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Supply Chain & Product Life Cycle Augmentation

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650.665.6409

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Contact Information

support@logyc.co

Request a Quote

Supply Chain & Product Life Cycle Augmentation

* Please Fill Required Fields *
img

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

Integrating data from different sources is where most data projects start.

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 Clustering 

Step 3: Tell Us If You Did NOT Get The Results You Desired