Merge pull request #254 from Foo-x/clean-1_4-readme

style: clean 1.4 README

1-Introduction/4-techniques-of-ML/README.md

@@ -4,8 +4,9 @@ The process of building, using, and maintaining machine learning models and the
 
 - Understand the processes underpinning machine learning at a high level.
 - Explore base concepts such as 'models', 'predictions', and 'training data'.
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 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
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 ## Introduction
 
 On a high level, the craft of creating machine learning (ML) processes is comprised of a number of steps:
@@ -44,9 +45,11 @@ To be able to answer your question with any kind of certainty, you need a good a
 A [feature](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) is a measurable property of your data. In many datasets it is expressed as a column heading like 'date' 'size' or 'color'. Your feature variable, usually represented as `y` in code, represents the answer to the question you are trying to ask of your data: in December, what **color** pumpkins will be cheapest? in San Francisco, what neighborhoods will have the best real estate **price**?
 
 🎓 **Feature Selection and Feature Extraction** How do you know which variable to choose when building a model? You'll probably go through a process of feature selection or feature extraction to choose the right variables for the most performant model. They're not the same thing, however: "Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features." ([source](https://wikipedia.org/wiki/Feature_selection))
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 ### Visualize your data
 
-An important aspect of the data scientist's toolkit is the power to  visualize data using several excellent libraries such as Seaborn or MatPlotLib. Representing your data visually might allow you to uncover hidden correlations that you can leverage. Your visualizations might also help you to uncover bias or unbalanced data (as we discover in [Classification](../../4-Classification/2-Classifiers-1/README.md)).
+An important aspect of the data scientist's toolkit is the power to visualize data using several excellent libraries such as Seaborn or MatPlotLib. Representing your data visually might allow you to uncover hidden correlations that you can leverage. Your visualizations might also help you to uncover bias or unbalanced data (as we discover in [Classification](../../4-Classification/2-Classifiers-1/README.md)).
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 ### Split your dataset
 
 Prior to training, you need to split your dataset into two or more parts of unequal size that still represent the data well.
@@ -61,10 +64,12 @@ Using your training data, your goal is to build a model, or a statistical repres
 
 ### Decide on a training method
 
-Depending on your question and the nature of your data, your will choose a method to train it. Stepping through [Scikit-learn's documentation](https://scikit-learn.org/stable/user_guide.html) - which we use in this course - you can explore many ways to train a model. Depending on your experience, you might have to try several different methods to build the best model. You are likely to go through a process whereby data scientists evaluate the performance of a model by feeding it unseen data, checking for accuracy, bias, and other quality-degrading issues, and selecting the most appropriate training method for the task at hand.
+Depending on your question and the nature of your data, you will choose a method to train it. Stepping through [Scikit-learn's documentation](https://scikit-learn.org/stable/user_guide.html) - which we use in this course - you can explore many ways to train a model. Depending on your experience, you might have to try several different methods to build the best model. You are likely to go through a process whereby data scientists evaluate the performance of a model by feeding it unseen data, checking for accuracy, bias, and other quality-degrading issues, and selecting the most appropriate training method for the task at hand.
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 ### Train a model
 
 Armed with your training data, you are ready to 'fit' it to create a model. You will notice that in many ML libraries you will find the code 'model.fit' - it is at this time that you send in your data as an array of values (usually 'X') and a feature variable (usually 'y').
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 ### Evaluate the model
 
 Once the training process is complete (it can take many iterations, or 'epochs', to train a large model), you will be able to evaluate the model's quality by using test data to gauge its performance. This data is a subset of the original data that the model has not previously analyzed. You can print out a table of metrics about your model's quality.