Core Machine Learning & Evaluation
Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.
Core Machine Learning & Evaluation
This course is part of AI Engineering Masterclass: From Zero to AI Hero Specialization
Included with
Ask Coursera
Recommended experience
Recommended experience
What you'll learn
Implement and evaluate machine learning algorithms such as regression, classification, and ensemble methods.
Understand and apply feature engineering and selection techniques to improve model performance.
Optimize models using hyperparameter tuning and regularization methods.
Use model evaluation techniques like cross-validation to assess and improve model accuracy.
Skills you'll gain
Tools you'll learn
Details to know
February 2026
6 assignments
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 4 modules in this course
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.
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.
What's included
8 videos2 readings1 assignment
8 videos•Total 150 minutes
- Introduction to Week 5 Introduction to Machine Learning•1 minute
- Day 1: Machine Learning Basics and Terminology•16 minutes
- Day 2: Introduction to Supervised Learning and Regression Models•16 minutes
- Day 3: Advanced Regression Models – Polynomial Regression and Regularization•35 minutes
- Day 4: Introduction to Classification and Logistic Regression•24 minutes
- Day 5: Model Evaluation and Cross-Validation•16 minutes
- Day 6: k-Nearest Neighbors (k-NN) Algorithm•17 minutes
- Day 7: Supervised Learning Mini Project•25 minutes
2 readings•Total 20 minutes
- Introduction to the Course 'Core Machine Learning & Evaluation'•10 minutes
- Full Specialization Resources•10 minutes
1 assignment•Total 15 minutes
- Week 5: Introduction to Machine Learning - Assessment•15 minutes
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.
What's included
8 videos1 assignment
8 videos•Total 119 minutes
- Introduction to Week 6 Feature Engineering and Model Evaluation•1 minute
- Day 1: Introduction to Feature Engineering•15 minutes
- Day 2: Data Scaling and Normalization•16 minutes
- Day 3: Encoding Categorical Variables•17 minutes
- Day 4: Feature Selection Techniques•16 minutes
- Day 5: Creating and Transforming Features•18 minutes
- Day 6: Model Evaluation Techniques•16 minutes
- Day 7: Cross-Validation and Hyperparameter Tuning•20 minutes
1 assignment•Total 15 minutes
- Week 6: Feature Engineering and Model Evaluation - Assessment•15 minutes
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.
What's included
8 videos1 assignment
8 videos•Total 126 minutes
- Introduction to Week 7 Advanced Machine Learning Algorithms•1 minute
- Day 1: Introduction to Ensemble Learning•15 minutes
- Day 2: Bagging and Random Forests•14 minutes
- Day 3: Boosting and Gradient Boosting•16 minutes
- Day 4: Introduction to XGBoost•20 minutes
- Day 5: LightGBM and CatBoost•20 minutes
- Day 6: Handling Imbalanced Data•17 minutes
- Day 7: Ensemble Learning Project – Comparing Models on a Real Dataset•23 minutes
1 assignment•Total 15 minutes
- Week 7: Advanced Machine Learning Algorithms - Assessment•15 minutes
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.
What's included
8 videos1 reading3 assignments
8 videos•Total 126 minutes
- Introduction to Week 8 Model Tuning and Optimization•1 minute
- Day 1: Introduction to Hyperparameter Tuning•14 minutes
- Day 2: Grid Search and Random Search•16 minutes
- Day 3: Advanced Hyperparameter Tuning with Bayesian Optimization•27 minutes
- Day 4: Regularization Techniques for Model Optimization•13 minutes
- Day 5: Cross-Validation and Model Evaluation Techniques•13 minutes
- Day 6: Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV•19 minutes
- Day 7: Optimization Project – Building and Tuning a Final Model•23 minutes
1 reading•Total 10 minutes
- Conclusion to the Course 'Core Machine Learning & Evaluation'•10 minutes
3 assignments•Total 90 minutes
- Week 8: Model Tuning and Optimization - Assessment•15 minutes
- Full Course Assessment•60 minutes
- Full Course Practice Assessment•15 minutes
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Instructor
Explore more from Machine Learning
- Status: Free Trial
Course
- Status: Free TrialC
Coursera
Course
- Status: Free Trial
- Status: Free Trial
Course
Why people choose Coursera for their career
Frequently asked questions
The "Core Machine Learning & Evaluation" course is a comprehensive introduction to key machine learning algorithms, evaluation techniques, and optimization strategies. It covers essential topics such as supervised learning, feature engineering, ensemble methods, and model tuning. This course is highly relevant for anyone wanting to delve deeper into machine learning, as these are the core techniques used in real-world AI applications across various industries, from healthcare to finance.
This course provides a solid foundation in machine learning concepts and techniques. In Week 5, you’ll learn about machine learning basics, regression, and classification models. Week 6 focuses on feature engineering and model evaluation, teaching you essential skills like data scaling, encoding categorical variables, and evaluating model performance. Week 7 dives into advanced machine learning algorithms such as ensemble learning and boosting methods like XGBoost and LightGBM. Finally, Week 8 covers model optimization, including hyperparameter tuning and regularization techniques.
Upon completing this course, you will have the skills to build, evaluate, and optimize machine learning models. You'll be able to apply supervised learning techniques like regression and classification, improve model performance using feature engineering, and use advanced algorithms like Random Forests and XGBoost. Additionally, you'll have hands-on experience with model tuning, cross-validation, and hyperparameter optimization, making you capable of developing high-performance machine learning models.
More questions
Financial aid available,