Supervised Machine Learning from First Principles

Enrolled: 82 students
Lectures: 76
Level: Beginner

Greetings students! I’m thrilled to present our course on the exhilarating world of Supervised Machine Learning. This course offers a unique opportunity to gain a comprehensive understanding of modern statistical learning techniques for modeling, prediction and inference.

We will start from first principles – understanding basic concepts like overfitting, bias vs variance tradeoffs, model selection methods. We will build intuition on why and when to apply supervision to our models. We will then systematically progress towards regression, classification, validation techniques, regularization, dimension reduction and tree-based methods.

The course strikes the perfect balance between theory and practical application. You will thoroughly learn linear regression, logistic regression, PCA, regularized models, tree pruning through extensive coding assignments. We want a seamless blend of core ideas and their implementation.

This is an applied course focusing on model accuracy, precision-recall tradeoffs, ethics. We want to make you experts in supervised techniques with both depth and breadth. I will highlight modern advances and limitations to provide perspective. By the end, you will have an integrated grasp of the what, why and how behind supervision.

I can’t wait for our pioneering journey into the frontier of Supervised Machine Learning. Buckle up for an intellectual joyride! Do reach out if you have any questions. Now let’s get started…

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