├── README.md
├── Recetas_Python.md
├── Recetas_R.md
├── Repositorio.md
├── Standford Cheatsheet spanish
├── hoja-referencia-aprendizaje-automatico-consejos-trucos.pdf
├── hoja-referencia-aprendizaje-no-supervisado.pdf
├── hoja-referencia-aprendizaje-profundo.pdf
├── hoja-referencia-aprendizaje-supervisado.pdf
├── repaso-algebra-lineal-calculo.pdf
├── repaso-probabilidades-estadisticas.pdf
└── super-hoja-referencia-machine-learning.pdf
└── listado_algoritmos.md
/README.md:
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1 | # Recopilatorio Data Science
2 | Recopilatorio de materiales relacionados con Data Science, Machine Learning, Deep Learning, Inteligencia artificial y todo lo relacionado con esas materias en la red
3 |
4 | 
5 |
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/Recetas_Python.md:
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1 | ### Código de los principales algoritmos en Python
2 |
3 | Regresión lineal, regresión logística, árbol de decisión, SVM, Naive Bayes, kNN, k-Means, Random Forest, PCA, GBM, XGBoost, LightGBM, Catboost
4 |
5 | #### Regresión lineal
6 | ~~~
7 | #Import Library
8 | #Import other necessary libraries like pandas, numpy...
9 | from sklearn import linear_model
10 | #Load Train and Test datasets
11 | #Identify feature and response variable(s) and values must be numeric and numpy arrays
12 | x_train=input_variables_values_training_datasets
13 | y_train=target_variables_values_training_datasets
14 | x_test=input_variables_values_test_datasets
15 | # Create linear regression object
16 | linear = linear_model.LinearRegression()
17 | # Train the model using the training sets and check score
18 | linear.fit(x_train, y_train)
19 | linear.score(x_train, y_train)
20 | #Equation coefficient and Intercept
21 | print('Coefficient: \n', linear.coef_)
22 | print('Intercept: \n', linear.intercept_)
23 | #Predict Output
24 | predicted= linear.predict(x_test)
25 | ~~~
26 |
27 | #### Regresión logística
28 | ~~~
29 | #Import Library
30 | from sklearn.linear_model import LogisticRegression
31 | #Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
32 | # Create logistic regression object
33 | model = LogisticRegression()
34 | # Train the model using the training sets and check score
35 | model.fit(X, y)
36 | model.score(X, y)
37 | #Equation coefficient and Intercept
38 | print('Coefficient: \n', model.coef_)
39 | print('Intercept: \n', model.intercept_)
40 | #Predict Output
41 | predicted= model.predict(x_test)
42 | ~~~
43 |
44 | #### Árbol de decisión
45 | ~~~
46 | #Import Library
47 | #Import other necessary libraries like pandas, numpy...
48 | from sklearn import tree
49 | #Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
50 | # Create tree object
51 | model = tree.DecisionTreeClassifier(criterion='gini') # for classification, here you can change the algorithm as gini or entropy (information gain) by default it is gini
52 | # model = tree.DecisionTreeRegressor() for regression
53 | # Train the model using the training sets and check score
54 | model.fit(X, y)
55 | model.score(X, y)
56 | #Predict Output
57 | predicted= model.predict(x_test)
58 | ~~~
59 |
60 | #### SVM (Support Vector Machine)
61 | ~~~
62 | #Import Library
63 | from sklearn import svm
64 | #Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
65 | # Create SVM classification object
66 | model = svm.svc() # there is various option associated with it, this is simple for classification. You can refer link, for mo# re detail.
67 | # Train the model using the training sets and check score
68 | model.fit(X, y)
69 | model.score(X, y)
70 | #Predict Output
71 | predicted= model.predict(x_test)
72 | ~~~
73 |
74 | #### Naive Bayes
75 | ~~~
76 | #Import Library
77 | from sklearn.naive_bayes import GaussianNB
78 | #Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
79 | # Create SVM classification object model = GaussianNB() # there is other distribution for multinomial classes like Bernoulli Naive Bayes, Refer link
80 | # Train the model using the training sets and check score
81 | model.fit(X, y)
82 | #Predict Output
83 | predicted= model.predict(x_test)
84 | ~~~
85 |
86 | #### kNN (k-Nearest Neighbors)
87 | ~~~
88 | #Import Library
89 | from sklearn.neighbors import KNeighborsClassifier
90 | #Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
91 | # Create KNeighbors classifier object model
92 | KNeighborsClassifier(n_neighbors=6) # default value for n_neighbors is 5
93 | # Train the model using the training sets and check score
94 | model.fit(X, y)
95 | #Predict Output
96 | predicted= model.predict(x_test)
97 | ~~~
98 |
99 | #### K-Means
100 | ~~~
101 | #Import Library
102 | from sklearn.cluster import KMeans
103 | #Assumed you have, X (attributes) for training data set and x_test(attributes) of test_dataset
104 | # Create KNeighbors classifier object model
105 | k_means = KMeans(n_clusters=3, random_state=0)
106 | # Train the model using the training sets and check score
107 | model.fit(X)
108 | #Predict Output
109 | predicted= model.predict(x_test)
110 | ~~~
111 |
112 | #### Random Forest
113 | ~~~
114 | #Import Library
115 | from sklearn.ensemble import RandomForestClassifier
116 | #Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
117 | # Create Random Forest object
118 | model= RandomForestClassifier()
119 | # Train the model using the training sets and check score
120 | model.fit(X, y)
121 | #Predict Output
122 | predicted= model.predict(x_test)
123 | ~~~
124 |
125 | #### PCA
126 | ~~~
127 | #Import Library
128 | from sklearn import decomposition
129 | #Assumed you have training and test data set as train and test
130 | # Create PCA obeject pca= decomposition.PCA(n_components=k) #default value of k =min(n_sample, n_features)
131 | # For Factor analysis
132 | #fa= decomposition.FactorAnalysis()
133 | # Reduced the dimension of training dataset using PCA
134 | train_reduced = pca.fit_transform(train)
135 | #Reduced the dimension of test dataset
136 | test_reduced = pca.transform(test)
137 | ~~~
138 |
139 | #### GBM
140 | ~~~
141 | #Import Library
142 | from sklearn.ensemble import GradientBoostingClassifier
143 | #Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
144 | # Create Gradient Boosting Classifier object
145 | model= GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0)
146 | # Train the model using the training sets and check score
147 | model.fit(X, y)
148 | #Predict Output
149 | predicted= model.predict(x_test)
150 | ~~~
151 |
152 | #### XGBoost
153 | ~~~
154 | from xgboost import XGBClassifier
155 | from sklearn.model_selection import train_test_split
156 | from sklearn.metrics import accuracy_score
157 | X = dataset[:,0:10]
158 | Y = dataset[:,10:]
159 | seed = 1
160 | X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=seed)
161 | model = XGBClassifier()
162 | model.fit(X_train, y_train)
163 | #Make predictions for test data
164 | y_pred = model.predict(X_test)
165 | ~~~
166 |
167 | #### LightGBM
168 | ~~~
169 | data = np.random.rand(500, 10) # 500 entities, each contains 10 features
170 | label = np.random.randint(2, size=500) # binary target
171 |
172 | train_data = lgb.Dataset(data, label=label)
173 | test_data = train_data.create_valid('test.svm')
174 | param = {'num_leaves':31, 'num_trees':100, 'objective':'binary'}
175 | param['metric'] = 'auc'
176 |
177 | num_round = 10
178 | bst = lgb.train(param, train_data, num_round, valid_sets=[test_data])
179 | bst.save_model('model.txt')
180 | # 7 entities, each contains 10 features
181 | data = np.random.rand(7, 10)
182 | ypred = bst.predict(data)
183 | ~~~
184 |
185 | #### Catboost
186 | ~~~
187 | import pandas as pd
188 | import numpy as np
189 | from catboost import CatBoostRegressor
190 |
191 | #Read training and testing files
192 | train = pd.read_csv("train.csv")
193 | test = pd.read_csv("test.csv")
194 |
195 | #Imputing missing values for both train and test
196 | train.fillna(-999, inplace=True)
