Includeing additional typos corrections highlight in Issues

Author: liamjberrisford

individual_modules/introduction_to_machine_learning/2_model_selection.ipynb

@@ -8,10 +8,10 @@
     "\n",
     "## Learning objectives\n",
     "\n",
-    "* Select different machine learning models in `scikit-learn`, and train them \n",
+    "* Select different machine learning models in `scikit-learn` and train them \n",
     "* Understand that models have different characteristics, including flexibility, parameterisation, and more, which affects model selection and efficacy\n",
     "* Explain the model flexibility/over-fit trade off \n",
-    "* Explain what model train and test error are, and how these relate to overfitting "
+    "* Explain what model train and test errors are, and how these relate to overfitting "
    ]
   },
   {
@@ -782,7 +782,7 @@
     "### Summary\n",
     "\n",
     "* We have used a linear model to classify points into two classes.\n",
-    "* We achieves a 95% accuracy score on the test set: this means that given a new (representative) point, we should have a 95% chance of this point being accurately classified."
+    "* We achieved a 95% accuracy score on the test set: this means that given a new (representative) point, we should have a 95% chance of this point being accurately classified."
    ]
   },
   {
@@ -1321,7 +1321,7 @@
    "metadata": {},
    "source": [
     "* Lets plot the misclassified points, but also the decision boundary\n",
-    "* For binary logistic regression, this is the line where the probability of belonging to a class is 0.5"
+    "* For a binary logistic regression, this is the line where the probability of belonging to a class is 0.5"
    ]
   },
   {
@@ -1380,10 +1380,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Changing model\n",
+    "## Changing the model\n",
     "\n",
     "* We can potentially get improved performance if we select a new model\n",
-    "* Lets use an SVM (support Vector Machine) with a radial basel function kernel.\n",
+    "* Lets use an SVM (Support Vector Machine) with a radial basis function kernel.\n",
     "* This can model non-linear decision boundaries."
    ]
   },
@@ -1888,7 +1888,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cfrr",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1902,9 +1902,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

individual_modules/introduction_to_machine_learning/3_pipeline_task.ipynb

@@ -47,7 +47,7 @@
     "\n",
     "* What kind of machine learning problem/task is this?\n",
     "* What are our goals? What are we predicting?\n",
-    "* If this was a research task, does what were doing make sense?\n",
+    "* If this was a research task, does what we're doing make sense?\n",
     "* How would a human answer the question?\n",
     "* What performance would we be happy with?\n",
     "* Do we have any assumptions yet? Can we verify these?\n",
@@ -5530,7 +5530,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cfrr",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -5544,9 +5544,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }