Cybersecurity Requirements for AI Systems 00:00:00
- 30. 6.30 – Handling Sensitive Personal Data in AI 00:00:00
- 31. 6.31 – Whistleblowing in AI Misuse Cases 00:00:00
- 32. 6.32 – AI Ethics Committees in Organizations 00:00:00
- 33. 6.33 – Risk Assessment Frameworks for AI Projects 00:00:00
- 34. 6.34 – Auditing AI Models for Bias and Performance 00:00:00
- 35. 6.35 – Red Teaming and Testing AI for Harmful Outputs 00:00:00
- 36. 6.36 – Mitigating AI Hallucinations in Language Models 00:00:00
- 37. 6.37 – Preventing Manipulation and Misinformation via AI 00:00:00
- 38. 6.38 – Psychological and Social Impacts of AI 00:00:00
- 39. 6.39 – AI and Child Safety Regulations 00:00:00
- 40. 6.40 – Military Use of AI: Ethics and Controls 00:00:00
- 41. 6.41 – Environmental Impact of AI Training and Deployment 00:00:00
- 42. 6.42 – Sustainable AI Development Practices 00:00:00
- 43. 6.43 – Industry Self-Regulation in AI Ethics 00:00:00
- 44. 6.44 – Case Study: AI Bias in Hiring Systems 00:00:00
- 45. 6.45 – Case Study: Facial Recognition Controversies 00:00:00
- 46. 6.46 – Case Study: Predictive Policing Risks 00:00:00
- 47. 6.47 – Building Ethical Guidelines for Your AI Team 00:00:00
- 48. 6.48 – Training Developers on AI Ethics and Law 00:00:00
- 49. 6.49 – The Future of AI Governance Globally 00:00:00
- 50. 6.50 – Creating a Responsible AI Action Plan 00:00:00
- 1. 7.1 – How AI is Reshaping the Global Economy 00:00:00
- 2. 7.2 – Identifying Profitable AI Business Models 00:00:00
- 3. 7.3 – AI Startups vs. AI-Enabled Businesses 00:00:00
- 4. 7.4 – Building an AI Product from Scratch 00:00:00
- 5. 7.5 – Monetizing AI Through Subscription Models 00:00:00
- 6. 7.6 – Monetizing AI Through API Access 00:00:00
- 7. 7.7 – AI as a Service (AIaaS) Platforms 00:00:00
- 8. 7.8 – White-Labeling AI Solutions for Clients 00:00:00
- 9. 7.9 – Building AI Consulting Services 00:00:00
- 10. 7.10 – Selling AI-Powered SaaS Applications 00:00:00
- 11. 7.11 – Using AI for E-Commerce Optimization 00:00:00
- 12. 7.12 – AI in Marketing: Personalization at Scale 00:00:00
- 13. 7.13 – Using AI for Predictive Customer Analytics 00:00:00
- 14. 7.14 – Automating Customer Support with AI 00:00:00
- 15. 7.15 – AI in Real Estate Market Predictions 00:00:00
- 16. 7.16 – AI in Financial Trading and Risk Management 00:00:00
- 17. 7.17 – AI in Manufacturing for Cost Reduction 00:00:00
- 18. 7.18 – AI for Supply Chain Optimization 00:00:00
- 19. 7.19 – AI in Healthcare Diagnostics and Operations 00:00:00
- 20. 7.20 – AI in Education Platforms and EdTech 00:00:00
- 21. 7.21 – Licensing Your AI Models to Other Businesses 00:00:00
- 22. 7.22 – Creating AI-Powered Mobile Apps 00:00:00
- 23. 7.23 – AI-Generated Content Businesses 00:00:00
- 24. 7.24 – AI in Gaming and Virtual Worlds 00:00:00
- 25. 7.25 – Partnering with Enterprises for AI Integration 00:00:00
- 26. 7.26 – AI Marketplaces and App Stores 00:00:00
- 27. 7.27 – Pitching AI Solutions to Investors 00:00:00
- 28. 7.28 – Understanding AI Investment Trends 00:00:00
- 29. 7.29 – Startup Fundraising for AI Companies 00:00:00
- 30. 7.30 – Government Grants and AI Research Funding 00:00:00
- 31. 7.31 – Intellectual Property Protection for AI Innovations 00:00:00
- 32. 7.32 – Managing Data Assets as a Business Resource 00:00:00
- 33. 7.33 – Scaling AI Teams and Operations 00:00:00
- 34. 7.34 – Building Strategic Partnerships in AI 00:00:00
- 35. 7.35 – Outsourcing AI Development vs. In-House Teams 00:00:00
- 36. 7.36 – Freelance AI Opportunities 00:00:00
- 37. 7.37 – Becoming an AI Prompt Engineer for Hire 00:00:00
- 38. 7.38 – Building a Portfolio of AI Projects 00:00:00
- 39. 7.39 – Creating Case Studies to Attract Clients 00:00:00