197 | test.fillna(-999,inplace=True)
198 |
199 | #Creating a training set for modeling and validation set to check model performance
200 | X = train.drop(['Item_Outlet_Sales'], axis=1)
201 | y = train.Item_Outlet_Sales
202 | from sklearn.model_selection import train_test_split
203 | X_train, X_validation, y_train, y_validation = train_test_split(X, y, train_size=0.7, random_state=1234)
204 | categorical_features_indices = np.where(X.dtypes != np.float)[0]
205 |
206 | #importing library and building model
207 | from catboost import CatBoostRegressormodel=CatBoostRegressor(iterations=50, depth=3, learning_rate=0.1, loss_function='RMSE')
208 | model.fit(X_train, y_train,cat_features=categorical_features_indices,eval_set=(X_validation, y_validation),plot=True)
209 | submission = pd.DataFrame()
210 | submission['Item_Identifier'] = test['Item_Identifier']
211 | submission['Outlet_Identifier'] = test['Outlet_Identifier']
212 | submission['Item_Outlet_Sales'] = model.predict(test)
213 | ~~~
214 |
215 | **Referencias**: https://www.analyticsvidhya.com/
216 |
217 |
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/Recetas_R.md:
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1 | ### Código de los principales algoritmos en R
2 |
3 | Regresión lineal, regresión logística, árbol de decisión, SVM, Naive Bayes, kNN, k-Means, Random Forest, PCA, GBM, XGBoost, LightGBM, Catboost
4 |
5 | #### Regresión lineal
6 | ~~~
7 | #Load Train and Test datasets
8 | #Identify feature and response variable(s) and values must be numeric and numpy arrays
9 | x_train <- input_variables_values_training_datasets
10 | y_train <- target_variables_values_training_datasets
11 | x_test <- input_variables_values_test_datasets
12 | x <- cbind(x_train,y_train)
13 | # Train the model using the training sets and check score
14 | linear <- lm(y_train ~ ., data = x)
15 | summary(linear)
16 | #Predict Output
17 | predicted= predict(linear,x_test)
18 | ~~~
19 |
20 | #### Regresión logística
21 | ~~~
22 | x <- cbind(x_train,y_train)
23 | # Train the model using the training sets and check score
24 | logistic <- glm(y_train ~ ., data = x,family='binomial')
25 | summary(logistic)
26 | #Predict Output
27 | predicted= predict(logistic,x_test)
28 | ~~~
29 |
30 | #### Árbol de decisión
31 | ~~~
32 | library(rpart)
33 | x <- cbind(x_train,y_train)
34 | # grow tree
35 | fit <- rpart(y_train ~ ., data = x,method="class")
36 | summary(fit)
37 | #Predict Output
38 | predicted= predict(fit,x_test)
39 | ~~~
40 |
41 | #### SVM (Support Vector Machine)
42 | ~~~
43 | library(e1071)
44 | x <- cbind(x_train,y_train)
45 | # Fitting model
46 | fit <-svm(y_train ~ ., data = x)
47 | summary(fit)
48 | #Predict Output
49 | predicted= predict(fit,x_test)
50 | ~~~
51 |
52 | #### Naive Bayes
53 | ~~~
54 | library(e1071)
55 | x <- cbind(x_train,y_train)
56 | # Fitting model
57 | fit <-naiveBayes(y_train ~ ., data = x)
58 | summary(fit)
59 | #Predict Output
60 | predicted= predict(fit,x_test)
61 | ~~~
62 |
63 | #### kNN (k-Nearest Neighbors)
64 | ~~~
65 | library(knn)
66 | x <- cbind(x_train,y_train)
67 | # Fitting model
68 | fit <-knn(y_train ~ ., data = x,k=5)
69 | summary(fit)
70 | #Predict Output
71 | predicted= predict(fit,x_test)
72 | ~~~
73 |
74 | #### K-Means
75 | ~~~
76 | library(cluster)
77 | fit <- kmeans(X, 3) # 5 cluster solutio
78 | ~~~
79 |
80 | #### Random Forest
81 | ~~~
82 | library(randomForest)
83 | x <- cbind(x_train,y_train)
84 | # Fitting model
85 | fit <- randomForest(Species ~ ., x,ntree=500)
86 | summary(fit)
87 | #Predict Output
88 | predicted= predict(fit,x_test)
89 | ~~~
90 |
91 | #### PCA
92 | ~~~
93 | library(stats)
94 | pca <- princomp(train, cor = TRUE)
95 | train_reduced <- predict(pca,train)
96 | test_reduced <- predict(pca,test)
97 | ~~~
98 |
99 | #### GBM
100 | ~~~
101 | library(caret)
102 | x <- cbind(x_train,y_train)
103 | # Fitting model
104 | fitControl <- trainControl( method = "repeatedcv", number = 4, repeats = 4)
105 | fit <- train(y ~ ., data = x, method = "gbm", trControl = fitControl,verbose = FALSE)
106 | predicted= predict(fit,x_test,type= "prob")[,2]
107 | ~~~
108 |
109 | #### XGBoost
110 | ~~~
111 | require(caret)
112 | x <- cbind(x_train,y_train)
113 | # Fitting model
114 | TrainControl <- trainControl( method = "repeatedcv", number = 10, repeats = 4)
115 | model<- train(y ~ ., data = x, method = "xgbLinear", trControl = TrainControl,verbose = FALSE)
116 | OR
117 | model<- train(y ~ ., data = x, method = "xgbTree", trControl = TrainControl,verbose = FALSE)
118 | predicted <- predict(model, x_test)
119 | ~~~
120 |
121 | #### LightGBM
122 | ~~~
123 | require(caret)
124 | require(RLightGBM)
125 | data(iris)
126 |
127 | model <-caretModel.LGBM()
128 |
129 | fit <- train(Species ~ ., data = iris, method=model, verbosity = 0)
130 | print(fit)
131 | y.pred <- predict(fit, iris[,1:4])
132 |
133 | library(Matrix)
134 | model.sparse <- caretModel.LGBM.sparse()
135 |
136 | #Generate a sparse matrix
137 | mat <- Matrix(as.matrix(iris[,1:4]), sparse = T)
138 | fit <- train(data.frame(idx = 1:nrow(iris)), iris$Species, method = model.sparse, matrix = mat, verbosity = 0)
139 | print(fit)
140 | ~~~
141 |
142 | #### Catboost
143 | ~~~
144 | set.seed(1)
145 | require(titanic)
146 | require(caret)
147 | require(catboost)
148 |
149 | tt <- titanic::titanic_train[complete.cases(titanic::titanic_train),]
150 | data <- as.data.frame(as.matrix(tt), stringsAsFactors = TRUE)
151 | drop_columns = c("PassengerId", "Survived", "Name", "Ticket", "Cabin")
152 | x <- data[,!(names(data) %in% drop_columns)]y <- data[,c("Survived")]
153 | fit_control <- trainControl(method = "cv", number = 4,classProbs = TRUE)
154 | grid <- expand.grid(depth = c(4, 6, 8),learning_rate = 0.1,iterations = 100, l2_leaf_reg = 1e-3, rsm = 0.95, border_count = 64)
155 | report <- train(x, as.factor(make.names(y)),method = catboost.caret,verbose = TRUE, preProc = NULL,tuneGrid = grid, trControl = fit_control)
156 | print(report)
157 | importance <- varImp(report, scale = FALSE)
158 | print(importance)
159 | ~~~
160 |
161 | **Referencias**: https://www.analyticsvidhya.com/
162 |
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/Repositorio.md:
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1 | # Recopilatorio Data Science
2 | Recopilatorio de materiales relacionados con Data Science, Machine Learning, Deep Learning, Inteligencia artificial y todo lo relacionado con esas materias en la red
3 | ***
4 | **Contenido**
5 | 1. [Recursos](#id1)
6 | 2. [Programas/Webs suite](#id2)
7 | 3. [Datasets](#id3)
8 | 4. [Modelos preentrenados](#id4)
9 | 5. [APIs](#id5)
10 | 6. [Libros y documentos](#id6)
11 | 7. [Blogs](#id7)
12 | 8. [Canales de Youtube](#id8)
13 | 9. [Plataformas de aprendizaje](#id9)
14 | 10. [Otros programas/recursos](#id10)
15 | 11. [Paquetes R](#id11)
16 | 12. [Paquetes Python](#id12)
17 | 13. [Casos de uso](#id13)
18 | 14. [Automated machine learning](#id14)
19 | 15. [Embedded machine learning](#id15)
20 | 16. [Spatial Data Science / Machine Learning](#id16)
21 | 17. [Herramientas de Anotación](#id17)
22 | ***
23 |
24 |
25 |
26 |
27 | ### Recursos
28 |
29 | Google AI education
30 | https://ai.google/education/
31 |
32 | Microsoft AI school
33 | https://aischool.microsoft.com/en-us/home
34 |
35 | Intel AI academy
36 | https://software.intel.com/es-es/ai-academy/students/courses
37 |
38 | NVIDIA Deep Learning Institute
39 | (https://www.nvidia.com/es-es/deep-learning-ai/education/)
40 |
41 | IBM cognitive class
42 | https://cognitiveclass.ai/
43 |
44 | Facebook Machine Learning
45 | https://research.fb.com/category/machine-learning/
46 |
47 | Amazon Machine Learning
48 | https://aws.amazon.com/es/aml/
49 |
50 | Kaggle
51 | https://www.kaggle.com/learn/overview
52 |
53 | Fast.ai
54 | https://www.fast.ai/
55 |
56 | Mlcourse.ai
57 | https://mlcourse.ai/
58 |
59 | Datacamp
60 | https://www.datacamp.com/
61 |
62 | Dataquest
63 | https://www.dataquest.io/
