Advanced Machine Learning Track

Have you outgrown beginner courses for machine learning and data science? Tired of spending hours on the internet trying to discern good from bad?

We get you. We’ve been there. Today, you can learn almost everything from the internet. But it takes an enormous amount of time to find resources you can trust.

That’s why we created these courses. We spent the time doing the research, so that you don’t have to.

Through our advanced machine learning courses, you will grow your machine learning skills through engaging videos that delve into the theory and intuition behind various advanced techniques. Additionally, you'll have access to numerous Jupyter notebooks, offering efficient Python implementations to reinforce your understanding through practical application.

Stop spending hours on the internet. Start boosting your skills today!

Machine Learning Path

As you embarked on your machine learning journey, your initial focus likely lay in mastering basic algorithms like linear regression, decision trees, random forests, support vector machines, and maybe even a bit of deep learning, depending on which course you took. You probably learned about evaluation methods and tackled some projects at university, during courses, or on Kaggle.

As you progressed, perhaps after landing your first data science job, you noticed that machine learning extends far beyond learning algorithms. You likely spent hours on data analysis, data preprocessing, and feature engineering. Then, you worked on model optimization. And when everything was ready and you thought you could put your model into production, you found out that your stakeholders wanted to understand how the model made its predictions and if and how they can trust it.

Unfortunately, you don’t learn all these skills in beginner courses. Like us, you probably realized the hard way, that tackling real-world projects, creating models that will be used to serve and assess real people, requires way more than fitting a model and looking at some metric values.

Our Advanced Machine Learning Courses

To maximize the performance and value of your machine learning algorithms, there are several tasks you'll likely perform. For example, you’ll want to engineer and select variables effectively, optimize your models for better performance, interpret complex models with clarity, and address challenges posed by imbalanced data sets. Our machine learning courses provide concrete, actionable knowledge that's essential for succeeding in real-world applications of machine learning.

You'll learn how to clean and transform data effectively, engineer features to enhance model performance, optimize hyperparameters for better results, and interpret models to extract meaningful insights. This journey into advanced concepts propels your understanding and application of machine learning to new heights, empowering you to tackle more complex problems and unlock greater potential in the field.

Through our machine learning courses, you'll dive deep into the intricacies of advanced machine learning techniques. You'll learn how to engineer and select variables effectively to enhance model performance, optimize your models for better accuracy and efficiency, interpret complex models with clarity to extract actionable insights, and address the challenges posed by imbalanced data sets. Through practical, hands-on exercises and real-world examples, you'll gain the expertise needed to excel in today's rapidly evolving tech landscape.


English subtitles

English subtitles

Instructor support

Instructor support

Certificate of completion

Certificate of completion

A peek into our courses

In the "Feature Engineering for Machine Learning" course, you'll learn the fundamental techniques of feature engineering essential for building robust and efficient machine learning models. From understanding the importance of feature transformation to mastering methods for creating new features from existing data, this course equips you with the tools to transform raw data into meaningful inputs.

In the "Feature Selection for Machine Learning" course, you'll uncover the critical process of selecting the most relevant features to improve model accuracy and efficiency, while keeping them simple and interpretable. You’ll learn about methods embedded in the learning process of the algorithm, like tree importance and lasso regularization, and also how to use statistical tests and bespoke selection procedures.

In the "Hyperparameter Optimization for Machine Learning" course, you'll learn to fine-tune the hyperparameters that govern the behavior of your machine learning models. You'll explore advanced optimization algorithms aimed at maximizing performance of traditional models, like gradient boosting machines, and also neural networks. From grid search and random search to more sophisticated methods like Bayesian optimization through Gaussian processes, this course equips you with the skills to efficiently navigate the hyperparameter space and unleash the full potential of your models.

In the "Machine Learning Interpretability" course, you'll gain a deeper understanding of how machine learning models make decisions and learn to interpret their outputs with clarity and precision.