- 40. 7.40 – Networking in the AI Industry 00:00:00
- 41. 7.41 – Staying Current with AI Trends and Tools 00:00:00
- 42. 7.42 – AI Conferences and Events Worth Attending 00:00:00
- 43. 7.43 – Publishing AI Research Papers for Authority 00:00:00
- 44. 7.44 – Personal Branding as an AI Expert 00:00:00
- 45. 7.45 – Teaching AI Courses and Workshops 00:00:00
- 46. 7.46 – Writing and Selling AI eBooks 00:00:00
- 47. 7.47 – Creating YouTube Channels About AI 00:00:00
- 48. 7.48 – Passive Income Streams from AI Work 00:00:00
- 49. 7.49 – Long-Term Career Planning in AI 00:00:00
- 50. 7.50 – Building a Future-Proof AI Business 00:00:00
- 1. 8.1 – Overview of the Most Popular AI Tools Today 00:00:00
- 2. 8.2 – Installing and Using Python for AI Development 00:00:00
- 3. 8.3 – Setting Up Jupyter Notebook for AI Projects 00:00:00
- 4. 8.4 – Introduction to Google Colab for Cloud-Based AI Work 00:00:00
- 5. 8.5 – Using Anaconda for AI Environment Management 00:00:00
- 6. 8.6 – scikit-learn for Machine Learning Basics 00:00:00
- 7. 8.7 – TensorFlow for Deep Learning 00:00:00
- 8. 8.8 – PyTorch for Deep Learning 00:00:00
- 9. 8.9 – Keras for Fast Neural Network Prototyping 00:00:00
- 10. 8.10 – Hugging Face Transformers for NLP 00:00:00
- 11. 8.11 – LangChain for Building LLM Applications 00:00:00
- 12. 8.12 – OpenAI API for Chatbots and Language Models 00:00:00
- 13. 8.13 – Stable Diffusion for AI Image Generation 00:00:00
- 14. 8.14 – Midjourney for Creative Artwork 00:00:00
- 15. 8.15 – DALL·E for Concept Image Generation 00:00:00
- 16. 8.16 – Runway for AI Video Editing 00:00:00
- 17. 8.17 – Pika Labs for AI Animation 00:00:00
- 18. 8.18 – ElevenLabs for AI Voice Generation 00:00:00
- 19. 8.19 – Whisper AI for Speech-to-Text 00:00:00
- 20. 8.20 – AutoGPT for Autonomous AI Agents 00:00:00
- 21. 8.21 – CrewAI for Multi-Agent AI Workflows 00:00:00
- 22. 8.22 – Weaviate for Vector Database Management 00:00:00
- 23. 8.23 – Pinecone for AI Search and Recommendations 00:00:00
- 24. 8.24 – Milvus for High-Performance AI Search 00:00:00
- 25. 8.25 – Streamlit for Building AI Web Apps 00:00:00
- 26. 8.26 – Gradio for AI App Demos 00:00:00
- 27. 8.27 – FastAPI for AI-Driven APIs 00:00:00
- 28. 8.28 – Flask for AI App Backends 00:00:00
- 29. 8.29 – Using AWS Sagemaker for Model Hosting 00:00:00
- 30. 8.30 – Azure Machine Learning Studio 00:00:00
- 31. 8.31 – Google Vertex AI for Enterprise AI 00:00:00
- 32. 8.32 – DataRobot for Automated Machine Learning 00:00:00
- 33. 8.33 – IBM Watson for Enterprise AI Solutions 00:00:00
- 34. 8.34 – KNIME for AI Data Workflows 00:00:00
- 35. 8.35 – RapidMiner for No-Code AI Projects 00:00:00
- 36. 8.36 – Building a Sentiment Analysis App 00:00:00
- 37. 8.37 – Building a Product Recommendation Engine 00:00:00
- 38. 8.38 – Building a Customer Support Chatbot 00:00:00
- 39. 8.39 – Building an AI-Powered Resume Screener 00:00:00
- 40. 8.40 – Building an AI-Powered Financial Forecasting Model 00:00:00
- 41. 8.41 – Building a Face Recognition System 00:00:00
- 42. 8.42 – Building an AI Voice Assistant 00:00:00
- 43. 8.43 – Building a Language Translation App 00:00:00
- 44. 8.44 – Building an Image Captioning Tool 00:00:00
- 45. 8.45 – Building a Music Generator with AI 00:00:00
- 46. 8.46 – Building an AI Video Summarizer 00:00:00
- 47. 8.47 – Building a Virtual Try-On App for Fashion 00:00:00
- 48. 8.48 – Building a Personalized Learning Platform 00:00:00
- 49. 8.49 – Building a Healthcare Symptom Checker 00:00:00
- 50. 8.50 – Final Capstone: End-to-End AI Application from Idea to Launch 00:00:00
- 1. 9.1 – The Role of Research in Advancing AI 00:00:00
