Artificial Intelligence & Machine Learning Study Materials

History of AI, types (narrow, general, super AI), Turing test, AI applications, and ethics.

Regression (linear, polynomial), classification (logistic regression, decision trees, SVM, k-NN).

K-means clustering, hierarchical clustering, PCA (dimensionality reduction), and autoencoders.

Perceptron, multilayer networks, activation functions, backpropagation, and gradient descent.

CNNs (image classification), RNNs/LSTMs (sequences), transfer learning, and model fine-tuning.

Tokenisation, stemming, TF-IDF, word embeddings, sentiment analysis, and chatbots.

scikit-learn, TensorFlow/Keras, PyTorch basics, Pandas, and NumPy for data science.

Train-test split, cross-validation, confusion matrix, precision, recall, F1, and hyperparameter tuning.

Feature selection, encoding categorical variables, handling missing values, and data scaling.

Image processing, edge detection, object detection (YOLO), and image classification with CNNs.

Agent, environment, rewards, Q-learning, policy gradient, and OpenAI Gym basics.

Bias in AI systems, fairness, transparency, explainability (XAI), and responsible AI guidelines.