In the "Working with Imbalanced Data in Machine Learning" course, you'll tackle the common challenge of imbalanced datasets and learn effective strategies to address it. From understanding the underlying causes of data imbalance to implementing techniques such as resampling methods, cost-sensitive learning, and advanced ensemble techniques, this course provides you with the knowledge and skills to handle imbalanced data effectively.

Course 1: Interpreting Machine Learning models

Take a look at what you'll learn in this course:

Course 2: Feature Engineering for Machine Learning

Check out what you'll learn in this course:

Course materials

Each of our machine learning courses are packed with more than 10 hours of video tutorials, where we explain advanced machine learning techniques for feature engineering, feature selection, hyperparameter optimization, how to work with imbalanced data and how to interpret your machine learning models.

The courses also come with a ton of Python code, where we show you how to implement each one of these methods. These notebooks are a resource and reference that you can revisit and adapt to your own machine learning projects.

You will also find quizzes and assignments to practice and test the skills you just learned.

Who is this course for?

These courses are tailored to those of you who have already started in the field of machine learning and would like to further your skills. Maybe you are a data science or computer science graduate, or someone who transitioned to data science from a different background.

You’ve probably tackled your first data science projects, and realized that there are more skills you need, besides those learned in introduction to machine learning courses.

No matter what your background is, if you are currently working on real-world machine learning projects, our courses will help you increase your knowledge of typical machine learning workflows and allow you to tackle your work more effectively and faster.

Throughout the courses you will also discover various open-source Python libraries that will help you make your machine learning workflows smoother, faster and robust and interpretable.

Your instructor

Soledad Galli, data scientist and instructor

Soledad Galli, PhD


Sole is a lead data scientist, instructor and developer of open source software.

She's the maintainer of the open-source Python library Feature-engine, dedicated to transform, create and select features for machine learning.

Sole is also the author of the books:

  • Python Feature engineering Cookbook (Packt)
  • Feature Selection for Machine Learning with Python

Sole's passionate about sharing knowledge about machine learning, so you'll see her around at meetups, podcasts and webinars all over the web.

More about Sole on LinkedIn.

Course prerequisites

To make the most of our courses, you need basic knowledge of Python programming and machine learning. You need to be familiar with the most common learning algorithms like linear and logistic regression, random forests and gradient boosting machines.

You also need to understand about basic model evaluation metrics like R-squared, mean squared error, accuracy and ROC-AUC.

Finally, you need to be comfortable with Python programming with common numerical computing libraries such as NumPy, pandas, Matplotlib, and Scikit-learn. Everything else is on us.

Wrapping up

This comprehensive advanced machine learning specialization will teach you skills essential for navigating today’s tech landscape with confidence and expertise. You'll delve into critical areas such as variable engineering and selection, model optimization, model interpretation, and managing imbalanced data sets. The hands-on approach ensures you gain practical knowledge vital for real-world applications of machine learning.

The curriculum features real-world data sets with practical demonstrations, accompanied by hands-on Python code examples in Jupyter notebooks. These examples serve as valuable references, aids for practice, and resources for reuse in individual projects.

You can get lifetime access at a fixed price or enroll in a subscription-based plan, only paying for as long as you need to access the courses.

Depending on your enrollment method, you'll enjoy either a 30-day money-back guarantee or a 3-day free trial, ensuring zero risk when signing up for these advanced machine learning courses. Take the opportunity now to join the most comprehensive advanced machine learning program available.

Frequently Asked Questions

Do I pay once or monthly?

Up to you. You can pay once and get lifetime access, or you can subscribe an pay monthly, in which case you'll be able to access the course so long your subscription is active.

What if I don't like the course?

If you pay once to get lifetime access, there is a 30-day money back guarantee. If you don't find the course useful, contact us within the first 30 days of purchase and you will get a full refund.

If you decide to subscribe, there is a 3-day free trial, during which you can watch the content and decide if it is right for you. If not, just cancel the subscription from your user panel.

Will I get a certificate?

Yes, you'll get a certificate of completion after completing all lectures, quizzes and assignments from a course.

You'll get one completion certificate per course.

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