Data science courses

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!

An introduction to machine learning with no coding involved.

In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.

Learn how to clean and prepare your data for machine learning!

In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.

Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.

In this course you will learn the basics of machine learning for classification.

Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

In this course you will learn the details of linear classifiers like logistic regression and SVM.

Understand the fundamentals of Machine Learning and how it's applied in the business world.

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

Learn to process, transform, and manipulate images at your will.

This course focuses on feature engineering and machine learning for time series data.

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

Create new features to improve the performance of your Machine Learning models.

Learn about ARIMA models in Python and become an expert in time series analysis.

Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.

Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.

Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.

Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.