Artificial Intelligence & Machine Learning Study Materials
AI and ML study materials covering supervised and unsupervised learning, regression, classification, clustering, neural networks, deep learning basics, NLP fundamentals, and Python ML libraries (scikit-learn, TensorFlow).
Artificial Intelligence & Machine Learning Topics — Chapter-wise Notes
History of AI, types (narrow, general, super AI), Turing test, AI applications, and ethics.
View Notes →Regression (linear, polynomial), classification (logistic regression, decision trees, SVM, k-NN).
View Notes →K-means clustering, hierarchical clustering, PCA (dimensionality reduction), and autoencoders.
View Notes →Perceptron, multilayer networks, activation functions, backpropagation, and gradient descent.
View Notes →CNNs (image classification), RNNs/LSTMs (sequences), transfer learning, and model fine-tuning.
View Notes →Tokenisation, stemming, TF-IDF, word embeddings, sentiment analysis, and chatbots.
View Notes →scikit-learn, TensorFlow/Keras, PyTorch basics, Pandas, and NumPy for data science.
View Notes →Train-test split, cross-validation, confusion matrix, precision, recall, F1, and hyperparameter tuning.
View Notes →Feature selection, encoding categorical variables, handling missing values, and data scaling.
View Notes →Image processing, edge detection, object detection (YOLO), and image classification with CNNs.
View Notes →Agent, environment, rewards, Q-learning, policy gradient, and OpenAI Gym basics.
View Notes →Bias in AI systems, fairness, transparency, explainability (XAI), and responsible AI guidelines.
View Notes →Study materials coming soon
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