Linear Algebra and Probability for AI

  Matrices, Vectors, and Linear Transformations

  Eigenvalues and Eigenvectors

  Probability Theory and Bayes’ Theorem

  Statistics for AI (Mean, Variance, Standard Deviation)

Optimization Techniques

  Gradient Descent and Backpropagation

  Convex vs. Non-Convex Optimization

  Loss Functions and Cost Functions

Supervised Learning Algorithms

  Regression Models (Linear, Logistic)

  Classification Algorithms (Decision Trees, SVM, Random Forest)

  Ensemble Learning (Bagging, Boosting, XGBoost)

  Model Evaluation (Accuracy, Precision, Recall, F1-Score)

Unsupervised Learning Algorithms

  Clustering (K-Means, Hierarchical, DBSCAN)

  Dimensionality Reduction (PCA, t-SNE)

  Anomaly Detection

Reinforcement Learning

  Markov Decision Processes (MDP)

  Q-Learning and Deep Q Networks (DQN)

  Policy Gradient Methods

Artificial Neural Networks (ANNs)

  Introduction to Neural Networks

  Activation Functions (ReLU, Sigmoid, Softmax)

  Forward and Backpropagation

Convolutional Neural Networks (CNNs)

  Image Classification and Object Detection

  CNN Architectures (AlexNet, VGG, ResNet)

  Transfer Learning and Fine-Tuning

Recurrent Neural Networks (RNNs) & Transformers

  Time Series Forecasting with RNNs

  LSTMs and GRUs for Sequential Data

  Introduction to Transformers (BERT, GPT)

Fundamentals of NLP

  Text Processing (Tokenization, Stemming, Lemmatization)

  Named Entity Recognition (NER)

  Sentiment Analysis