diff --git a/_ml-commons-plugin/algorithms.md b/_ml-commons-plugin/algorithms.md
index 685b779e..c0a6ca35 100644
--- a/_ml-commons-plugin/algorithms.md
+++ b/_ml-commons-plugin/algorithms.md
@@ -65,7 +65,7 @@ Linear regression maps the linear relationship between inputs and outputs. In ML
 
 Parameter | Type   | Description | Default Value
 :--- |:--- | :--- | :---
-learningRate | Double | The gradient descent step size at each iteration when moving toward a minimum of a loss function or optimal value. | 0.01
+learningRate | Double | The initial step size used in an iterative optimization algorithm. | 0.01
 momentumFactor | Double | The extra weight factors that accelerate the rate at which the weight is adjusted. This helps move the minimization routine out of local minima.  | 0
 epsilon | Double | The value for stabilizing gradient inversion. | 1.00E-06 
 beta1 | Double | The exponential decay rates for the moment estimates. |  0.9
@@ -413,7 +413,7 @@ A classification algorithm, logistic regression models the probability of a disc
 
 | Parameter | Type | Description | Default Value |
 |---|---|---|---|
-| learningRate | Double | The gradient descent step size at each iteration when moving toward a minimum of a loss function or optimal value  | 1 |
+| learningRate | Double | The initial step size used in an iterative optimization algorithm. | 1 |
 | momentumFactor | Double | The extra weight factors that accelerate the rate at which the weight is adjusted. This helps move the minimization routine out of local minima. | 0 |
 | epsilon | Double | The value for stabilizing gradient inversion. | 0.1 |
 | beta1 | Double | The exponential decay rates for the moment estimates. | 0.9 |