Fix some typos in chapter 2 (DataTalksClub#103)

* Correct typo Change Nagivation to Navigation * Correct typo Change Nagivation to Navigation * Correct typos Changed Nagivation to Navigation * Correct typo Changed Nagivation to Navigation * Correct typo Changed Nagivation to Navigation Added linear combination in first point under linear combination heading, after first para * Correct typo Changed Nagivation to Navigation * Correct typo Changed Nagivation to Navigation Changed 't' to 'to' * Correct typo Changed Nagivation to Navigation * Correct typo Changed Nagivation to Navigation Removed redundant 'we' in first point under heading Questions * Correct typo Changed Nagivation to Navigation * Correct typo Changed Nagivation to Navigation * Correct typo Changed Nagivation to Navigation * Correct typo Changed Nagivation to Navigation Changed showed to shown * Correct typo Changed Nagivation to Navigation Changed df.rest_index to df.reset_index * Correct typo Changed Nagivation to Navigation * Correct typo Changed Nagivation to Navigation Changed retrive to retrieve in second last line under heading pandas attributes and methods * Correct typo Changed Nagivation to Navigation
levankankadze · Jul 31, 2022 · 3a50ad8 · 3a50ad8
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diff --git a/course-zoomcamp/02-regression/01-car-price-intro.md b/course-zoomcamp/02-regression/01-car-price-intro.md
@@ -34,7 +34,7 @@ The code and dataset are available at this [link](https://github.com/alexeygrigo
 </table>
 
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/02-data-preparation.md b/course-zoomcamp/02-regression/02-data-preparation.md
@@ -16,7 +16,7 @@
 * df.columns.str.lower() - lowercase all the letters 
 * df.columns.str.replace(' ', '_') - replace the space separator 
 * df.dtypes - retrieve data types of all features 
-* df.index - retrive indices of a dataframe
+* df.index - retrieve indices of a dataframe
 
 The entire code of this project is available in [this jupyter notebook](https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/chapter-02-car-price/02-carprice.ipynb).
 
@@ -30,7 +30,7 @@ The entire code of this project is available in [this jupyter notebook](https://
    </tr>
 </table>
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/03-eda.md b/course-zoomcamp/02-regression/03-eda.md
@@ -36,7 +36,7 @@ The entire code of this project is available in [this jupyter notebook](https://
    </tr>
 </table>
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/04-validation-framework.md b/course-zoomcamp/02-regression/04-validation-framework.md
@@ -13,7 +13,7 @@ In general, the dataset is split into three parts: training, validation, and tes
 **Pandas attributes and methods:** 
 
 * `df.iloc[]` - returns subsets of records of a dataframe, being selected by numerical indices
-* `df.rest_index()` - restate the orginal indices 
+* `df.reset_index()` - restate the orginal indices 
 * `del df[col]` - eliminates target variable 
 
 **Numpy methods:**
@@ -34,7 +34,7 @@ The entire code of this project is available in [this jupyter notebook](https://
    </tr>
 </table>
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/05-linear-regression-simple.md b/course-zoomcamp/02-regression/05-linear-regression-simple.md
@@ -11,7 +11,7 @@
 Model for solving regression tasks, in which the objective is to adjust a line for the data and make predictions on new values. The input of this model is 
 the feature matrix and a y vector of predictions is obtained, trying to be as close as possible to the actual y values. The LR formula is the sum of the 
 bias term (WO), which refers to the predictions if there is no information, and each of the feature values times their corresponding weights. We need to 
-assure that the result is showed on the untransformed scale. 
+assure that the result is shown on the untransformed scale. 
 
 The entire code of this project is available in [this jupyter notebook](https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/chapter-02-car-price/02-carprice.ipynb).  
 
