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. This course dives deep into applied machine learning and model optimization, covering everything from foundational concepts to advanced algorithms. You'll gain hands-on experience working with different types of machine learning models, evaluating their performance, and fine-tuning them for optimal results. The course emphasizes practical, real-world applications, with interactive projects and mini-projects to ensure you can implement what you learn. Throughout the course, you'll explore core machine learning algorithms such as regression, classification, ensemble methods, and advanced techniques like XGBoost and LightGBM. You'll also focus on model optimization, including hyperparameter tuning, cross-validation, and regularization techniques. These skills will allow you to enhance the performance of your models, even in complex scenarios. This course is designed for learners who already have a basic understanding of machine learning and wish to build more advanced skills in model building and optimization. It is ideal for those looking to pursue careers in data science, machine learning engineering, or AI development. By the end of the course, you will be able to implement various machine learning algorithms, optimize model performance using hyperparameter tuning, and evaluate models effectively for real-world tasks.

Syllabus

Introduction to Machine Learning

In this module, we will lay the groundwork for your machine learning journey. You’ll learn essential concepts, including supervised learning and regression models, and dive into advanced techniques like polynomial regression and regularization. By the end of the module, you’ll gain hands-on experience building a supervised learning model on a real-world dataset.

Feature Engineering and Model Evaluation

In this module, we will focus on improving your machine learning models through feature engineering and model evaluation. You’ll learn how to scale, normalize, and encode data, create new features, and select the best ones. The module also covers crucial model evaluation techniques to ensure your models are robust and performant.

Advanced Machine Learning Algorithms

In this module, we will take your machine learning models to the next level by exploring advanced algorithms. You will dive into ensemble learning methods, including bagging, boosting, and algorithms like XGBoost and CatBoost. By the end of this module, you’ll be able to handle imbalanced data and apply ensemble learning to improve model performance.

Model Tuning and Optimization

In this module, we will delve into the crucial aspects of model tuning and optimization. You will learn how to fine-tune hyperparameters, apply regularization techniques, and explore advanced optimization methods like Bayesian optimization. The module also includes automation tools like GridSearchCV to speed up the hyperparameter tuning process, ensuring better model performance.

Intermediate Projects

In this section, we will guide you through a variety of intermediate-level projects that will enhance your programming abilities. You’ll work on real-world tools like weather dashboards, expense trackers, and interactive games. This hands-on approach will help you solidify your skills while creating practical applications for daily use.

Advanced Intermediate Projects

In this module, we will focus on advanced intermediate projects that challenge your skills further. You’ll work on building dynamic applications such as a movie recommendation system, stock market dashboard, and portfolio website backend. These projects will also deepen your understanding of web scraping, task automation, and data visualization.

Machine Learning Algorithms and Implementation in Python

In this module, you will explore and implement a wide variety of machine learning algorithms in Python. From supervised learning techniques like linear regression and SVM to unsupervised algorithms like K-Means and DBSCAN, you will gain hands-on experience with each method. The module also covers advanced deep learning algorithms such as CNNs, RNNs, and Transformers for tackling complex tasks like image classification and natural language processing.