Fix typos (#839)

Author: omahs

CONTRIBUTING.md

@@ -39,7 +39,7 @@ $ make notebooks/02_numerical_pipeline_scaling.ipynb
 - when saving the notebook inside Jupyter it will actually write to the `.py` file
 
 In our experience, this workflow is less convenient (Visual Studio Code is a
-nicer developping environment) and also it tends to add some not very important
+nicer developing environment) and also it tends to add some not very important
 (and different on everyone's machine) metadata changes in the `.py` file, for
 example about jupytext version, Jupyter kernel, Python version, etc ...
 
@@ -101,7 +101,7 @@ make full-index
 ## JupyterBook
 
 JupyterBook is the tool we use to generate our .github.io website from our
-`.py` and `.md` files (note than `.ipynb` files are not used in our JupyterBook
+`.py` and `.md` files (note that `.ipynb` files are not used in our JupyterBook
 setup).
 
 ```

notebooks/01_tabular_data_exploration.ipynb

@@ -102,7 +102,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "An alternative is to omit the `head` method. This would output the intial and\n",
+    "An alternative is to omit the `head` method. This would output the initial and\n",
     "final rows and columns, but everything in between is not shown by default. It\n",
     "also provides the dataframe's dimensions at the bottom in the format `n_rows`\n",
     "x `n_columns`."

notebooks/linear_models_sol_03.ipynb

@@ -98,7 +98,7 @@
     "Since we scaled the features, the coefficients of the linear model can be\n",
     "meaningful compared directly. `\"capital-gain\"` is the most impacting feature.\n",
     "Just be aware not to draw conclusions on the causal effect provided the impact\n",
-    "of a feature. Interested readers are refered to the [example on Common\n",
+    "of a feature. Interested readers are referred to the [example on Common\n",
     "pitfalls in the interpretation of coefficients of linear\n",
     "models](https://scikit-learn.org/stable/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html)\n",
     "or the [example on Failure of Machine Learning to infer causal\n",

notebooks/logistic_regression.ipynb

@@ -390,7 +390,7 @@
     "function](https://en.wikipedia.org/wiki/Softmax_function) to make predictions.\n",
     "Giving more details on that scenario is beyond the scope of this MOOC.\n",
     "\n",
-    "In any case, interested users are refered to the [scikit-learn user guide](\n",
+    "In any case, interested users are referred to the [scikit-learn user guide](\n",
     "https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression)\n",
     "for a more mathematical description of the `predict_proba` method of the\n",
     "`LogisticRegression` and the respective normalization functions."

notebooks/parameter_tuning_grid_search.ipynb

@@ -451,7 +451,7 @@
     "In this notebook we have seen:\n",
     "\n",
     "* how to optimize the hyperparameters of a predictive model via a grid-search;\n",
-    "* that searching for more than two hyperparamters is too costly;\n",
+    "* that searching for more than two hyperparameters is too costly;\n",
     "* that a grid-search does not necessarily find an optimal solution."
    ]
   }

notebooks/trees_classification.ipynb

@@ -9,7 +9,7 @@
     "In this notebook we illustrate decision trees in a multiclass classification\n",
     "problem by using the penguins dataset with 2 features and 3 classes.\n",
     "\n",
-    "For the sake of simplicity, we focus the discussion on the hyperparamter\n",
+    "For the sake of simplicity, we focus the discussion on the hyperparameter\n",
     "`max_depth`, which controls the maximal depth of the decision tree."
    ]
   },

plan.md

@@ -54,7 +54,7 @@ Features and samples
 A few words about the style and scope of this course: it is centered
 around code, though we strive to keep it simple
 
-## Quizz:
+## Quiz:
 Given a case study (e.g. pricing apartments based on a real estate website database) and sample toy dataset: say whether it’s an application of supervised vs unsupervised, classification vs regression, what are the features, what is the target variable, what is a record.
 
 Propose a hand engineer decision rule that can be used as a baseline
@@ -92,7 +92,7 @@ Simple exploratory data analysis with pandas and matplotlib
 
 ### Content
 
-Prepare a train  / test split
+Prepare a train / test split
 
 Basic model on numerical features only
 
@@ -113,7 +113,7 @@ Model fitting and performance evaluation with cross-validation
 ## Notebook module #3: basic parameter tuning and final test score evaluation
 
 ### Learning objectives:
-- Learn to no trust blindly the default parameters of scikit-learn estimators
+- Learn not to trust blindly the default parameters of scikit-learn estimators
 
 ### Content
 Parameter tuning with Grid and Random hyperparameter search

python_scripts/01_tabular_data_exploration.py

@@ -71,7 +71,7 @@
 adult_census.head()
 
 # %% [markdown]
-# An alternative is to omit the `head` method. This would output the intial and
+# An alternative is to omit the `head` method. This would output the initial and
 # final rows and columns, but everything in between is not shown by default. It
 # also provides the dataframe's dimensions at the bottom in the format `n_rows`
 # x `n_columns`.

python_scripts/linear_models_sol_03.py

@@ -66,7 +66,7 @@
 # Since we scaled the features, the coefficients of the linear model can be
 # meaningful compared directly. `"capital-gain"` is the most impacting feature.
 # Just be aware not to draw conclusions on the causal effect provided the impact
-# of a feature. Interested readers are refered to the [example on Common
+# of a feature. Interested readers are referred to the [example on Common
 # pitfalls in the interpretation of coefficients of linear
 # models](https://scikit-learn.org/stable/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html)
 # or the [example on Failure of Machine Learning to infer causal

python_scripts/logistic_regression.py

@@ -266,7 +266,7 @@
 # function](https://en.wikipedia.org/wiki/Softmax_function) to make predictions.
 # Giving more details on that scenario is beyond the scope of this MOOC.
 #
-# In any case, interested users are refered to the [scikit-learn user guide](
+# In any case, interested users are referred to the [scikit-learn user guide](
 # https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression)
 # for a more mathematical description of the `predict_proba` method of the
 # `LogisticRegression` and the respective normalization functions.