Complete Artificial Intelligence Course (Beginner to Advanced)
📘 Beginner Level: Foundations of AI
1. Introduction to AI
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What is AI? History, Scope, and Applications
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Types of AI (Narrow, General, Super AI)
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Goals and Philosophy of AI
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Real-world Examples: Siri, Google Maps, Chatbots
2. Mathematics for AI
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Linear Algebra: Vectors, Matrices, Eigenvalues
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Probability & Statistics: Bayes Theorem, Gaussian Distribution
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Calculus: Derivatives, Gradients (for backpropagation)
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Discrete Mathematics: Logic, Sets, Graphs
3. Python for AI
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Basics: Variables, Loops, Functions
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Libraries: NumPy, Pandas, Matplotlib
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Hands-on: Load a dataset, basic preprocessing
📘 Intermediate Level: Machine Learning (ML)
4. Supervised Learning
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Regression (Linear, Polynomial)
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Classification (Logistic, Decision Trees, KNN, SVM)
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Metrics: Accuracy, Precision, Recall, F1 Score
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Hands-on: Predict house prices, classify emails as spam/ham
5. Unsupervised Learning
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Clustering (K-Means, Hierarchical)
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Dimensionality Reduction (PCA, t-SNE)
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Association Rule Learning (Apriori, Eclat)
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Hands-on: Customer segmentation, market basket analysis
6. Model Evaluation & Selection
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Cross-validation, Bias-Variance Trade-off
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Overfitting vs Underfitting
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Grid Search, Hyperparameter Tuning
📘 Advanced Level: Deep Learning & Neural Networks
7. Neural Networks
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Perceptron, Multilayer Perceptron
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Activation Functions: Sigmoid, ReLU, Tanh
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Loss Functions, Gradient Descent
8. Deep Learning with TensorFlow/Keras
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Build models with Keras
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CNNs (Convolutional Neural Networks) — for images
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RNNs (Recurrent Neural Networks), LSTM — for sequences
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Hands-on: Image classification, Sentiment analysis
9. Natural Language Processing (NLP)
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Text preprocessing: Tokenization, Stemming, Stopwords
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NLP Models: Bag of Words, TF-IDF, Word2Vec
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Transformers, BERT, GPT
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Hands-on: Chatbot, Text summarizer, Question Answering
10. Computer Vision
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Image Filtering, Object Detection
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OpenCV + Deep Learning
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YOLO, Mask R-CNN
📘 Expert Level: Specialized AI Domains
11. Reinforcement Learning
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Q-Learning, Markov Decision Process (MDP)
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Deep Q Networks (DQN)
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Applications: Game AI, Robotics
12. Explainable AI & Ethics
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Interpretability (LIME, SHAP)
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AI Bias, Fairness, Privacy
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AI and Ethics in Society
13. AI for Real-world Use
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AI in Healthcare, Finance, Education, Law
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AI Project Lifecycle: Ideation → Data Collection → Modeling → Deployment
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MLOps: Model Deployment with Flask, FastAPI, Docker
14. Capstone Projects
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Fake News Detector
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Image Caption Generator
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AI-powered Recommendation System
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Autonomous Car Simulation (OpenAI Gym)
📘 Tools, Certifications, and Practice
15. Tools & Platforms
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Jupyter Notebook, Google Colab
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Kaggle, GitHub, HuggingFace, Weights & Biases
16. Certifications (Free & Paid)
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Coursera: AI for Everyone by Andrew Ng
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edX: Columbia University’s AI Course
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Google AI, Microsoft Learn
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IBM AI Engineering
Home Academy : AI for beginners
17. Practice & Interview Prep
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100+ AI Interview Questions
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Projects Portfolio Building
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GitHub Resume for AI Jobs
🔁 How to Learn This Course:
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Daily Time: 1–2 hours consistently
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Practice: After every topic, do 1 mini-project or Kaggle task
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Code: Implement ML/DL models from scratch
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Revise: Weekly revision with notes and quizzes