64 |
65 | KDnuggets
66 | https://www.kdnuggets.com/
67 |
68 | Data Science Central
69 | https://www.datasciencecentral.com/
70 |
71 | Analytics Vidhya
72 | https://www.analyticsvidhya.com/blog/
73 |
74 | Towards data science
75 | https://towardsdatascience.com/
76 |
77 | Business Science University
78 | https://university.business-science.io/
79 |
80 | Quick-R
81 | https://www.statmethods.net/index.html
82 |
83 | RDM
84 | http://www.rdatamining.com/
85 |
86 | Capacítate para el empleo (Tecnología)
87 | https://capacitateparaelempleo.org
88 |
89 | School of AI
90 | https://www.theschool.ai/
91 |
92 | DataFlair
93 | https://data-flair.training/blogs/
94 |
95 | Elements of IA
96 | https://course.elementsofai.com/
97 |
98 | Made With ML
99 | (https://madewithml.com/)
100 |
101 | New Deep Learning Techniques
102 | (http://www.ipam.ucla.edu/programs/workshops/new-deep-learning-techniques/?tab=schedule)
103 |
104 | AI Institute Geometry of Deep Learning
105 | (https://www.microsoft.com/en-us/research/event/ai-institute-2019/#!videos)
106 |
107 | Machine Learning Práctico (https://elsonidoq.github.io/machine-learning-practico/)
108 |
109 |
110 |
111 | #### Universidades
112 |
113 | ##### Statistics, Probability & Data Science
114 |
115 | * Stanford University Course Probability for Computer Scientists
116 | (https://web.stanford.edu/class/archive/cs/cs109/cs109.1196/schedule.html)
117 |
118 | * EPFL University Course Advanced probability and applications
119 | (https://moodle.epfl.ch/course/view.php?id=14557)
120 |
121 | * Brown University Course Probability & Computing
122 | (http://cs.brown.edu/courses/csci1450/lectures.html)
123 |
124 | * Gothenburg University Course Statistical Methods for Data Science
125 | (https://gu.instructure.com/courses/11025)
126 |
127 | * MIT University Course Statistical Learning Theory and Applications
128 | (http://www.mit.edu/~9.520/fall19/)
129 |
130 | * Cornell University Course Data Science for all
131 | (https://www.cs.cornell.edu/courses/cs1380/2020sp/schedule.html)
132 |
133 | * Brown University Course Introduction to Data Science
134 | (https://cs.brown.edu/courses/csci1951-a/slides/)
135 |
136 | * Brown University Course Data Science
137 | (http://cs.brown.edu/courses/cs195w/slides.shtml)
138 |
139 |
140 |
141 | ##### Machine Learning
142 |
143 | * Stanford University Course Machine Learning (http://cs229.stanford.edu/syllabus-summer2020.html)
144 |
145 | * Harvard University Course Machine Learning (https://harvard-ml-courses.github.io/cs181-web/schedule)
146 |
147 | * Oxford University Course Machine Learning (https://www.cs.ox.ac.uk/people/varun.kanade/teaching/ML-MT2016/lectures/)
148 |
149 | * EPFL University Course Machine Learning (https://www.epfl.ch/labs/mlo/machine-learning-cs-433/)
150 |
151 | * Alberta University Course Machine Learning (https://marthawhite.github.io/mlcourse/schedule.html)
152 |
153 | * Aachen University Course Machine Learning (http://www.vision.rwth-aachen.de/course/31/)
154 |
155 | * Brown University Course Machine Learning (http://cs.brown.edu/courses/csci1420/lectures.html)
156 |
157 | * Cornell University Course Machine Learning for Intelligent Systems (https://www.cs.cornell.edu/courses/cs4780/2019fa/)
158 |
159 | * Cornell University Course Machine Learning for Data Science (https://www.cs.cornell.edu/courses/cs4786/2020sp/lectures.htm)
160 |
161 | * Cornell University Course Advanced Machine Learning (https://www.cs.cornell.edu/courses/cs6780/2019sp/)
162 |
163 | * Aachen University Course Advanced Machine Learning (http://www.vision.rwth-aachen.de/course/29/)
164 |
165 | * Cornell University Course Advanced Machine Learning Systems (https://www.cs.cornell.edu/courses/cs6787/2019fa/)
166 |
167 | * Chalmers University Course Applied Machine Learning (https://chalmers.instructure.com/courses/8685/)
168 |
169 | * McGill University Course Applied Machine Learning (https://cs.mcgill.ca/~wlh/comp551/schedule.html)
170 |
171 | * Cornell University Course Principles of Large-Scale Machine Learning (https://www.cs.cornell.edu/courses/cs4787/2020sp/)
172 |
173 | * Tuebingen University Course Probabilistic Machine Learning (https://uni-tuebingen.de/en/faculties/faculty-of-science/departments/computer-science/lehrstuehle/methods-of-machine-learning/probabilistic-machine-learning/)
174 |
175 | * UC Berkeley University Course Deep Unsupervised Learning
176 | (https://sites.google.com/view/berkeley-cs294-158-sp19/home)
177 |
178 |
179 |
180 | ##### Computer Vision
181 |
182 | * UC Berkeley University Course Computer Vision
183 | (https://inst.eecs.berkeley.edu/~cs280/sp18/)
184 |
185 | * Cornell University Course Introduction to Computer Vision
186 | (https://www.cs.cornell.edu/courses/cs5670/2020sp/lectures/lectures.html)
187 |
188 | * Brown University Course Introduction to Computer Vision
189 | (https://cs.brown.edu/courses/csci1430/)
190 |
191 | * Cornell University Course Computer Vision
192 | (https://www.cs.cornell.edu/courses/cs4670/2020sp/calendar-2020.html)
193 |
194 | * MIT CSAIL Advances in Computer Vision
195 | (http://6.869.csail.mit.edu/fa19/schedule.html)
196 |
197 | * EPFL University Course Computer Vision
198 | (https://moodle.epfl.ch/course/view.php?id=472)
199 |
200 | * Alberta University Course Computer Vision
201 | (https://webdocs.cs.ualberta.ca/~vis/courses/CompVis/)
202 |
203 | * New York University Course Computer Vision
204 | (https://cs.nyu.edu/~fergus/teaching/vision/index.html)
205 |
206 | * Illinois University Course Computer Vision
207 | (https://courses.engr.illinois.edu/cs543/sp2015/)
208 |
209 | * Aachen University Course Computer Vision
210 | (v2016 (más completa) http://www.vision.rwth-aachen.de/course/11/; v2019 http://www.vision.rwth-aachen.de/course/28/)
211 |
212 | * TUM University Course Computer Vision I: Variational Methods
213 | (https://vision.in.tum.de/teaching/online/cvvm)
214 |
215 | * TUM University Course Computer Vision II: Multiple View Geometry
216 | (https://vision.in.tum.de/teaching/online/mvg)
217 |
218 | * Michigan University Course Depp Learning for Computer Vision
219 | (https://francescopochetti.com/dl-for-computer-vision-justin-johnson-university-of-michigan-learning-pills/) (lista videos: https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r)
220 |
221 | * Stanford University Course Convolutional Neural Networks for Visual Recognition
222 | (http://cs231n.stanford.edu/syllabus.html)
223 |
224 | * Carnegie Mellon University Course Probabilistic Graphical Models
225 | (https://sailinglab.github.io/pgm-spring-2019/lectures/)
226 |
227 | * Technical University of Munich Course Advanced Deep Learning for Computer Vision
228 | (https://dvl.in.tum.de/teaching/adl4cv-ws18/)
229 |
230 |
231 |
232 | ##### Deep Learning
233 |
234 | * EPFL University Course Artificial Neural Networks
235 | (https://moodle.epfl.ch/course/view.php?id=15633)
236 |
237 | * MIT University Course Introduction to Deep Learning
238 | (http://introtodeeplearning.com/)
239 |
240 | * MIT University Course Deep Learning
241 | (https://deeplearning.mit.edu/)
242 |
243 | * New York University Course Deep Learning
244 | (https://atcold.github.io/pytorch-Deep-Learning/)
245 |
246 | * Stanford University Course Deep Learning
247 | (https://cs230.stanford.edu/syllabus/)
248 |
249 | * Brown University Course Deep Learning in Genomics
250 | (http://cs.brown.edu/courses/csci1850/lectures.html)
251 |
252 |
253 |
254 | ##### Natural Language Processing
255 |
256 | * Chalmers University Course Machine learning for Natural Language Processing
257 | (https://chalmers.instructure.com/courses/7916)
258 |
259 | * Chalmers University Course Deep Learning for Natural Language Processing
260 | (https://liu-nlp.github.io/dl4nlp/)
261 |
262 | * EPFL University Course Introduction to Natural Language Processing
263 | (https://coling.epfl.ch/)
264 |
265 | * EPFL University Course Computer Language Processing