- 2. 9.2 – Understanding AI Research Papers and Preprints 00:00:00
- 3. 9.3 – Key AI Conferences: NeurIPS, ICML, CVPR, ACL 00:00:00
- 4. 9.4 – How to Read and Summarize AI Research Papers 00:00:00
- 5. 9.5 – Staying Updated with arXiv and AI Research Blogs 00:00:00
- 6. 9.6 – Current Breakthroughs in Deep Learning 00:00:00
- 7. 9.7 – Scaling Laws in Large Language Models 00:00:00
- 8. 9.8 – Advances in Multi-Modal AI Models 00:00:00
- 9. 9.9 – Foundation Models and Their Capabilities 00:00:00
- 10. 9.10 – Self-Supervised Learning in AI 00:00:00
- 11. 9.11 – Few-Shot and Zero-Shot Learning Research 00:00:00
- 12. 9.12 – Advances in Computer Vision Techniques 00:00:00
- 13. 9.13 – AI for Protein Folding and Drug Discovery 00:00:00
- 14. 9.14 – AI for Climate Modeling and Sustainability 00:00:00
- 15. 9.15 – AI for Energy Optimization 00:00:00
- 16. 9.16 – AI for Space Exploration and Robotics 00:00:00
- 17. 9.17 – Autonomous AI Systems and Self-Improving Models 00:00:00
- 18. 9.18 – AI in Quantum Computing Research 00:00:00
- 19. 9.19 – Brain-Computer Interfaces and AI 00:00:00
- 20. 9.20 – Neuromorphic Computing for AI Acceleration 00:00:00
- 21. 9.21 – AI for Synthetic Biology and Genetic Engineering 00:00:00
- 22. 9.22 – AI in Advanced Materials Discovery 00:00:00
- 23. 9.23 – AI in Financial Market Prediction 00:00:00
- 24. 9.24 – AI for Advanced Transportation Systems 00:00:00
- 25. 9.25 – Swarm Intelligence and Collective AI Systems 00:00:00
- 26. 9.26 – AI in Smart City Development 00:00:00
- 27. 9.27 – Human-AI Collaboration Research 00:00:00
- 28. 9.28 – Explainable AI (XAI) Research Trends 00:00:00
- 29. 9.29 – AI for Emotional Recognition and Empathy 00:00:00
- 30. 9.30 – AI for Mental Health Diagnostics 00:00:00
- 31. 9.31 – Ethical Challenges in Next-Gen AI Systems 00:00:00
- 32. 9.32 – Bias Detection and Removal Innovations 00:00:00
- 33. 9.33 – Privacy-Preserving AI Research 00:00:00
- 34. 9.34 – Federated Learning at Scale 00:00:00
- 35. 9.35 – Advances in Reinforcement Learning 00:00:00
- 36. 9.36 – AI for Automated Scientific Discovery 00:00:00
- 37. 9.37 – AI in Creative Industries and Art 00:00:00
- 38. 9.38 – AI in Entertainment and Film Production 00:00:00
- 39. 9.39 – AI in News and Media Generation 00:00:00
- 40. 9.40 – The Debate on Artificial General Intelligence (AGI) 00:00:00
- 41. 9.41 – Predictions on AGI Arrival Timelines 00:00:00
- 42. 9.42 – AI Regulation Trends and Global Policies 00:00:00
- 43. 9.43 – The Impact of AI on Global Power Dynamics 00:00:00
- 44. 9.44 – AI’s Role in Future Wars and Defense Strategies 00:00:00
- 45. 9.45 – Opportunities in AI-Driven Education 00:00:00
- 46. 9.46 – Preparing for AI-Driven Job Disruption 00:00:00
- 47. 9.47 – AI in Personal Life Enhancement 00:00:00
- 48. 9.48 – How to Contribute to AI Research as an Individual 00:00:00
- 49. 9.49 – The Path to Ethical and Beneficial AI 00:00:00
- 50. 9.50 – Envisioning the Next 50 Years of AI 00:00:00
- 1. 10.1 – Introduction to the Final Capstone Phase 00:00:00
- 2. 10.2 – Selecting Your AI Capstone Project Idea 00:00:00
- 3. 10.3 – Defining Clear Objectives and Success Metrics 00:00:00
- 4. 10.4 – Choosing the Right AI Framework and Tools 00:00:00
- 5. 10.5 – Gathering and Preparing Your Dataset 00:00:00
- 6. 10.6 – Cleaning and Preprocessing Data for Accuracy 00:00:00
- 7. 10.7 – Exploratory Data Analysis (EDA) for Insights 00:00:00
- 8. 10.8 – Choosing the Right Model for Your Problem 00:00:00
- 9. 10.9 – Implementing a Baseline Model for Comparison 00:00:00
- 10. 10.10 – Iterating and Improving Model Performance 00:00:00
- 11. 10.11 – Hyperparameter Tuning for Optimization 00:00:00
- 12. 10.12 – Applying Feature Engineering Techniques 00:00:00
