Who is this course for?

This course is ideal for individuals with a foundational understanding of machine learning who want to deepen their knowledge of advanced algorithms and optimization techniques. It is perfect for data scientists, machine learning engineers, and anyone seeking to improve their skills in developing and optimizing machine learning models.

How long does it take to complete the course?

The course includes approximately 19 hours of video content, which can be completed at your own pace. Depending on your learning speed and available time, you can expect to spend around 3-4 weeks if you dedicate a few hours per week to complete the course.

Can I preview a course before enrolling?

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

When will I have access to the lectures and assignments?

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

What will I get when I enroll?

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Applied Machine Learning and Model Optimization

Applied Machine Learning and Model Optimization

Instructor: Packt - Course Instructors

Included with

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7 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

7 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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.

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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

Instructor

Packt - Course Instructors

Packt

1,471 Courses 392,127 learners

Offered by

Packt

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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.

This course assumes a basic understanding of programming and some familiarity with Python, as it involves coding in Python for implementing machine learning algorithms. Some knowledge of fundamental machine learning concepts would be beneficial, though beginners can still benefit from the course. A basic understanding of statistics and mathematics, especially linear algebra, is also helpful for grasping the mathematical concepts behind machine learning models.