
In this lecture we explain how to use google colab for programming in Python.
In this lecture we make a brief introduction to Machine Learning.
On this lesson we introduce the breast cancer dataset.
Later we will use Linear Classification to predict if the patient has a malignant or benign tumor.
On this lesson we partition our dataset into training and test.
On this lesson we preprocess and prepare our data to apply visualization techniques and classification models.
On this lesson we visualize the separation between the classes malignant and benign.
We see on the graph that the classes form two clusters. This means patients that belong to the same class will have similar values of the features. Furthermore, patients that belong to different classes will have different values of the features.
On this lesson we implement our first classifier: Linear Discriminant Analysis.
We study the results in the classification report. Moreover, we plot the Confusion Matrix and the Roc Curve.
On this lesson we implement the Naive Bayes classifier.
We study the results in the classification report. Moreover, we plot the Confusion Matrix and the Roc Curve.
On this lesson we implement the Quadratic Discriminant classifier.
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As if this course wasn’t complete enough, I offer full support, answering any questions you have.
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See you on the inside (hurry, Classification is waiting!)