English | Size: 558 MB
Genre: eLearning
Supervised Machine Learning in Python, Master the fundamentals of supervised machine learning and discover how to make predictions using labeled data. Join the ML revolution today! If you’re new to machine learning, or want to specialize in supervised machine learning, this is an ideal place to start. You’ll start by learning about and implementing core supervised learning models, such as K-Nearest Neighbors (KNN), Logistic Regression, Linear Regression, Support Vector Machines (SVMs), and tree-based models with the popular scikit-learn library. You’ll also discover how to use state-of-the-art algorithms like XGBoost to efficiently boost modelling performance on tabular datasets. To get the most out of your models, you’ll learn about different hyperparameter tuning techniques and how to decide which technique to use for your use case. You’ll finish the track by bringing your knowledge of these diverse models together to learn about ensemble learning, where different models are combined to improve performance and solve more complex problems. By the time you’re finished, you’ll have mastered the essential supervised machine learning concepts and be able to apply them in Python.
What you’ll learn
Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!
In this course you will learn the details of linear classifiers like logistic regression and SVM.
In this course, you’ll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
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