266 | (https://lara.epfl.ch/w/cc19:top)
267 |
268 | * Cornell University Course Natural Language Processing
269 | (https://www.cs.cornell.edu/courses/cs5740/2020sp/schedule.html)
270 |
271 | * Cornell University Course Structured Prediction for NLP
272 | (https://www.cs.cornell.edu/courses/cs6741/2017fa/)
273 |
274 | * Stanford University Course Natural Language Processing with Deep Learning
275 | (http://web.stanford.edu/class/cs224n/index.html#schedule)
276 |
277 | * Brown University Course Introduction to Computational Linguistics
278 | (cs.brown.edu/courses/csci1460/)
279 |
280 |
281 |
282 | ##### Reinforcement Learning
283 |
284 | * UCL University Course on Reinforcement Learning
285 | (https://www.davidsilver.uk/teaching/)
286 |
287 | * UC Berkeley University Course Deep Reinforcement Learning
288 | (http://rail.eecs.berkeley.edu/deeprlcourse/)
289 |
290 | * EPFL University Course Markov chains and algorithmic applications
291 | (https://moodle.epfl.ch/course/view.php?id=15016)
292 |
293 | * Stanford University Course Deep Multi-Task and Meta Learning
294 | (http://cs330.stanford.edu/)
295 |
296 |
297 |
298 | ##### Artificial Intelligence
299 |
300 | * Cornell University Course Foundations of Artificial Intelligence
301 | (https://www.cs.cornell.edu/courses/cs4700/2020sp/)
302 |
303 | * Stanford University Course Artificial Intelligence: Principles and Techniques
304 | (https://stanford-cs221.github.io/spring2020/)
305 |
306 | * UC Berkeley University Course Introduction to Artificial Intelligence
307 | (https://inst.eecs.berkeley.edu/~cs188/fa19/index.html)
308 |
309 | * Brown University Course Artificial Intelligence
310 | (http://cs.brown.edu/courses/csci1410/)
311 |
312 |
313 |
314 | ##### Algorithmics
315 |
316 | * Brown University Course Design and Analysis Algorithms
317 | (http://cs.brown.edu/courses/csci1570/lectures.html)
318 |
319 |
320 |
321 |
322 |
323 |
324 | ### Programas/Webs suite
325 |
326 | Power BI (https://powerbi.microsoft.com/es-es/)
327 |
328 | Knime (https://www.knime.com/)
329 |
330 | Weka (https://www.cs.waikato.ac.nz/ml/weka/)
331 |
332 | Orange (https://orange.biolab.si/)
333 |
334 | RapidMiner (https://rapidminer.com/)
335 |
336 | BIRT (https://www.eclipse.org/birt/)
337 |
338 | Jaspersoft (https://www.jaspersoft.com/es/soluciones)
339 |
340 | Microsoft Azure (https://docs.microsoft.com/es-es/learn/azure/)
341 |
342 | IBM Watson Studio (https://www.ibm.com/cloud/watson-studio)
343 |
344 | Tableau (https://www.tableau.com/es-es)
345 |
346 | Metabase (https://metabase.com/)
347 |
348 | Knowage (https://www.knowage-suite.com/site/home/)
349 |
350 | Pentaho (Hitachi Vintara) (https://sourceforge.net/projects/pentaho/)
351 |
352 | DeepCognition (https://deepcognition.ai/)
353 |
354 | Google Data Studio (https://datastudio.google.com/overview)
355 |
356 | Qlik (https://www.qlik.com/us)
357 |
358 | Zoho Analytics (https://www.zoho.com/es-xl/analytics/)
359 |
360 | Google Cloud Datalab (https://cloud.google.com/datalab/)
361 |
362 | DataRobot (https://www.datarobot.com/)
363 |
364 | Dataiku Data Science Studio (DSS) (https://www.dataiku.com/dss/)
365 |
366 | BigML (https://bigml.com/)
367 |
368 | Google Colaboratory (https://colab.research.google.com/notebooks/welcome.ipynb)
369 |
370 | MLflow (https://mlflow.org/)
371 |
372 | MLkit app mobile (https://developers.google.com/ml-kit/)
373 |
374 |
375 |
376 |
377 |
378 | ### Datasets
379 |
380 | #### Imágenes
381 |
382 | Google repositorio imágenes etiquetadas (https://storage.googleapis.com/openimages/web/index.html)
383 |
384 | Visión artificial (https://www.visualdata.io/)
385 |
386 | Biometría (http://openbiometrics.org/)
387 |
388 | COCO (https://cocodataset.org/#home)
389 |
390 | ImageNet (http://www.image-net.org/)
391 |
392 | Gestos - HaGRID - HAnd Gesture Recognition Image Dataset (https://github.com/hukenovs/hagrid)
393 |
394 |
395 |
396 | #### Audio
397 |
398 | Google repositorio de audio (https://research.google.com/audioset/)
399 |
400 | Common Voice (https://commonvoice.mozilla.org/)
401 |
402 | VoxForge (http://voxforge.org/es)
403 |
404 |
405 |
406 | #### Texto
407 |
408 | The Stanford Question Answering Dataset (https://rajpurkar.github.io/SQuAD-explorer/)
409 |
410 | Amazon question/answer data (http://jmcauley.ucsd.edu/data/amazon/qa/)
411 |
412 | Recommender Systems Datasets (https://cseweb.ucsd.edu/~jmcauley/datasets.html)
413 |
414 | El corpus del español (https://www.corpusdelespanol.org/xs.asp)
415 |
416 | MAS Corpus (Corpus for Marketing Analysis in Spanish) (http://mascorpus.linkeddata.es/)
417 |
418 |
419 |
420 | #### Finanzas
421 |
422 | Quandl (https://www.quandl.com/)
423 |
424 |
425 |
426 | #### Conducción autónoma y coches
427 |
428 | Waymo (https://waymo.com/open/)
429 |
430 | KITTI 360 (http://www.cvlibs.net/datasets/kitti-360/)
431 |
432 | CompCars Dataset (https://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html)
433 |
434 | Stanford Cars Dataset (https://ai.stanford.edu/~jkrause/cars/car_dataset.html)
435 |
436 | BDD100K (https://www.bdd100k.com/)
437 |
438 | SHIFT (https://www.vis.xyz/shift/)
439 |
440 |
441 |
442 | #### Datos científicos/investigación
443 |
444 | Repositorios de datos recomendados por Scientific Data (https://www.nature.com/sdata/policies/repositories)
445 |
446 | Mendeley Data (https://data.mendeley.com/)
447 |
448 | Figshare (https://figshare.com/)
449 |
450 | Dryad Digital Repository (https://datadryad.org/stash)
451 |
452 | Harvard Dataverse (https://dataverse.harvard.edu/)
453 |
454 | Open Scientific Framework (https://osf.io/)
455 |
456 | Zenodo (https://zenodo.org/)
457 |
458 | Open Acces Directory (http://oad.simmons.edu/oadwiki/Data_repositories)
459 |
460 | IEEE DataPort (https://ieee-dataport.org/datasets)
461 |
462 |
463 |
464 | #### UAVs / Drones
465 |
466 | Open Aerial Map (https://openaerialmap.org/)
467 |
468 | Semantic Drone Dataset (https://www.tugraz.at/index.php?id=22387)
469 |
470 | SenseFly Datasets (https://www.sensefly.com/education/datasets/)
471 |
472 | VisDrone Dataset (https://github.com/VisDrone/VisDrone-Dataset)
473 |
474 | UAVid (https://uavid.nl/)
475 |
476 | Rice Seedling Dataset (https://github.com/aipal-nchu/RiceSeedlingDataset)
477 |
478 | UAV Sugarbeets 2015-16 Datasets (https://www.ipb.uni-bonn.de/data/uav-sugarbeets-2015-16/)
479 |
480 | ReforesTree (https://github.com/gyrrei/ReforesTree)
481 |
482 |
483 |
484 | #### Agricultura de precisión
485 |
486 | Plant Pathology 2021 - FGVC8 (https://www.kaggle.com/c/plant-pathology-2021-fgvc8/overview)
487 |
488 | PlantVillage Dataset (https://www.kaggle.com/abdallahalidev/plantvillage-dataset)
489 |
490 | PlantDoc: A Dataset for Visual Plant Disease Detection (https://github.com/pratikkayal/PlantDoc-Dataset)
491 |
492 | Plants_Dataset[99 classes] (https://www.kaggle.com/muhammadjawad1998/plants-dataset99-classes)
493 |
494 | V2 Plant Seedlings Dataset (https://www.kaggle.com/vbookshelf/v2-plant-seedlings-dataset)
495 |
496 | Eden Library (https://edenlibrary.ai/datasets)
497 |
498 | Pl@ntNet - Base de datos e identificación de plantas (https://identify.plantnet.org/es)
499 |
500 | A Crop/Weed Field Image Dataset (https://github.com/cwfid/dataset)
501 |
502 | DeepWeeds (https://github.com/AlexOlsen/DeepWeeds)
503 |
504 | Identification of Plant Leaf Diseases (https://data.mendeley.com/datasets/tywbtsjrjv/1)
505 |
506 | Perrenial Plants Detection (https://www.kaggle.com/benediktgeisler/perrenial-plants-detection)
507 |
508 | Global Wheat Challenge 2021 (https://www.kaggle.com/bendvd/global-wheat-challenge-2021)
509 |
510 | Flower Recognition (https://www.kaggle.com/aymenktari/flowerrecognition)
511 |
512 | Potato Disease Leaf Dataset(PLD) (https://www.kaggle.com/rizwan123456789/potato-disease-leaf-datasetpld)
513 |
514 | Potato and weeds (https://www.kaggle.com/jchrysanthemum/potato-and-weeds)
515 |
516 | Potato Plants Dataset (https://www.kaggle.com/ali7432/potato-plants-dataset)
517 |
