diff --git a/lessons/2-Symbolic/README.md b/lessons/2-Symbolic/README.md
index 0a8ed834..6437b4e4 100644
--- a/lessons/2-Symbolic/README.md
+++ b/lessons/2-Symbolic/README.md
@@ -58,7 +58,7 @@ Untyped-Language | doesn't have | type definitions
 
 > ✅ Think how triplets can be used to represent other types of knowledge.
 
-2. **Hierarchical representations** emphasize the fact that we often create a hierarchy of objects inside our head. For example, we know that canary is a bird, and all birds have wings. We also have some idea about what colour canary usually is, and what is their flight speed.
+2. **Hierarchical representations** emphasize the fact that we often create a hierarchy of objects inside our head. For example, we know that canary is a bird, and all birds have wings. We also have some idea about what colour a canary usually is, and what is their flight speed.
 
    - **Frame representation** is based on representing each object or class of objects as a **frame** which contains **slots**. Slots have possible default values, value restrictions, or stored procedures that can be called to obtain the value of a slot. All frames form a hierarchy similar to an object hierarchy in object-oriented programming languages.
    - **Scenarios** are special kind of frames that represent complex situations that can unfold in time.
diff --git a/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/lessons/3-NeuralNetworks/04-OwnFramework/README.md
index 5afddc6a..f649500b 100644
--- a/lessons/3-NeuralNetworks/04-OwnFramework/README.md
+++ b/lessons/3-NeuralNetworks/04-OwnFramework/README.md
@@ -29,7 +29,7 @@ In the definition of *f* above, *w* and *b* are called **parameters** θ=
 
 ## Gradient Descent Optimization
 
-There is a well-known method of function optimization called **gradient descent**. The idea is that we can compute a derivative (in multi-dimensional case call **gradient**) of loss function with respect to parameters, and vary parameters in such a way that the error would decrease. This can be formalized as follows:
+There is a well-known method of function optimization called **gradient descent**. The idea is that we can compute a derivative (in multi-dimensional case called **gradient**) of loss function with respect to parameters, and vary parameters in such a way that the error would decrease. This can be formalized as follows:
 
 * Initialize parameters by some random values w<sup>(0)</sup>, b<sup>(0)</sup>
 * Repeat the following step many times: