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Artificial Intelligence
Artificial Intelligence (AI) enables machines to simulate human intelligence - learning, reasoning, and problem-solving - through algorithms, data, and computational power.
Flow Chart
For a Artificial Intelligence course, the flow chart for learning skills could look like this
Module 1: Foundations of AI
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Introduction to AI: History, applications, and ethics
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Python for AI: NumPy, Pandas, Matplotlib
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Math Essentials: Linear algebra, calculus, probability, and statistics
Module 2: Machine Learning (ML) Core
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Supervised Learning: Regression, decision trees, SVM, ensemble methods
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Unsupervised Learning: Clustering (k-means, DBSCAN), PCA
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Model Evaluation: Cross-validation, metrics (precision, recall, ROC)
Module 3: Deep Learning (DL)
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Neural Networks: Perceptrons, backpropagation, activation functions
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CNNs: Image classification, object detection (YOLO, ResNet)
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RNNs & Transformers: NLP (LSTM, BERT), time-series analysis
Module 4: Advanced Topics
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Reinforcement Learning: Q-learning, Deep Q-Networks (DQN)
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Generative AI: GANs, VAEs, diffusion models
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AI Deployment: Model quantization, ONNX, Flask/Django APIs
Module 5: Specializations (Choose 1-2)
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Computer Vision: OpenCV, YOLO, image segmentation
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NLP: Tokenization, transformers, chatbots (GPT, Llama)
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Robotics: ROS, SLAM, motion planning