518 | Potato Leaf Annotation (https://www.kaggle.com/rizwan123456789/potato-leaf-annotation)
519 |
520 | Cotton-Diseased or Fresh (https://www.kaggle.com/ananysharma/diseasecotton)
521 |
522 | Cucumber plant diseases dataset (https://www.kaggle.com/kareem3egm/cucumber-plant-diseases-dataset)
523 |
524 | Rice Leaf Diseases Dataset (https://www.kaggle.com/vbookshelf/rice-leaf-diseases)
525 |
526 | Rice Plant Dataset (https://www.kaggle.com/rajkumar898/rice-plant-dataset)
527 |
528 | Tomato Cultivars (https://www.kaggle.com/olgabelitskaya/tomato-cultivars)
529 |
530 | AppleScabFDs (https://www.kaggle.com/projectlzp201910094/applescabfds)
531 |
532 | AppleScabLDs (https://www.kaggle.com/projectlzp201910094/applescablds)
533 |
534 | Hops Classification (https://www.kaggle.com/scruggzilla/hops-classification)
535 |
536 | Plant semantic segmentation (https://www.kaggle.com/humansintheloop/plant-semantic-segmentation)
537 |
538 | Synthetic RGB-D data for plant segmentation (https://www.kaggle.com/harlequeen/synthetic-rgbd-images-of-plants)
539 |
540 | Synthetic RGB Data for Grapevine Detection (https://www.kaggle.com/carmenca/synthetic-rgb-data-for-grapevine-detection)
541 |
542 | Leaf disease segmentation dataset (https://www.kaggle.com/fakhrealam9537/leaf-disease-segmentation-dataset)
543 |
544 | Weed-AI Datasets (https://weed-ai.sydney.edu.au/datasets)
545 |
546 | Plant Phenotyping Datasets (https://www.plant-phenotyping.org/datasets-home)
547 |
548 | MinneApple: A Benchmark Dataset for Apple Detection and Segmentation (https://github.com/nicolaihaeni/MinneApple)
549 |
550 | Embrapa Wine Grape Instance Segmentation Dataset (https://github.com/thsant/wgisd)
551 |
552 | A Large-Scale Benchmark Dataset for Insect Pest Recognition (https://github.com/xpwu95/IP102)
553 |
554 | Sugar Beets 2016 (https://www.ipb.uni-bonn.de/data/sugarbeets2016/)
555 |
556 | TobSet: Tobacco Crop and Weeds Image Dataset (https://github.com/mshahabalam/TobSet)
557 |
558 | WE3DS: An RGB-D image dataset for semantic segmentation in agriculture (https://zenodo.org/record/7457983)
559 |
560 |
561 |
562 | #### Ganadería de precisión
563 |
564 | Animal Pose (vaca/oveja/caballo) (https://sites.google.com/view/animal-pose/)
565 |
566 | AwA Pose Dataset (vaca/oveja/caballo/cerdo) (https://github.com/prinik/AwA-Pose)
567 |
568 |
569 |
570 | #### Buscadores
571 |
572 | Google Search Dataset
573 | (https://toolbox.google.com/datasetsearch)
574 |
575 | Repositorio de la UCI para Machine Learning
576 | (http://mlr.cs.umass.edu/ml/)
577 |
578 | Microsoft Research Open Data
579 | (https://msropendata.com/)
580 |
581 | Buscador georreferenciado de datos abiertos (https://opendatainception.io/)
582 |
583 | Portal de datos abiertos de la UE
584 | (http://data.europa.eu/euodp/es/data/)
585 |
586 | Portal de datos abiertos de la FAO
587 | (http://www.fao.org/faostat/en/#data)
588 |
589 | Gobierno de España datos abiertos
590 | (http://datos.gob.es/es)
591 |
592 | Portal de datos abiertos Esri España
593 | (http://opendata.esri.es/)
594 |
595 | Búsqueda de repositorios de datos abiertos
596 | (https://www.re3data.org/)
597 |
598 |
599 |
600 |
601 |
602 | ### Modelos Preentrenados
603 |
604 | Model Zoo (https://modelzoo.co/)
605 |
606 | Tensorflow Model Garden (https://github.com/tensorflow/models)
607 |
608 | Tensorflow Hub (https://tfhub.dev/)
609 |
610 | Pytorch Hub (https://pytorch.org/hub/)
611 |
612 | MediaPipe Models (https://google.github.io/mediapipe/solutions/models)
613 |
614 | Piinto Model Zoo (https://github.com/PINTO0309/PINTO_model_zoo)
615 |
616 | ONNX Model Zoo (https://github.com/onnx/models)
617 |
618 | Jetson Zoo (https://www.elinux.org/Jetson_Zoo#Model_Zoo)
619 |
620 | Awesome CoreML Models (iOS Apple) (https://github.com/likedan/Awesome-CoreML-Models)
621 |
622 | Open Model Zoo (https://github.com/openvinotoolkit/open_model_zoo)
623 |
624 | YOLO v3 y otros detectores (https://pjreddie.com/darknet/yolo/)
625 |
626 | Kaggle Models (https://www.kaggle.com/models)
627 |
628 |
629 |
630 |
631 |
632 | ### APIs
633 |
634 | Listado de APIs de Google
635 | (https://developers.google.com/apis-explorer/#p/)
636 |
637 | Listado de APIs de IBM
638 | (https://developer.ibm.com/api/list)
639 |
640 | Listado de APIs de Microsoft
641 | (https://msdn.microsoft.com/en-us/library/ms123401.aspx)
642 |
643 | Búscadores de APIs por nombre o temática
644 | (https://any-api.com/ ; http://apis.io/ ; https://apis.guru/browse-apis/)
645 |
646 |
647 |
648 |
649 |
650 | ### Libros y documentos
651 |
652 | #### Español
653 |
654 | Aprender R: Iniciación y Perfeccionamiento (https://myrbooksp.netlify.app/)
655 |
656 | AnalizaR Datos políticos (https://arcruz0.github.io/libroadp/index.html)
657 |
658 | Econometría, Estadística y Machine Learning con R (https://econometria.wordpress.com/2017/07/23/estadistica-y-machine-learning-con-r/)
659 |
660 | Fundamentos de Ciencia de Datos con R (https://cdr-book.github.io/index.html)
661 |
662 | Interpretable Machine Learning *Black Box (Spanish Edition) (https://fedefliguer.github.io/AAI/)
663 |
664 | Introducción a estadística con R (https://bookdown.org/matiasandina/R-intro/)
665 |
666 | Introducción a la ciencia de datos - Análisis de datos y algoritmos de predicción con R (https://rafalab.github.io/dslibro/)
667 |
668 | Introducción a la Estadística para Científicos de Datos con R (https://analisisydecision.es/estadistica-data-scientist/index.html)
669 |
670 | Introducción al Análisis de Datos con R (https://rubenfcasal.github.io/intror/)
671 |
672 | Libro vivo de Ciencia de Datos (https://librovivodecienciadedatos.ai/)
673 |
674 | Manual de R (https://fhernanb.github.io/Manual-de-R/)
675 |
676 | Modelos Estadísticos Avanzados (https://pegasus.uprm.edu/~pedro.torres/book/)
677 |
678 | Modelos estadísticos con R (https://bookdown.org/j_morales/weblinmod/)
679 |
680 | Modelos lineales y aditivos en ecología (https://bookdown.org/fxpalacio/bookdown_curso/)
681 |
682 | R para Ciencia de Datos (https://es.r4ds.hadley.nz/)
683 |
684 | R para principiantes (https://bookdown.org/jboscomendoza/r-principiantes4/)
685 |
686 | R para profesionales (https://www.datanalytics.com/libro_r/)
687 |
688 |
689 |
690 | #### Inglés
691 |
692 | Apache Arrow R Cookbook (https://arrow.apache.org/cookbook/r/index.html)
693 |
694 | Behavior Analysis with Machine Learning Using R (https://enriquegit.github.io/behavior-free/index.html)
695 |
696 | Big Book of R (libro recopilatorio de libros) (https://www.bigbookofr.com/)
697 |
698 | Caret Package R (http://topepo.github.io/caret/index.html)
699 |
700 | Causal Inference in R (https://www.r-causal.org/)
701 |
702 | Data Science in Education using R (https://datascienceineducation.com/)
703 |
704 | Data Science Live Book R (https://livebook.datascienceheroes.com/)
705 |
706 | Deep Learning MIT Press book (https://www.deeplearningbook.org/)
707 |
708 | Deep Learning on Graphs (http://cse.msu.edu/~mayao4/dlg_book/)
709 |
710 | Dive into Deep Learning (https://d2l.ai/)
711 |
712 | Elegant and informative maps with tmap (https://r-tmap.github.io/tmap-book/)
713 |
714 | Elegant Graphics for Data Analysis with ggplot2 (https://ggplot2-book.org/index.html)
715 |
716 | Engineering Production-Grade Shiny Apps (https://engineering-shiny.org/)
717 |
718 | Explanatory Model Analysis (https://ema.drwhy.ai/)
719 |
720 | Feature Engineering A-Z (https://feaz-book.com/)
721 |
722 | Feature Engineering and Selection (http://www.feat.engineering/)
723 |
724 | Forecasting and Analytics with ADAM (https://openforecast.org/adam/)
725 |
726 | Forecasting: Principles and Practice (R) (Second Edition: https://otexts.org/fpp2/; Third Edition: https://otexts.org/fpp3/)
727 |
728 | Fundamentals of Data Visualization (https://clauswilke.com/dataviz/index.html)
729 |
730 | Geocomputation with R (https://geocompr.robinlovelace.net/)
731 |
732 | Graph Representation Learning Book (https://www.cs.mcgill.ca/~wlh/grl_book/)
