The mind map offers a concise overview of machine learning,
covering fundamental concepts, algorithms, and applications. It
introduces the types of machine learning and their workflow, along
with key concepts like data representation, model evaluation, and
regularization. Various algorithms are discussed, including
regression, decision trees, SVM, and neural networks. The map
explores dimensionality reduction techniques, model evaluation
metrics, and unsupervised learning methods like clustering. It also
touches on natural language processing, computer vision,
reinforcement learning, and Monte Carlo methods. Real-world
applications in healthcare, finance, e-commerce, and fraud detection
are highlighted.
Acronyms related to Machine Learning:
ML: Machine Learning AI: Artificial Intelligence DL: Deep
Learning SVM: Support Vector Machines NN: Neural Networks
CNN: Convolutional Neural Networks RNN: Recurrent Neural Networks
GAN: Generative Adversarial Network NLP: Natural Language
Processing PCA: Principal Component Analysis MDP: Markov
Decision Process Q-Learning: Quality Learning RL:
Reinforcement Learning MCMC: Markov Chain Monte Carlo AUC:
Area Under the Curve KNN: K-Nearest Neighbors DT: Decision
Tree RF: Random Forest LDA: Latent Dirichlet Allocation EM:
Expectation-Maximization LSTM: Long Short-Term Memory SGD:
Stochastic Gradient Descent GMM: Gaussian Mixture Model ANN:
Artificial Neural Network MLP: Multilayer Perceptron
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