- 13. 10.13 – Building Your Neural Network Architecture 00:00:00
- 14. 10.14 – Training Your AI Model on Local or Cloud Systems 00:00:00
- 15. 10.15 – Monitoring Training Progress and Loss Curves 00:00:00
- 16. 10.16 – Validating the Model with a Test Dataset 00:00:00
- 17. 10.17 – Avoiding Overfitting Through Regularization 00:00:00
- 18. 10.18 – Integrating Transfer Learning for Faster Results 00:00:00
- 19. 10.19 – Documenting Every Step of the AI Build Process 00:00:00
- 20. 10.20 – Adding Explainability Features to Your Model 00:00:00
- 21. 10.21 – Implementing a Human-in-the-Loop Review Step 00:00:00
- 22. 10.22 – Testing for Bias and Fairness in the Model 00:00:00
- 23. 10.23 – Ensuring Data Privacy and Security Compliance 00:00:00
- 24. 10.24 – Deploying Your AI Model to a Web Application 00:00:00
- 25. 10.25 – Creating a User Interface for Your AI Solution 00:00:00
- 26. 10.26 – Building an API Endpoint for Model Access 00:00:00
- 27. 10.27 – Setting Up Continuous Integration and Deployment 00:00:00
- 28. 10.28 – Adding Monitoring and Error Logging to the System 00:00:00
- 29. 10.29 – Handling Model Drift in Production 00:00:00
- 30. 10.30 – Scaling the AI Solution for More Users 00:00:00
- 31. 10.31 – Optimizing Costs for Cloud Hosting 00:00:00
- 32. 10.32 – Adding Backup and Disaster Recovery Plans 00:00:00
- 33. 10.33 – Collecting User Feedback for Improvements 00:00:00
- 34. 10.34 – Implementing Feedback Loops in the Model 00:00:00
- 35. 10.35 – Conducting A/B Testing for Different Model Versions 00:00:00
- 36. 10.36 – Creating Marketing Materials for Your AI Product 00:00:00
- 37. 10.37 – Pitching Your AI Solution to Investors or Clients 00:00:00
- 38. 10.38 – Writing a Whitepaper for Your AI Project 00:00:00
- 39. 10.39 – Presenting Your AI Solution at Conferences 00:00:00
- 40. 10.40 – Building a Public Portfolio with Your Capstone Project 00:00:00
- 41. 10.41 – Open-Sourcing Your AI Code for Community Contribution 00:00:00
- 42. 10.42 – Applying for AI Patents or Intellectual Property Rights 00:00:00
- 43. 10.43 – Monetizing Your AI Project Through Licensing 00:00:00
- 44. 10.44 – Partnering with Businesses to Scale Your AI 00:00:00
- 45. 10.45 – Offering AI Consulting Services Based on Your Project 00:00:00
- 46. 10.46 – Creating Training Content from Your AI Project 00:00:00
- 47. 10.47 – Teaching Workshops Based on Your Capstone Solution 00:00:00
- 48. 10.48 – Maintaining and Updating Your AI Over Time 00:00:00
- 49. 10.49 – Lessons Learned from the Masterclass 00:00:00
- 50. 10.50 – Roadmap for Your Next Steps in AI Mastery 00:00:00
Course overview
Lesson Overview
2.24 – t-SNE for Visualizing High-Dimensional Data: t-Distributed Stochastic Neighbor Embedding (t-SNE) converts complex, high-dimensional data into visual 2D or 3D representations. It excels at revealing patterns, clusters, and relationships that other methods might miss. t-SNE is widely used for exploring neural network outputs, genetic data, and text embeddings. It maintains local structure, showing which data points are most similar. Despite its computational intensity, t-SNE produces intuitive, visually engaging insights. It’s a favorite tool for understanding deep learning behavior.
About this course
A complete 500+ lesson journey from AI fundamentals to advanced machine learning, deep learning, generative AI, deployment, ethics, business applications, and cutting-edge research. Perfect for both beginners and seasoned AI professionals.
This course includes:
- Step-by-step AI development and deployment projects
- Practical coding examples with popular AI frameworks
- Industry use cases and real-world case studies