733 |
734 | Interactive web-based data visualization with R, plotly, and shiny (https://plotly-r.com/index.html)
735 |
736 | Interpretable Machine Learning *Black Box* (https://christophm.github.io/interpretable-ml-book/)
737 |
738 | Learning Statistical Models Through Simulation in R (https://psyteachr.github.io/stat-models-v1/index.html)
739 |
740 | Limitations of Interpretable Machine Learning (https://compstat-lmu.github.io/iml_methods_limitations/)
741 |
742 | Machine Learning Engineering Book (http://www.mlebook.com/wiki/doku.php)
743 |
744 | Mastering Spark with R (https://therinspark.com/)
745 |
746 | Mastering Shiny (https://mastering-shiny.org/)
747 |
748 | Modern R with the tidyverse (https://b-rodrigues.github.io/modern_R/)
749 |
750 | Natural Language Processing with Python (https://www.nltk.org/book/)
751 |
752 | Outstanding User Interfaces with Shiny (https://unleash-shiny.rinterface.com/index.html)
753 |
754 | R Base Graphics (http://rstudio-pubs-static.s3.amazonaws.com/7953_4e3efd5b9415444ca065b1167862c349.html)
755 |
756 | R Graphics Cookbook (https://r-graphics.org/)
757 |
758 | R Markdown: The Definitive Guide (https://bookdown.org/yihui/rmarkdown/)
759 |
760 | Statistical rethinking with brms, ggplot2, and the tidyverse: Second edition (https://bookdown.org/content/4857/)
761 |
762 | Supervised Machine Learning for Science (https://ml-science-book.com/)
763 |
764 | The Data Engineering Cookbook (https://github.com/andkret/Cookbook)
765 |
766 | The Hundred-Page Machine Learning Book (http://themlbook.com/wiki/doku.php)
767 |
768 | Tidy Modeling with R (https://www.tmwr.org/)
769 |
770 | UC Business Analytics R Programming Guide (http://uc-r.github.io/)
771 |
772 | YaRrr! The Pirate’s Guide to R (https://bookdown.org/ndphillips/YaRrr/)
773 |
774 |
775 |
776 |
777 |
778 | ### Blogs
779 |
780 | #### Español
781 |
782 | Ciencia y datos (https://medium.com/datos-y-ciencia; https://www.cienciaydatos.org/)
783 |
784 | Materiales de RStudio en Español (https://resources.rstudio.com/espanol)
785 |
786 | Joaquin Amat R (https://rpubs.com/Joaquin_AR)
787 |
788 | Ciencia de Datos, Estadística, Visualización y Machine Learning (https://www.cienciadedatos.net/)
789 |
790 | Escuela de Datos Vivos (https://blog.escueladedatosvivos.ai/)
791 |
792 | Machine Learning para todos (http://machinelearningparatodos.com/blog/)
793 |
794 | Foro argentino de R (https://datosenr.org/)
795 |
796 | Tutoriales R & Python (https://datascienceplus.com/)
797 |
798 | LUCA Telefónica (https://data-speaks.luca-d3.com/)
799 |
800 | Análisis de datos y Machine Learning-dlegorreta (https://dlegorreta.wordpress.com/)
801 |
802 | Aprender machine learning (http://www.aprendemachinelearning.com/)
803 |
804 | BI y Machine Learning con Diego Calvo (http://www.diegocalvo.es/)
805 |
806 | Ejemplos de Machine Learning y Data Mining con R (http://apuntes-r.blogspot.com/)
807 |
808 | Aprendiendo sobre IA-Lidgi González (http://ligdigonzalez.com/)
809 |
810 | Actualidad de Big data e Inteligencia artificial (http://www.sorayapaniagua.com/)
811 |
812 | Blog sobre python (http://www.pythondiario.com/)
813 |
814 | Visión por computador (https://carlosjuliopardoblog.wordpress.com/)
815 |
816 | DataSmarts (https://datasmarts.net/es/)
817 |
818 | OMES, Visión por computador (https://omes-va.com/)
819 |
820 | Inteligencia artificial en la práctica (https://iartificial.net/)
821 |
822 | Tutoriales sobre ML y librerías usadas en ML (https://www.interactivechaos.com/tutoriales)
823 |
824 | Blog con información sobre big data, aprendizaje automático e ia (http://sitiobigdata.com/)
825 |
826 | Blog sobre fundamentos de ML e IA (https://www.juanbarrios.com/machine-learning-aprendizaje-maquina/)
827 |
828 | Todo AI (https://todoia.es/)
829 |
830 | Dream Learning blog (https://dreamlearning.ai/blog/)
831 |
832 | Repositorio de cursos con diapositivas de IA, ML, DL - Fernando Berzal (https://elvex.ugr.es/courses.html)
833 |
834 | Blog del profesor Fernando Sancho Caparrini (ETSI) sobre IA, ML, DL (http://www.cs.us.es/~fsancho/)
835 |
836 | Blog del profesor Jordi Torres (https://torres.ai/blog/)
837 |
838 | Blogg del profesor Eduardo Morales (https://ccc.inaoep.mx/~emorales/)
839 |
840 | Escuela de Datos Vivos (https://escueladedatosvivos.ai/blog)
841 |
842 | Blog Rubiales Alberto Medium (https://rubialesalberto.medium.com/)
843 |
844 | Blog Javi GG (https://javi897.github.io/)
845 |
846 | Blog Ander Fernández (https://anderfernandez.com/blog/)
847 |
848 | Aprender Big Data (https://aprenderbigdata.com/)
849 |
850 | Hablando en Data (https://hablandoendata.com/)
851 |
852 | Análisis y decisión (https://analisisydecision.es/)
853 |
854 | Mis apuntes Data Science (MDS) (https://misapuntesdedatascience.es/)
855 |
856 | Hablamos R (https://hablamosr.blogspot.com/)
857 |
858 | #### Inglés
859 |
860 | ClaoudML (https://www.claoudml.com/)
861 |
862 | Listen Data (https://www.listendata.com/)
863 |
864 | Data Science Heroes (https://blog.datascienceheroes.com/)
865 |
866 | Machine Learning para dispositivos móviles (https://heartbeat.fritz.ai/)
867 |
868 | Tutoriales sobre aprendizaje automático en R y Python (https://www.machinelearningplus.com/)
869 |
870 | Visión artificial con OpenCV (https://www.learnopencv.com/; github: https://github.com/spmallick/learnopencv/blob/master/README.md?ck_subscriber_id=323792569)
871 |
872 | CV-Tricks Visión artificial (https://cv-tricks.com/)
873 |
874 | Business Intelligence y Data Science (http://www.dataprix.com/)
875 |
876 | Towards AI (https://medium.com/towards-artificial-intelligence)
877 |
878 | Data Science Dojo (https://blog.datasciencedojo.com/)
879 |
880 | Machine Learning Mastery (https://machinelearningmastery.com/)
881 |
882 | Open Data Science (https://opendatascience.com/)
883 |
884 | 365 Data Science Blog (https://365datascience.com/blog/)
885 |
886 | PyImageSearch (https://www.pyimagesearch.com/)
887 |
888 | deepwizAI (https://www.deepwizai.com/)
889 |
890 | ML in Production
891 | (http://mlinproduction.com/)
892 |
893 | R in Production
894 | (https://www.rinproduction.com/en/)
895 |
896 |
897 |
898 |
899 |
900 | ### Canales de Youtube
901 |
902 | Dot CSV
903 | (https://www.youtube.com/channel/UCy5znSnfMsDwaLlROnZ7Qbg)
904 |
905 | Lidgi González
906 | (https://www.youtube.com/channel/UCLJV54sFqPiH4MYcJKvGesg)
907 |
908 | Descubriendo la inteligencia artificial
909 | (https://www.youtube.com/channel/UCrEM9nM7pxy0TtgDyTXljFQ)
910 |
911 | Xpikuos - ML / IA / Robótica
912 | (https://www.youtube.com/channel/UCCmHFfUhcgZHenBWRzSEB0w/featured)
913 |
914 | Luca Talks
915 | (https://www.youtube.com/channel/UCiz4K2MbbIEAr31L3Wps3Ew/featured)
916 |
917 | cctmexico
918 | (https://www.youtube.com/playlist?list=PLgHCrivozIb0HQ9oPRLVqw5scdIG-AxQL)
919 |
920 | AMP Tech
921 | (https://www.youtube.com/playlist?list=PLA050nq-BHwMr0uk7pPJUqRgKRRGhdvKb)
922 |
923 | Luis Serrano
924 | (https://www.youtube.com/playlist?list=PLs8w1Cdi-zvZ43xD_AA-eAuEW1FLK0cef)
925 |
926 | Capacítate para el empleo
927 | (https://www.youtube.com/watch?v=kKm1cXSyLqk)
928 |
929 | Full Stack Deep Learning
930 | (https://www.youtube.com/c/FullStackDeepLearning/featured)
931 |
932 | Applied Machine Learning 2020
933 | (https://www.youtube.com/playlist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM)
934 |
935 | Statistical Machine Learning
936 | (https://www.youtube.com/playlist?list=PL05umP7R6ij2XCvrRzLokX6EoHWaGA2cC)
937 |
938 | Advanced Deep Learning & Reinforcement Learning
939 | (https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs)
940 |
941 | Data Driven Control with Machine Learning
942 | (https://www.youtube.com/playlist?list=PLMrJAkhIeNNQkv98vuPjO2X2qJO_UPeWR)
943 |
944 | ECE AI Seminar Series
945 | (https://www.youtube.com/playlist?list=PLhwo5ntex8iY9xhpSwWas451NgVuqBE7U)
946 |
947 | CSEP 546 - Machine Learning
948 | (https://www.youtube.com/playlist?list=PLTPQEx-31JXj87XLsYutYGKw6K9dNaD36)
949 |
