Applied Machine Learning and Model Optimization
Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.
Applied Machine Learning and Model Optimization
This course is part of AI & Python Development Megaclass Specialization
Included with
Ask Coursera
Recommended experience
Recommended experience
What you'll learn
Implement various supervised and unsupervised machine learning algorithms in Python.
Apply ensemble learning techniques like Random Forests, XGBoost, and LightGBM to improve model performance.
Master model optimization techniques such as hyperparameter tuning, cross-validation, and regularization.
Evaluate machine learning models using advanced metrics and real-world validation techniques.
Skills you'll gain
- Predictive Modeling
- Fine-tuning
- Model Optimization
- Machine Learning Methods
- Unsupervised Learning
- Model Evaluation
- Data Transformation
- Data Preprocessing
- Machine Learning Algorithms
- Model Training
- Feature Engineering
- Supervised Learning
- Statistical Machine Learning
- Applied Machine Learning
- Artificial Intelligence and Machine Learning (AI/ML)
- Dimensionality Reduction
Tools you'll learn
Details to know
February 2026
9 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 7 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. 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.
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.
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 'Applied Machine Learning and Model Optimization'•10 minutes
- Full Specialization Resources•10 minutes
1 assignment•Total 15 minutes
- Introduction to Machine Learning - Assessment•15 minutes
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.
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
- Feature Engineering and Model Evaluation - Assessment•15 minutes
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.
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
- Advanced Machine Learning Algorithms - Assessment•15 minutes
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.
What's included
8 videos1 assignment
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 assignment•Total 15 minutes
- Model Tuning and Optimization - Assessment•15 minutes
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.
What's included
11 videos1 assignment
11 videos•Total 206 minutes
- Day 50: Weather Dashboard App•29 minutes
- Day 51: Expense Tracker•18 minutes
- Day 52: File Organizer Tool•17 minutes
- Day 53: Tic-Tac-Toe Game•25 minutes
- Day 54: Mini Chatbot•11 minutes
- Day 55: Music Playlist Organizer•20 minutes
- Day 56: Personal Budget Planner•14 minutes
- Day 57: ASCII Art Generator•15 minutes
- Day 58: Pomodoro Timer•22 minutes
- Day 59: Markdown to HTML Converter•17 minutes
- Day 60: Personal Diary App•18 minutes
1 assignment•Total 15 minutes
- Intermediate Projects - Assessment•15 minutes
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.
What's included
10 videos1 assignment
10 videos•Total 143 minutes
- Day 61: Social Media Scraper•13 minutes
- Day 62: Automated Backup Tool•13 minutes
- Day 63: Movie Recommendation System•16 minutes
- Day 64: PDF Merger Tool•11 minutes
- Day 65: Portfolio Website Backend•21 minutes
- Day 66: Flashcards Learning App•10 minutes
- Day 67: Stock Market Dashboard•16 minutes
- Day 68: Task Scheduler•15 minutes
- Day 69: Currency Converter•11 minutes
- Day 70: Data Visualizer App•18 minutes
1 assignment•Total 15 minutes
- Advanced Intermediate Projects - Assessment•15 minutes
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.
What's included
28 videos1 reading3 assignments
28 videos•Total 189 minutes
- Introduction to Machine Learning Algorithms and Implementation in Python•4 minutes
- 1. Supervised Learning Algorithms: Linear Regression Implementation•6 minutes
- 2. Supervised Learning Algorithms: Ridge and Lasso Regression Implementation•8 minutes
- 3. Supervised Learning Algorithms: Polynomial Regression Implementation•7 minutes
- 4. Supervised Learning Algorithms: Logistic Regression Implementation•6 minutes
- 5. Supervised Learning Algorithms: K-Nearest Neighbors (KNN) Implementation•6 minutes
- 6. Supervised Learning Algorithms: Support Vector Machines (SVM) Implementation•6 minutes
- 7. Supervised Learning Algorithms: Decision Trees Implementation•6 minutes
- 8. Supervised Learning Algorithms: Random Forests Implementation•6 minutes
- 9. Supervised Learning Algorithms: Gradient Boosting Implementation•6 minutes
- 10. Supervised Learning Algorithms: Naive Bayes Implementation•6 minutes
- 11. Unsupervised Learning Algorithms: K-Means Clustering Implementation•4 minutes
- 12. Unsupervised Learning Algorithms: Hierarchical Clustering Implementation•5 minutes
- 13. Unsupervised Learning Algorithms: DBSCAN•5 minutes
- 14. Unsupervised Learning Algorithms: Gaussian Mixture Models (GMM)•5 minutes
- 15. Unsupervised Learning Algorithms: Principal Component Analysis (PCA)•5 minutes
- 16. Unsupervised Learning Algorithms: t-Distributed Stochastic Neighbor Embedding•5 minutes
- 17. Unsupervised Learning Algorithms: Autoencoders Implementation•8 minutes
- 18. Self-Training Implementation•7 minutes
- 19. Q-Learning Implementation•9 minutes
- 20. Deep Q-Networks (DQN) Implementation•14 minutes
- 21. Policy Gradient Methods Implementation•10 minutes
- 22. One-Class SVM Implementation•5 minutes
- 23. Isolation Forest Implementation•5 minutes
- 24. Convolutional Neural Networks (CNNs) Implementation•8 minutes
- 25. Recurrent Neural Networks (RNNs) Implementation•8 minutes
- 26. Long Short-Term Memory (LSTM) Implementation•7 minutes
- 27. Transformers Implementation•11 minutes
1 reading•Total 10 minutes
- Conclusion to the Course 'Applied Machine Learning and Model Optimization'•10 minutes
3 assignments•Total 90 minutes
- Machine Learning Algorithms and Implementation in Python - 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
- Status: Free Trial
Course
- Status: Free Trial
Course
- Status: Free TrialA
Alberta Machine Intelligence Institute
Course
Why people choose Coursera for their career
Frequently asked questions
This course covers advanced concepts in machine learning, focusing on model optimization and fine-tuning techniques to improve the performance of machine learning models. Machine learning is a key technology behind many AI applications, and knowing how to apply and optimize algorithms for real-world datasets is crucial for anyone pursuing a career in AI or data science.
The course explores various machine learning algorithms, model evaluation techniques, feature engineering, and methods for optimizing models. You will learn how to implement supervised and unsupervised learning algorithms, ensemble methods like Random Forests and XGBoost, and advanced techniques for model tuning such as hyperparameter optimization. The course also provides hands-on projects where you apply these skills to real-world data.
Upon completing this course, you will be able to apply machine learning algorithms such as linear regression, logistic regression, k-NN, and decision trees, and optimize their performance using techniques like cross-validation, grid search, and ensemble learning. You will also gain the skills needed to work with complex datasets, perform feature engineering, and handle model tuning and optimization to achieve better results in your machine learning projects.
More questions
Financial aid available,