This week’s module introduces you to the concept of Maximum Likelihood Estimation (MLE) and its application in statistical modeling. Through this material, you will gain a thorough understanding of how to mathematically implement MLE and apply it to real-world datasets. First, we will revisit foundational concepts of convex optimization, offering a solid foundation in optimization techniques. We will also explore the iterative process of the gradient descent algorithm, allowing you to understand and implement this method for finding optimal solutions in machine learning models. Through a combination of theoretical knowledge and practical application, you will build essential skills in statistical estimation and optimization, preparing for advanced studies in machine learning and data analysis.

In this module, you will gain a comprehensive understanding of supervised machine learning, from model training to evaluation. Specifically, you will interpret each step in the learning process and apply training and evaluation techniques to real-world data. This will enable you to fit and assess models, while addressing issues like overfitting and underfitting. By understanding the bias-variance trade-off, you can optimize models for greater accuracy and reliability. We will also cover cross-validation methods, further equipping you with robust tools for model assessment and performance analysis. In short, this week’s learning combines theoretical insights with hands-on programming, preparing you for advanced work in machine learning.

This week, we will focus on the foundational principles of linear regression, a key technique in predictive modeling. You will learn to apply linear regression models and derive the ordinary least squares (OLS) formulation, gaining insight into how OLS is used to fit data accurately. We will also cover solution methods, including gradient descent and convex optimization, which provide a toolkit for efficient model training. Finally, you will explore regularization techniques to enhance model robustness and prevent overfitting. By implementing these regularized regression models in Python, you will gain hands-on experience in model optimization.

This week, we will dive into advanced techniques for linear regression, with a focus on regularization. You will have the opportunity to explore the concepts of Lasso and Ridge regression and learn how to formulate and apply these regularization methods to linear models. The module also covers polynomial regression, allowing you to fit more complex nonlinear relationships within data. Through hands-on exercises, you will implement Lasso, Ridge, and polynomial regression models in Python. By the end of this week, you will have the practical knowledge needed to apply regularized regression techniques effectively, making the models more resilient and adaptable in real-world scenarios.

This week’s module offers a comprehensive introduction to logistic regression, a fundamental technique in classification tasks. You will learn to apply logistic regression to binary and multi-class classification problems, starting with the derivation of the maximum likelihood formulation specific to logistic models. We will also explore generalized linear models (GLMs) and their application in classification, broadening your understanding of model flexibility across various scenarios. Practical exercises focus on implementing logistic regression in Python, enabling you to gain hands-on experience with real-world data. By the end of this module, you will be well-prepared to tackle classification challenges with logistic regression and GLMs, applying statistical theory alongside programming skills.

This week, we introduce Support Vector Machines (SVMs) as a powerful tool for discriminative classification. You will start by understanding the mathematical formulation of SVMs, focusing on margin optimization to maximize model separation between classes. We then delve into various kernel functions—linear, polynomial, and Gaussian—highlighting their unique applications and effects on classification. You will also learn techniques for hyperparameter tuning to optimize SVM performance, adapting models for complex datasets. You will also gain hands-on experience in building and refining SVM models to effectively use SVMs for a wide range of classification tasks in machine learning.