950 | CS285 Fall 2019
951 | (https://www.youtube.com/playlist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A)
952 |
953 | Deep Bayes
954 | (https://www.youtube.com/playlist?list=PLe5rNUydzV9QHe8VDStpU0o8Yp63OecdW)
955 |
956 | CMU Neural Nets for NLP
957 | (https://www.youtube.com/playlist?list=PL8PYTP1V4I8CJ7nMxMC8aXv8WqKYwj-aJ)
958 |
959 | Workshop on New Directions in Reinforcement Learning and Con
960 | (https://www.youtube.com/playlist?list=PLdDZb3TwJPZ61sGqd6cbWCmTc275NrKu3)
961 |
962 | Theoretical Machine Leraning Lecture Series
963 | (https://www.youtube.com/playlist?list=PLdDZb3TwJPZ5VLprf2VUfC0h1zOGvV_gz)
964 |
965 |
966 |
967 |
968 |
969 | ### Plataformas de aprendizaje
970 |
971 | Coursera (https://www.coursera.org/)
972 |
973 | edX (https://www.edx.org/es)
974 |
975 | miriadaX (https://miriadax.net/home)
976 |
977 | Udemy (https://www.udemy.com/)
978 |
979 | Udacity (https://eu.udacity.com/)
980 |
981 | SimpliLearn (https://www.simplilearn.com/)
982 |
983 | FutureLearn (https://www.futurelearn.com/)
984 |
985 | freeCodecamp (https://www.freecodecamp.org/)
986 |
987 | Codecademy (https://www.codecademy.com/)
988 |
989 | Cursos python en español (https://unipython.com/)
990 |
991 | Katacoda (www.katacoda.com)
992 |
993 | 365 Data Science (https://365datascience.com/)
994 |
995 | Linux Academy (https://linuxacademy.com/)
996 |
997 | PluralSight (www.pluralsight.com)
998 |
999 | Intellipaat (https://intellipaat.com/)
1000 |
1001 |
1002 |
1003 |
1004 |
1005 | ### Otros programas/recursos
1006 |
1007 | Papers with Code (https://paperswithcode.com/)
1008 |
1009 | Google App Maker (https://blog.google/outreach-initiatives/education/build-custom-apps-your-school-app-maker/)
1010 |
1011 | Cisco Academy Cursos (https://www.netacad.com/es/courses/all-courses)
1012 |
1013 | Estado del arte de la IA (https://www.stateoftheart.ai/)
1014 |
1015 | Generar webs estáticas con Rmarkdown (https://github.com/rstudio/blogdown)
1016 |
1017 | Escribir libros con Rmarkdown (https://github.com/rstudio/bookdown)
1018 |
1019 | Certificaciones Data Science (https://digitaldefynd.com/best-data-science-certification-course-tutorial/)
1020 |
1021 | Diccionario tecnológico IA & Big Data (https://luca-d3.com/es/diccionario-tecnologico/index.html)
1022 |
1023 | Aprendizaje interactivo de Probabilidad básica (https://seeing-theory.brown.edu/)
1024 |
1025 | Google Talk to Books (https://books.google.com/talktobooks/)
1026 |
1027 | Búsqueda repositorios Github (https://gitdiscoverer.shinyapps.io/rstudio-shiny-contest/)
1028 |
1029 | Asociación Española para la Inteligencia Artificial (AEPIA) (http://www.aepia.org/aepia/index.php)
1030 |
1031 | Ayuda interactiva para elección de gráficos (https://www.data-to-viz.com/index.html)
1032 |
1033 | Introducción visual al aprendizaje automático (http://www.r2d3.us/)
1034 |
1035 | Creación de infografías (Infogram, Piktochart, Canvas) (https://infogram.com/; https://piktochart.com/; https://www.canva.com/es_es/)
1036 |
1037 | Presentaciones interactivas (https://www.genial.ly/es)
1038 |
1039 | Galería de gráficos en R (https://www.r-graph-gallery.com/)
1040 |
1041 | Galería de gráficos en Python (https://www.python-graph-gallery.com/)
1042 |
1043 | Página web sobre estadística con R (http://www.sthda.com/english/)
1044 |
1045 |
1046 |
1047 |
1048 |
1049 | ### Paquetes R
1050 |
1051 | #### Importación
1052 |
1053 | readr, readxl, XML, jsonlite, httr, DBI
1054 |
1055 | #### Exploración
1056 |
1057 | DataExplorer, GGally, summarytools, skimr, funModeling, radiant, anomalize, correlationfunnel, corrplot
1058 |
1059 | #### Limpieza y manipulación
1060 |
1061 | tidyr, dplyr, dbplyr, data.table, dtplyr (data.table), datapasta, forcats, janitor, lubridate, stringr, purrr, drake (pipelines)
1062 |
1063 | #### Visualización
1064 |
1065 | ggplot2, plotly, ggstatsplot (jjstatsplot con GUI), trelliscopejs, esquisse, ggplotgui, gganimate, ggforce, rayshader, r2d3
1066 |
1067 | #### Geoespaciales
1068 |
1069 | sen2r, rgee, ggmap, gstat, spatstat, sf, sp, raster, rgdal, leaflet, RQGIS3, tmap, rgeos, whiteboxR
1070 |
1071 | #### Estadística y Machine learning/Deep Learning
1072 |
1073 | infer, rstatix, tidymodels, stacks, caret, mlr, h2o, glmnet, tensorflow, keras, ruta (unsupervised DL), xgboost, lightgbm, parsnip, recipes, recommenderlab
1074 |
1075 | #### Series temporales
1076 |
1077 | forecast, fable, feasts, timetk, maltese, modeltime, modeltime.ensemble, modeltime.gluonts, prophet
1078 |
1079 | #### Procesamiento del lenguaje natural
1080 |
1081 | tidytext, text2vec, quanteda
1082 |
1083 | #### Interpretabilidad de modelos
1084 |
1085 | iml, DALEX, LIME, shapr, modelStudio (interactive dashboard), DrWhy.AI, modelDown
1086 |
1087 | #### Paralelización y Big Data
1088 |
1089 | parallel, foreach, bigmemory, bigtabulate, biganalytics, iotools, sparklyr, rsparkling, furrr
1090 |
1091 | #### Despliegue
1092 |
1093 | plumber, rdocker, cloudml, cloudyr, aws.s3, Paws (AWS), AzureR (familia paquetes)
1094 |
1095 | #### Reporting
1096 |
1097 | knitr, rmarkdown, shiny, flexdashboard, shinydashboard, shinymanager, Microsoft365R
1098 |
1099 | #### WebScrapping
1100 |
1101 | rvest, RSelenium
1102 |
1103 | #### Otros
1104 |
1105 | reticulate, pdftools, tabulizer, tesseract, utils, onnx, aurelius, ArenaR, fs, fusen
1106 |
1107 |
1108 |
1109 |
1110 |
1111 | ### Paquetes Python
1112 |
1113 | #### Importación
1114 |
1115 | SQLAlchemy, pandas, PyMongo
1116 |
1117 | #### Exploración
1118 |
1119 | pandas, bamboolib, pandas_profiling, D-Tale, pandasgui, pandas_ui, sweetviz, funpymodeling, autoplotter, lux, mito, QuickDA
1120 |
1121 | #### Limpieza y manipulación
1122 |
1123 | pandas, numpy, scipy, featuretools, feature-engine, featurewiz, siuba (R dplyr syntax), kangas (pandas for computer vision)
1124 |
1125 | #### Automatización
1126 |
1127 | PyAutoGUI, AutoPy, rpa, rpaframework, watchdog
1128 |
1129 | #### Visualización
1130 |
1131 | seaborn, bokeh, matplotlib, plotly, plotnine (R ggplot syntax)
1132 |
1133 | #### Geoespaciales
1134 |
1135 | GeoPandas, PyQGIS, GDAL, Folium, ipyleaflet, geemap, WhiteboxTools, leafmap
1136 |
1137 | #### Estadística y Machine Learning/Deep Learning
1138 |
1139 | statsmodels, scikit-learn, imbalanced-learn, PyOD, pycaret, Keras, Tensorflow, PyTorch, skorch (sklearn + pytorch), xgboost, ngboost, Hyperopt, scikit-optimize (skopt), DEAP, TPOT
1140 |
1141 | #### Series temporales
1142 |
1143 | statsmodels.tsa, Darts, sktime, skforecast, Kats, AutoTS, tslearn, tsfresh, fbprophet, GluonTS, neuralprophet, tsai, nixtla
1144 |
1145 | #### Procesamiento del lenguaje natural
1146 |
1147 | NLTK, Gensim, spaCy, CoreNLP, TextBlob, polyplot
1148 |
1149 | #### Aprendizaje por refuerzo
1150 |
1151 | Tensorforce, TFAgents, RLlib, Stable Baselines, RL_Coach, Coax
1152 |
1153 | #### Interpretabilidad de modelos
1154 |
1155 | yellowbrick, LIME, ELI5, MLxtend, Shapash, DrWhy.AI
1156 |
1157 | #### Paralelización y Big Data
1158 |
1159 | Dask, Vaex, modin, PySpark, optimus, koalas, polars
1160 |
1161 | #### Despliegue
1162 |
1163 | boto3 (AWS), BentoML, FastAPI, Flask
1164 |
1165 | #### Reporting
1166 |
1167 | dash, streamlit, stlite (Serverless Streamlit), gradio, panel, voilà, PyWebIO, mia, taipy, shinyexpress
1168 |
1169 | #### Monitoring/Drifting
1170 |
1171 | frouros, alibi-detect, evidently
1172 |
1173 | #### WebScrapping
1174 |
1175 | BeautifulSoup, scrapy, selenium
1176 |
1177 | ##### GUIs
1178 |
1179 | Gooey, PySimpleGUI, PyGTK, wxPython, PyQT, Tkinter, PySide2
1180 |
1181 | #### Otros
1182 |
1183 | onnx
1184 |
1185 |
1186 |
1187 |
1188 |
1189 | ### Casos de uso
1190 |
1191 | Recomendaciones personalizadas, análisis social media, predicción de ventas, mantenimiento predictivo, automatización de procesos, detección de fraudes, análisis financiero, servicios asistidos de atención al cliente, procesamiento del lenguaje natural, tratamiento de salud personalizado, traducción, audio y voz, etc.
