Contact Information
support@logyc.co
Phone
650.665.6409
Working Hours
We are happy to meet you during our working hours. Please make an appointment.
Contact Information
support@logyc.co
Phone
650.665.6409
Working Hours
We are happy to meet you during our working hours. Please make an appointment.
Resources: Building Intelligent Systems
Step 1: Create a Data Lake
Step 2: Apply Machine Learning Algorithms
Deep Learning
– Deep Boltzmann Machine (DBM)
– Deep Belief Networks (DBN)
– Convolutional Neural Network (CNN)
– Stacked Auto-Encoders
Neural Networks
– Radial Basis Function Network (RBFN)
– Perceptron
– Backpropagation
– Hopfield Network
Ensemble Learning
– Random Forest
– Gradient Boosting Machines (GBM)
– Boosting
– Bootstrap Aggregation (Bagging)
– AdaBoost
– Stacked Generalization (Blending)
– Gradient Boosted Regression Trees (GBRT)
Regularization
– Ridge Regression
– Least Absolute Shrinkage and Selection Operator (LASSO)
– Elastic Net
– Least Angle Regression (LARS)
Rule-Based System
– Cubist
– One Rule (OneR)
– Zero Rule (ZeroR)
– Repeated Incremental Pruning to Produce Error Reduction (RIPPER)
Regression Analysis
– Linear Regression
– Ordinary Least Squares Regression (OLSR)
– Stepwise Regression
– Multivariate Adaptive Regression Splines (MARS)
– Locally Weighted Scatterplot Smoothing (LOWESS)
– Logistic Regression
Bayesian Inference
– Naive Bayes
– Averaged One-Dependence Estimators (AODE)
– Bayesian Belief Network (BBN)
– Gaussian Naive Bayes
– Multinomial Naive Bayes
– Bayesian Network (BN)
Decision Tree
– Classification and Regression Tree (CART)
– Iterative Dichotomiser 3 (ID3)
– C4.5
– C5.0
– Chi-squared Automatic Interaction Detection (CHAID)
– Decision Stump
– Conditional Decision Trees
– M5
Dimensionality Reduction
– Principal Component Analysis (PCA)
– Partial Least Squares Regression (PLSR)
– Sammon Mapping
– Multidimensional Scaling (MDS)
– Projection Pursuit (PP)
– Principal Component Regression (PCR)
– Partial Least Square Discriminant Analysis
– Mixture Discriminant Analysis (MDA)
– Quadratic Discriminant Analysis (QDA)
– Flexible Discriminant Analysis (FDA)
– Linear Discriminant Analysis (LDA)
Instance-Based Learning
– k-Nearest Neighbors (kNN)
– Learning Vector Quantization (LVQ)
– Self-Organizing Map (SOM)
– Locally Weighted Learning (LWL)
Cluster Analysis
– k-Means
– k-Medians
– Expectation Maximization (EM)
– Hierarchical ClusteringÂ
Step 3: Tell Us If You Did NOT Get The Results You Desired
