Introduction to Machine Learning Learn to train and assess models performing common machine learning tasks such as classification and clustering. 6 hours

Machine Learning Toolbox This course teaches the big ideas in machine learning like how to build and evaluate predictive models. 4 hours

Unsupervised Learning in R This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective. 4 hours Play preview

Supervised Learning with scikit-learn Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data. 4 hours Play preview

Introduction to Deep Learning in Python Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0. 4 hours Play preview

Sentiment Analysis in R Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelli... 4 hours

Unsupervised Learning in Python Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy. 4 hours Play preview

Supervised Learning in R: Classification In this course you will learn the basics of machine learning for classification. 4 hours Play preview

Machine Learning with Tree-Based Models in R In this course, you'll learn how to use tree-based models and ensembles for regression and classification. 4 hours Play preview

Sentiment Analysis in R: The Tidy Way In this course, you will the learn principles of sentiment analysis from a tidy data perspective. 4 hours Play preview

Introduction to Natural Language Processing in Python Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from ... 4 hours Play preview

Building Chatbots in Python Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning. 4 hours Play preview

Extreme Gradient Boosting with XGBoost Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve... 4 hours Play preview

Supervised Learning in R: Regression In this course you will learn how to predict future events using linear regression, generalized additive models, rand... 4 hours Play preview

Dimensionality Reduction in R Develop your intuition for when to reduce dimensionality in your data, and master the fundamentals of how to do so in R. 4 hours

Cluster Analysis in R Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract in... 4 hours Play preview

Supervised Learning in R: Case Studies Apply your supervised machine learning skills by working through four case studies using data from the real world. 4 hours Play preview

Machine Learning for Time Series Data in Python This course focuses on feature engineering and machine learning for time series data. 4 hours

Linear Classifiers in Python In this course you will learn the details of linear classifiers like logistic regression and SVM. 4 hours Play preview

HR Analytics in Python: Predicting Employee Churn In this course you'll learn how to apply machine learning in the HR domain. 4 hours

Machine Learning with Tree-Based Models in Python In this course, you'll learn how to use tree-based models and ensembles for regression and classification using sciki... 5 hours Play preview

Foundations of Predictive Analytics in Python (Part 1) In this course you'll learn to use and present logistic regression models for making predictions. 4 hours

Advanced Deep Learning with Keras Build multiple-input and multiple-output deep learning models using Keras. 4 hours Play preview

Preprocessing for Machine Learning in Python In this course you'll learn how to get your cleaned data ready for modeling. 4 hours

Machine Learning for Finance in Python Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks. 4 hours

Hyperparameter Tuning in R Learn how to tune your model's hyperparameters to get the best predictive results. 4 hours

Support Vector Machines in R This course will introduce the support vector machine (SVM) using an intuitive, visual approach. 4 hours

Building Recommendation Engines with PySpark Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users. 4 hours

Machine Learning in the Tidyverse Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models. 5 hours Play preview

Convolutional Neural Networks for Image Processing Convolutional neural networks are deep learning algorithms that are particularly powerful for analysis of images. 4 hours Play preview

Advanced NLP with spaCy Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine le... 5 hours

Supply Chain Analytics in Python Leverage the power of Python and PuLP to optimize supply chains. 4 hours

Advanced Dimensionality Reduction in R Learn how to apply advanced dimensionality techniques such as t-SNE and GLRM. 4 hours

Topic Modeling in R Learn how to fit topic models using the Latent Dirichlet Allocation algorithm. 4 hours

Feature Engineering in R Learn a variety of feature engineering techniques to develop meaningful features that will uncover useful insights ab... 4 hours

Foundations of Predictive Analytics in Python (Part 2) Learn how to prepare and organize your data for predictive analytics. 4 hours

Designing Machine Learning Workflows in Python Learn to build pipelines that stand the test of time. 4 hours

Feature Engineering for Machine Learning in Python Create new features to improve the performance of your Machine Learning models. 4 hours Play preview

Clustering Methods with SciPy In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means c... 4 hours

Introduction to TensorFlow in Python Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow. 4 hours

Model Validation in Python Learn the basics of model validation, validation techniques, and begin creating validated and high performing models. 4 hours

Ensemble Methods in Python Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging... 4 hours

Introduction to Deep Learning with PyTorch Learn to create deep learning models with the PyTorch library. 4 hours

Introduction to Deep Learning with Keras Learn to start developing deep learning models with Keras. 4 hours

Feature Engineering for NLP in Python Learn techniques to extract useful information from text and process them into a format suitable for machine learning. 4 hours

Sentiment Analysis in Python Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment an... 4 hours

Forecasting Using ARIMA Models in Python Learn about ARIMA models in Python and become an expert in time series analysis. 4 hours

Recurrent Neural Networks for Language Modeling in P... Use RNNs to classify text sentiment, generate sentences, and translate text between languages. 4 hours

Machine Translation in Python Are you curious about the inner workings of the models that are behind products like Google Translate? 4 hours

Preparing for Machine Learning Interview Questions in R Prepare for your upcoming machine learning interview by working through these practice questions that span across imp... 4 hours

Introduction to TensorFlow in R Learn how to use TensorFlow, a state-of-the-art machine learning framework that specializes in the ability to develop... 4 hours

Introduction to Natural Language Processing in R Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R. 4 hours

Machine Learning for Marketing in Python From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for M... 4 hours

Preparing for Machine Learning Interview Questions i... Sharpen your knowledge in machine learning, and prepare for any potential question you might get in a machine learnin... 4 hours