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.

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

Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.

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

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

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

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

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

Learn to create deep learning models with the PyTorch library.

Learn the fundamentals of AI. No programming experience required!

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

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

In this course you'll learn how to get your cleaned data ready for modeling.

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

Learn to start developing deep learning models with Keras.

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

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

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

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

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

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

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

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

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

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

Build multiple-input and multiple-output deep learning models using Keras.

Learn to tune hyperparameters in Python.

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.

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

Learn to streamline your machine learning workflows with tidymodels.

Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.

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

Leverage the power of Python and PuLP to optimize supply chains.

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

Learn how to detect fraud using Python.

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

Learn how to approach and win competitions on Kaggle.

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

Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.

Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Learn how to segment customers in Python.

In this course you'll learn to use and present logistic regression models for making predictions.

Learn to build recommendation engines in Python using machine learning techniques.

Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.

Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.

Learn to build pipelines that stand the test of time.

This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.

Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.

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

Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.

Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.

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

Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.

Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.

Learn how to tune your model's hyperparameters to get the best predictive results.

Learn the bag of words technique for text mining with R.

Learn the power of deep learning in PyTorch. Build your first neural network, adjust hyperparameters, and tackle classification and regression problems.

In this course you'll learn how to use data science for several common marketing tasks.

Learn a variety of feature engineering techniques to develop meaningful features that will uncover useful insights about your machine learning models.

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.

Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.

Learn to process sensitive information with privacy-preserving techniques.

In this course you'll learn how to apply machine learning in the HR domain.

Are you curious about the inner workings of the models that are behind products like Google Translate?

Learn how to apply advanced dimensionality techniques such as t-SNE and GLRM.

Learn to detect fraud with analytics in R.

Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.

Learn how to prepare and organize your data for predictive analytics.