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Astronomy: Exploring Time and Space
Inglés empresarial: ventas, gestión y liderazgo
AI and Big Data in Global Health Improvement
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Introducción práctica al aprendizaje automático en español, cubriendo técnicas supervisadas y no supervisadas con aplicaciones reales como reconocimiento de imágenes y sistemas de recomendación.
Explore Latent Dirichlet Allocation through a two-part series, covering its fundamentals and training using Gibbs Sampling.
Explore Bayes Theorem, Hidden Markov Models, Shannon Entropy, Naive Bayes classifier, Beta distribution, and Thompson sampling in this friendly introduction to key probability concepts.
Explore denoising autoencoders, VAEs, GANs, and RBMs in this comprehensive introduction to generative models, covering key concepts and applications in machine learning.
Explore key unsupervised learning techniques including clustering, dimensionality reduction, and generative models. Gain insights into real-world applications like recommendation systems and image compression.
This Course is a friendly introduction series to machine learning, deep learning, neural networks and generative adversarial networks.
Explore key machine learning concepts, algorithms, and evaluation methods in this comprehensive introduction to the field.
Comprehensive introduction to key machine learning concepts, algorithms, and applications, covering testing, error metrics, recommendation systems, and various classification methods.
Explore linear regression, logistic regression, perceptron algorithm, and support vector machines in this friendly introduction to key supervised learning concepts.
Explore binomial and Poisson distributions, from basic concepts to practical applications in probability calculations for real-world scenarios.
Learn to build and train decision trees through a simple example, exploring key concepts like data splitting, overfitting prevention, and alternative evaluation metrics.
Friendly introduction to quantum computing and machine learning, covering qubits, quantum gates, entanglement, and their applications in generative modeling and optimization.
Explore autoencoders, powerful generative models for dimensionality reduction and data generation. Learn about denoising and variational autoencoders, their applications, and training techniques.
Friendly exploration of deep reinforcement learning concepts, including Markov decision processes, Q-networks, and policy gradients, using examples and visuals to explain complex ideas.
Learn singular value decomposition (SVD) and its application in image compression. Explore matrix transformations, dimensionality reduction, and practical implementation techniques.
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