Core Machine Learning & Evaluation

Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will build a strong foundation in machine learning and model evaluation techniques. You will begin by learning the core concepts of machine learning, including supervised learning, regression models, and classification techniques. The course will then guide you through more advanced topics like feature engineering, model evaluation methods, and hyperparameter tuning, which are essential for building high-performing machine learning models. By working through hands-on projects, you'll apply these concepts and tools in real-world scenarios. Throughout the course, you will explore key machine learning algorithms such as decision trees, random forests, boosting, and ensemble learning methods. You'll also learn how to evaluate and optimize models using techniques like cross-validation and hyperparameter tuning. These skills will enable you to refine your models and improve their accuracy, ensuring that they are ready for real-world applications. This course is suitable for anyone looking to deepen their understanding of machine learning, model evaluation, and optimization. While there are no strict prerequisites, a basic understanding of Python programming and machine learning concepts is recommended. The course is designed for intermediate learners, and the content will provide valuable skills for anyone looking to pursue a career in data science or machine learning engineering. By the end of the course, you will be able to implement and optimize machine learning models using various algorithms, perform feature engineering and selection, evaluate models using cross-validation, and apply advanced techniques such as boosting and ensemble methods.

Syllabus

Week 5: Introduction to Machine Learning

In this module, we will introduce you to the fundamental concepts of machine learning, focusing on supervised learning techniques like regression and classification. You will learn model evaluation strategies and apply your knowledge in a supervised learning mini project to solidify your skills.

Week 6: Feature Engineering and Model Evaluation

In this module, we will dive into the art of feature engineering, focusing on techniques like scaling, encoding, and feature selection to improve model performance. You will also explore various model evaluation methods and apply them to fine-tune your machine learning models for optimal outcomes.

Week 7: Advanced Machine Learning Algorithms

In this module, we will explore advanced machine learning algorithms such as ensemble learning, Random Forests, and boosting methods like XGBoost and LightGBM. You will also tackle common challenges like imbalanced datasets and apply your learning in a hands-on project comparing various models.

Week 8: Model Tuning and Optimization

In this module, we will focus on model optimization techniques, including hyperparameter tuning, regularization, and cross-validation. You will learn advanced tuning methods such as Bayesian optimization and apply these techniques in a project to build and fine-tune your final machine learning model.