JetBrains AcademyBy the end of this course, you will be able to:
Understand key machine learning concepts;
Use NumPy and Pandas to prepare and manipulate data for machine learning tasks;
Build and evaluate machine learning models using scikit-learn;
Apply data preprocessing and feature engineering techniques, including scaling, encoding, and text vectorization;
Develop and assess regression and classification models using appropriate performance metrics;
Implement decision trees, ensemble methods, and clustering algorithms for supervised and unsupervised learning;
Apply dimensionality reduction and optimization techniques to improve model performance.
After completing the course, you'll have a solid practical foundation in machine learning with Python and be ready to progress toward advanced machine learning or data science topics.
Master advanced engineering concepts through ambitious projects. Each project deepens your expertise and transforms you from an experienced engineer into an exceptional one.
Data scientists often train and optimize different machine learning models and then choose the one with the best performance. But did you know that combining these models can make them even better? This project will teach you how to train and optimize multiple models and then combine them to get better results. You will discover how ensemble learning can outperform using a single model by working on a multi-class classification task about music and emotions.
Graduate
Linear regression is one of the simplest yet powerful tools for finding regularities in data and using them for prediction. It is widely applied both in science and practice. In this project, you will learn how to apply scikit-learn library to fit linear models, use them for prediction, compare the models, and select the best one. You will also learn how to carry out testing for certain issues with data.
Graduate
In this project, you are going to explore the main classification algorithms and learn how to find and train the best possible model for the classification of handwritten digits. You will need to process a dataset that includes images of handwritten numbers from 0 to 9. The ultimate goal is to train the model to identify a digit on the picture. Sounds interesting? Let's dive into it!
Graduate