1192 |
1193 |
1194 |
1195 |
1196 |
1197 | ### Automated machine learning
1198 |
1199 | LazyPredict (https://lazypredict.readthedocs.io/en/latest/index.html)
1200 |
1201 | AutoML (https://cloud.google.com/automl/?hl=es-419)
1202 |
1203 | Auto-Keras (https://autokeras.com/)
1204 |
1205 | Auto-Sklearn (https://automl.github.io/auto-sklearn/master/)
1206 |
1207 | Auto-Weka (https://www.cs.ubc.ca/labs/beta/Projects/autoweka/)
1208 |
1209 | AutoGluon (https://autogluon.mxnet.io/)
1210 |
1211 | AutoGOAL (https://autogoal.github.io/)
1212 |
1213 | MLBox (https://mlbox.readthedocs.io/en/latest/)
1214 |
1215 | TPOT (https://github.com/EpistasisLab/tpot)
1216 |
1217 | H2O AutoML (http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html)
1218 |
1219 | TransmogrifAI (https://transmogrif.ai/)
1220 |
1221 | Glaucus (https://github.com/ccnt-glaucus/glaucus)
1222 |
1223 | EvalML (https://github.com/alteryx/evalml)
1224 |
1225 |
1226 |
1227 |
1228 |
1229 | ### Embedded Machine Learning
1230 |
1231 | * Introduction to Embedded Machine Learning (https://www.coursera.org/learn/introduction-to-embedded-machine-learning)
1232 | * Computer Vision with Embedded Machine Learning (https://www.coursera.org/learn/computer-vision-with-embedded-machine-learning)
1233 | * Device-based Models with TensorFlow Lite (https://www.coursera.org/learn/device-based-models-tensorflow)
1234 | * Tiny Machine Learning (TinyML) (https://www.edx.org/professional-certificate/harvardx-tiny-machine-learning)
1235 | * MLOps for Scaleing TinyML (https://www.edx.org/es/course/mlops-for-scaling-tinyml)
1236 | * CS249r: Tiny Machine Learning - Applied Machine Learning for Embedded IoT Devices (https://sites.google.com/g.harvard.edu/tinyml/lectures)
1237 | * Intel® Edge AI for IoT Developers (https://www.udacity.com/course/intel-edge-ai-for-iot-developers-nanodegree--nd131)
1238 | * Intel® Edge AI Fundamentals with OpenVINO (https://www.udacity.com/course/intel-edge-AI-fundamentals-with-openvino--ud132)
1239 | * Intel® Edge AI Certification (https://www.intel.com/content/www/us/en/developer/tools/devcloud/edge/learn/certification.html)
1240 | * Introduction to TensorFlow Lite (https://www.udacity.com/course/intro-to-tensorflow-lite--ud190)
1241 | * Embedded and Distributed AI TEDS20 Spring 2020 (https://www.youtube.com/playlist?list=PLyulI6o7oOtycIT15i_I2_mhuLxnNvPvX)
1242 | * Edge Impulse - Tutorials (https://www.youtube.com/playlist?list=PL7VEa1KauMQp9bQdo2jLlJCdzprWkc7zC)
1243 | * Arduino TensorFlow Lite Tutorials (https://github.com/arduino/ArduinoTensorFlowLiteTutorials/)
1244 | * Getting Started with Machine Learning at the Edge on Arm (https://www.coursera.org/learn/getting-started-with-machine-learning-at-the-edge-on-arm)
1245 |
1246 |
1247 | #### Optimización de modelos para dipositivos embebidos (edge devices) (arduino, raspberry pi, Jetson Nano, ESP32, móviles....)
1248 |
1249 | * Tensorflow Lite Optimization (https://www.tensorflow.org/model_optimization)
1250 | * TensorRT optimization (https://developer.nvidia.com/tensorrt)
1251 | * OpenVINO optimization (https://docs.openvino.ai/latest/openvino_docs_model_optimization_guide.html#doxid-openvino-docs-model-optimization-guide)
1252 |
1253 |
1254 |
1255 |
1256 |
1257 | ### Spatial Data Science / Machine Learning
1258 |
1259 | * Spatial Data Science and Applications (https://www.coursera.org/learn/spatial-data-science)
1260 | * Spatial Data Science with R (http://rspatial.org/index.html)
1261 | * Spatial Data Science (Luc Anselin, 2017) (https://www.youtube.com/playlist?list=PLzREt6r1Nenlu-MBaxCRL2KZNk62n7o1g)
1262 | * Geographic Data Science (https://darribas.org/gds_course/content/home.html)
1263 | * Geographic Data Science with PySAL and the PyData Stack (https://geographicdata.science/book/intro.html)
1264 | * Spatial Data Science: The New Frontier in Analytics (https://www.esri.com/training/catalog/5d76dcf7e9ccda09bef61294/spatial-data-science%3A-the-new-frontier-in-analytics/)
1265 | * Introducing Machine Learning for Spatial Data Analysis (https://www.analyticsvidhya.com/blog/2021/03/introducing-machine-learning-for-spatial-data-analysis/)
1266 |
1267 | #### Libros
1268 |
1269 | * Introduction to Spatial Data Programming with R (https://geobgu.xyz/r/index.html)
1270 | * Geocomputation with R (https://geocompr.robinlovelace.net/)
1271 | * Spatial Data Science with applications in R (https://keen-swartz-3146c4.netlify.app/)
1272 | * Spatial Statistics for Data Science: Theory and Practice with R (https://www.paulamoraga.com/book-spatial/)
1273 | * Data Analysis and Visualization with R: Spatial (http://www.geo.hunter.cuny.edu/~ssun/R-Spatial/)
1274 | * Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny (https://www.paulamoraga.com/book-geospatial/index.html)
1275 | * Guía para el análisis de datos espaciales. Aplicaciones en agricultura (https://www.agro.unc.edu.ar/~estadisticaaplicada/GpADEAA/)
1276 |
1277 |
1278 |
1279 |
1280 |
1281 | ### Herramientas de Anotación
1282 |
1283 | * Labelbox (https://labelbox.com/)
1284 | * LabelImg (https://github.com/tzutalin/labelImg)
1285 | * MakesenseAI (https://www.makesense.ai/)
1286 | * Scalabel (https://www.scalabel.ai/)
1287 | * CVAT (https://www.cvat.ai/)
1288 |
1289 |
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1 | ## 1. Aprendizaje supervisado
2 |
3 | ### 1.1. *Regresión*
4 |
5 | Regresión lineal, bayesiana, Lasso, Ridge, polinómica, Elastic Net, GBM, XGBoost, LightGBM, CatBoost, AdaBoost, árbol de decisión, Random Forest, Redes Neuronales, Series temporales (Exponential smoothing, AR, MA, ARMA, ARIMA, SARIMA, GARCH).
6 |
7 | ### 1.2. *Clasificación*
8 |
9 | kNN, Naive Bayes, SVM, regresión logística, GBM, XGBoost, LightGBM, CatBoost, AdaBoost, árbol de decisión,
10 | Random Forest, Redes Neuronales.
11 |
12 |
13 |
14 | ## 2. Aprendizaje no supervisado
15 |
16 | ### 2.1. *Agrupamiento (clustering)*
17 |
18 | K-means, Hierarchical clustering, K-medoids (PAM), Expectation-Maximization, DBSCAN, Gaussian Mixture Models, Fuzzy c-Means,
19 | Mean-shift
20 |
21 | ### 2.2. *Reglas de Asociación*
22 |
23 | Apriori, Eclat, FP-Growth
24 |
25 | ### 2.3. *Patrones Secuenciales*
26 |
27 | GSP, PSP, SPADE, DEGSeq, FreeSpan, PrefixSpan, CloSpan, SSMiner, IncSpan, BIDE, MG-FSM
28 |
29 | ### 2.4 *Reducción de dimensionalidad*
30 |
31 | tSNE, PCA, PLS, PCR, LSA, SVD, LDA, ICA, UMAP, ISOMAP, CCA, PCR, NMF (Non-negative matrix factorization), GLRM (Generalized Low Rank Models)
32 |
33 | ### 2.5 *Detección de anomalías*
34 |
35 | Isolation forest, one-class SVM, Cluster analysis, PCA-based Anomaly detection, Local outlier Factor (LoF), Métricas de distancia1
36 |
37 | 1*Numéricas*: euclídea, euclídea normalizada, Manhattan, Canberra, Minkowski, Mahalanobis, Chebyshev; *Categóricas*: chi-cuadrado, Levenshtein, Hamming, Simple matching coefficient, Índice de Jaccard; *Mixta*: Gower
38 |
39 |
40 |
41 | ## 3. Aprendizaje reforzado
42 |
43 | Algoritmos genéticos, SARSA, Q-learning, A3C, DeepQ-network, Monte Carlo, Proceso de decisión de Markov
44 |
45 |
46 |
47 | ## 4. Otros
48 |
49 | ### 4.1. *Selección de variables (feature selection)*
50 |
51 | *Indirecta o filtrado*: Correlación de pearson, LDA, ANOVA, Chi cuadrado, Welch t-prueba, prueba F de Fisher.
52 |
53 | *Directa o wrapper*: Recursive Feature Elimination, Algoritmos genéticos, Simulated annealing, Best Subset Selection, Forward Stepwise Selection, Backward Stepwise Selection, Sequential Feature Selector.
54 |
55 | ### 4.2. *Sistemas de recomendación*
56 |
57 | Filtro colaborativo (Collaborative filtering), filtro basado en contenido (Content-based filtering), mixtos
58 |
59 | ### 4.3. *Visión artificial*
60 |
61 | * Descriptores y/o detectores: HOG, BRIEF, ORB (FAST + BRIEF), BRISK, SIFT, SURF, KAZE, AKAZE, FREAK, GLOH, LESH, LDB, MSER, DAISY, PCBR, LoG, DoG, DoH, VLAD, Viola-Jones, Lucas-Kanade, Horn-Schunk, filtro de Kalman, filtros de Haar
62 |
63 | ***
64 |
65 |
66 |
67 | 
68 |
69 |
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