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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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Artificial Intelligence Approaches
Artificial Intelligence Technology
Artificial Intelligence Philosophy
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Branches of AI by Application
Emerging Technologies
Business Mind
Maps
Bloom's Taxonomy
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