- Clustering Básico: k-means, DBSCAN e mean shift
- Machine Learning: classificação por trás dos panos
- Clusterização de dados: segmentação de clientes
- Análise de componentes principais: elaboração de rankings com o PCA
- .ipynb
- Machine Learning: Credit Scoring
- Keras Regression (Houses Price)
- Keras Classification (Cancer)
- Keras Classification Exercise (Lending Money)
- Machine Learning: Aprendizado supervisionado
- .ipynb
- Keras CNN Classification (MNIST Digits)
- Keras CNN Classification (CIFAR-10)
- Keras CNN Classification (Malaria Infection)
- Keras CNN Classification (Fashion MNIST)
- Machine Learning: Aprendizado supervisionado
- .ipynb
- Boruta - auto feature selection
- Handling Imbalanced Datasets SMOTE Technique
- Machine Learning: validação de modelos
- Linguagem Natural parte 1: NLP com análise de sentimento
- Linguagem Natural parte 2: continuando com a análise de sentimento
- Corretor Ortográfico em Python: aplicando técnicas de NLP
- NLP: regex e modelos de linguagem
- Word2Vec: interpretação da linguagem humana com Word embedding
- Word2Vec: treinamento de Word Embedding
- nlp/curso_word2vec_2.ipynb
- nlp/classificador_de_artigos/
- python3 app.py
- localhost:5000
- Markov Chains
- Markov Chains Clearly Explained! Part - 1
- Markov Chains: Recurrence, Irreducibility, Classes | Part - 2
- Markov Chains: n-step Transition Matrix | Part - 3
- Markov Chains: Generating Sherlock Holmes Stories | Part - 4
- Hidden Markov Model Clearly Explained! Part - 5
- Forward Algorithm Clearly Explained | Hidden Markov Model | Part - 6
- Markov Chains: Simulation in Python | Stationary Distribution Computation | Part - 7
- Classificação multilabel de textos: múltiplos contextos em NLP
- Q-Learning implementation (Tic Tac Toe)
- Deep Q-Learning implementation (Tic Tac Toe)
- Learn how to use Reinforcement Learning techniques to create practical Artificial Intelligence programs!
- rf_lr/qlearning/intro_rflr_gym.ipynb
- rf_lr/qlearning/agent_rflr_gym.ipynb
- rf_lr/qlearning/simple_agent_rflr_gym.ipynb
- rf_lr/qlearning/q_learning_rflr_gym.ipynb
- rf_lr/qlearning/q_learning_continuous_rflr_gym.ipynb
- rf_lr/qlearning/q_learning_exercise.ipynb
- rf_lr/qlearning/q_learning_route.ipynb
- rf_lr/deepqlearning/deep_q_learning_rflr_gym
- rf_lr/deepqlearning/deep_q_learning_rflr_gym_keras_rl2.ipynb
- rf_lr/deepqlearning/deep_q_learning_rflr_gym_keras_rl2_exercise.ipynb
- rf_lr/deepqlearning/deep_q_learning_image_rflr_gym_keras_rl2.ipynb
- rf_lr/deepqlearning/deep_q_learning_image_rflr_gym_keras_rl2_exercise.ipynb
- rf_lr/deepqlearning/deep_q_learning_rflr_custom_snake_gym_keras_rl2.ipynb
- Inteligência Artificial aplicada para Empresas e Negócios
- rf_lr/qlearning/q_learning_route.ipynb
- rf_lr/deepqlearning/deep_q_learning_server_cost_minimization.ipynb
- rf_lr/mab/thompson_sampling.ipynb
- mab - Multi Armed Bandit (Thompson Sampling)
- Deep RL Course
From scratch series - Patrick Loeber course
- KNN
- .py
- Linear Regression
- .py
- Logistic Regression
- .py
- Linear and Logistic Regression
- .py
- Naive Bayes
- .py
- Perceptron
- .py
- SVM
- .py
- Decision Tree
- .py
- Random Forest
- .py
- PCA
- .py
- K-Means
- AdaBoost
- .py
- LDA (Linear Discriminant Analysis)
- .py
- How to Load Machine Learning Data From Files
- .py