diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index a3288e1c5eef..bd062a887bd3 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -30,13 +30,28 @@ repos:
           - --ignore=E203,W503
           - --max-complexity=25
           - --max-line-length=88
-#     FIXME: fix mypy errors and then uncomment this
-#    - repo: https://github.com/pre-commit/mirrors-mypy
-#      rev: v0.782
-#      hooks:
-#        - id: mypy
-#          args:
-#            - --ignore-missing-imports
+# FIXME: fix mypy errors in other directories and add them here
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v0.812
+    hooks:
+      - id: mypy
+        args:
+          - --ignore-missing-imports
+          - arithmetic_analysis
+          - backtracking
+          # - bit_manipulation  # missing __init__.py
+          - blockchain
+          - boolean_algebra
+          - cellular_automata
+          # - computer_vision  # missing __init__.py
+          # - digital_image_processing  # missing __init__.py
+          - fuzzy_logic
+          - genetic_algorithm
+          - geodesy
+          # - knapsack  # missing __init__.py
+          - networking_flow
+          - scheduling
+          # - sorts  # missing __init__.py
   - repo: https://github.com/codespell-project/codespell
     rev: v1.17.1
     hooks:
diff --git a/DIRECTORY.md b/DIRECTORY.md
index dfb673dea829..136825e41976 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -472,6 +472,7 @@
   * [Runge Kutta](https://github.com/TheAlgorithms/Python/blob/master/maths/runge_kutta.py)
   * [Segmented Sieve](https://github.com/TheAlgorithms/Python/blob/master/maths/segmented_sieve.py)
   * Series
+    * [Arithmetic Mean](https://github.com/TheAlgorithms/Python/blob/master/maths/series/arithmetic_mean.py)
     * [Geometric Mean](https://github.com/TheAlgorithms/Python/blob/master/maths/series/geometric_mean.py)
     * [Geometric Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/geometric_series.py)
     * [Harmonic Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/harmonic_series.py)
diff --git a/machine_learning/k_nearest_neighbours.py b/machine_learning/k_nearest_neighbours.py
index e90ea09a58c1..2a90cfe5987a 100644
--- a/machine_learning/k_nearest_neighbours.py
+++ b/machine_learning/k_nearest_neighbours.py
@@ -32,7 +32,7 @@ def classifier(train_data, train_target, classes, point, k=5):
     :train_data: Set of points that are classified into two or more classes
     :train_target: List of classes in the order of train_data points
     :classes: Labels of the classes
-    :point: The data point that needs to be classifed
+    :point: The data point that needs to be classified
 
     >>> X_train = [[0, 0], [1, 0], [0, 1], [0.5, 0.5], [3, 3], [2, 3], [3, 2]]
     >>> y_train = [0, 0, 0, 0, 1, 1, 1]
diff --git a/maths/polynomial_evaluation.py b/maths/polynomial_evaluation.py
index e929a2d02972..68ff97ddd25d 100644
--- a/maths/polynomial_evaluation.py
+++ b/maths/polynomial_evaluation.py
@@ -5,7 +5,7 @@ def evaluate_poly(poly: Sequence[float], x: float) -> float:
     """Evaluate a polynomial f(x) at specified point x and return the value.
 
     Arguments:
-    poly -- the coeffiecients of a polynomial as an iterable in order of
+    poly -- the coefficients of a polynomial as an iterable in order of
             ascending degree
     x -- the point at which to evaluate the polynomial
 
@@ -26,7 +26,7 @@ def horner(poly: Sequence[float], x: float) -> float:
     https://en.wikipedia.org/wiki/Horner's_method
 
     Arguments:
-    poly -- the coeffiecients of a polynomial as an iterable in order of
+    poly -- the coefficients of a polynomial as an iterable in order of
             ascending degree
     x -- the point at which to evaluate the polynomial
 
diff --git a/searches/hill_climbing.py b/searches/hill_climbing.py
index 70622ebefb4e..bb24e781a6c1 100644
--- a/searches/hill_climbing.py
+++ b/searches/hill_climbing.py
@@ -60,7 +60,7 @@ def get_neighbors(self):
 
     def __hash__(self):
         """
-        hash the string represetation of the current search state.
+        hash the string representation of the current search state.
         """
         return hash(str(self))