@@ -25,7 +25,7 @@ The entire code of this project is available in [this jupyter notebook](https://
    </tr>
 </table>
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/06-linear-regression-vector.md b/course-zoomcamp/02-regression/06-linear-regression-vector.md
@@ -24,7 +24,7 @@ The entire code of this project is available in [this jupyter notebook](https://
    </tr>
 </table>
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/07-linear-regression-training.md b/course-zoomcamp/02-regression/07-linear-regression-training.md
@@ -27,7 +27,7 @@ The entire code of this project is available in [this jupyter notebook](https://
    </tr>
 </table>
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/08-baseline-model.md b/course-zoomcamp/02-regression/08-baseline-model.md
@@ -24,7 +24,7 @@ The entire code of this project is available in [this jupyter notebook](https://
    </tr>
 </table>
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/09-rmse.md b/course-zoomcamp/02-regression/09-rmse.md
@@ -22,7 +22,7 @@ The entire code of this project is available in [this jupyter notebook](https://
    </tr>
 </table>
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/10-car-price-validation.md b/course-zoomcamp/02-regression/10-car-price-validation.md
@@ -23,7 +23,7 @@ The entire code of this project is available in [this jupyter notebook](https://
    </tr>
 </table>
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/11-feature-engineering.md b/course-zoomcamp/02-regression/11-feature-engineering.md
@@ -24,7 +24,7 @@ The entire code of this project is available in [this jupyter notebook](https://
    </tr>
 </table>
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/12-categorical-variables.md b/course-zoomcamp/02-regression/12-categorical-variables.md
@@ -30,7 +30,7 @@ This way of encoding categorical features is called "one-hot encoding".
 We'll learn more about it in Session 3. 
 
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/13-regularization.md b/course-zoomcamp/02-regression/13-regularization.md
@@ -35,13 +35,13 @@ The entire code of this project is available in [this jupyter notebook](https://
 I mentioned the term *linear combination* in the video, but didn't explain what it means. 
 So if you're interested what it means, you can read here
 
-* One column is a  of others when you can express one column of a matrix as a sum of others columns
+* One column is a linear combination of others when you can express one column of a matrix as a sum of others columns
 * The simplest example is when a column is an exact duplicate of another column
 * Another example. Let's say we have 3 columns: `a`, `b`, `c`. If `c = 0.2 * a + 0.5 * b`, then `c` is a linear combination of `a` and `b`
 * More formal definition: https://en.wikipedia.org/wiki/Linear_combination
 
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/14-tuning-model.md b/course-zoomcamp/02-regression/14-tuning-model.md
@@ -23,7 +23,7 @@ The entire code of this project is available in [this jupyter notebook](https://
    </tr>
 </table>
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/15-using-model.md b/course-zoomcamp/02-regression/15-using-model.md
@@ -8,7 +8,7 @@
 ## Notes
 
 After finding the best model and its parameters, it was trained with training and validation partitions and the final evaluation was calculated on the test partition. 
-Finally, the final model was used t predict the price of new cars. 
+Finally, the final model was used to predict the price of new cars. 
 
 The entire code of this project is available in [this jupyter notebook](https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/chapter-02-car-price/02-carprice.ipynb).  
 
@@ -22,7 +22,7 @@ The entire code of this project is available in [this jupyter notebook](https://
    </tr>
 </table>
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/16-summary.md b/course-zoomcamp/02-regression/16-summary.md
@@ -21,7 +21,7 @@ were explained using the problem to predict the price of cars.
    </tr>
 </table>
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)

diff --git a/course-zoomcamp/02-regression/17-explore-more.md b/course-zoomcamp/02-regression/17-explore-more.md
@@ -3,7 +3,7 @@
 
 ### Questions
 
-* In this project, we We included only 5 top features. What happens if we include 10?
+* In this project, we included only 5 top features. What happens if we include 10?
 
 > That's not a graded homework, it's just for you if you want to try more things on this project
 
@@ -17,7 +17,7 @@ Here are other datasets that you can play with to learn more about the topic:
 * UCI ML Repository contains a lot of other datasets suitable for practicing regression - https://archive.ics.uci.edu/ml/datasets.php?task=reg
 
 
-## Nagivation
+## Navigation
 
 * [Machine Learning Zoomcamp course](../)
 * [Session 2: Machine Learning for Regression](./)