In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Understand the fundamentals of Machine Learning and how it's applied in the business world.
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.
This course focuses on feature engineering and machine learning for time series data.
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 how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.
Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
In this course you'll learn how to use data science for several common marketing tasks.
An introduction to machine learning with no coding involved.
Build multiple-input and multiple-output deep learning models using Keras.
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
Learn to build pipelines that stand the test of time.
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
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 get your cleaned data ready for modeling.
Learn to start developing deep learning models with Keras.
Learn the power of deep learning in PyTorch. Build your first neural network, adjust hyperparameters, and tackle classification and regression problems.
Create new features to improve the performance of your Machine Learning models.
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 build a model to automatically classify items in a school budget.
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!
Learn to streamline your machine learning workflows with tidymodels.
Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
Learn to build recommendation engines in Python using machine learning techniques.
In this course you'll learn how to apply machine learning in the HR domain.
In this course you will learn the basics of machine learning for classification.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Learn powerful command-line skills to download, process, and transform data, including machine learning pipeline.
Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.
In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.
Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
Learn a variety of feature engineering techniques to develop meaningful features that will uncover useful insights about your machine learning models.
Learn how to use ChatGPT. Discover best practices for writing prompts and explore common business use cases for the powerful AI tool.
Learn the fundamentals of AI. No programming experience required!
Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.
Build a machine learning model to predict if a credit card application will get approved.
Build a machine learning classifier that knows whether President Trump or Prime Minister Trudeau is tweeting!
Use tree-based machine learning methods to identify the characteristics of legendary Pokémon.
Rock or rap? Apply machine learning methods in Python to classify songs into genres.
Discover the top tools Kaggle participants use for data science and machine learning.
Predict the impact of climate change on bird distributions using spatial data and machine learning.
Build a binary classifier to predict if a blood donor is likely to donate again.
Use regression trees and random forests to find places where New York taxi drivers earn the most.
Use data manipulation, cleaning, and feature engineering skills to prepare a payment dataset for fraud prediction modeling.
Process ingredient lists for cosmetics on Sephora then visualize similarity using t-SNE and Bokeh.
Use NLP and clustering on movie plot summaries from IMDb and Wikipedia to quantify movie similarity.
Use cluster analysis to glean insights into cryptocurrency gambling behavior.
Apply unsupervised learning techniques to help plan an education program in Argentina.
Explore the salary potential of college majors with a k-means cluster analysis.
Build a deep learning model that can automatically detect honey bees and bumble bees in images.
Experiment with clustering algorithms to help doctors inform treatment for heart disease patients.
Build a convolutional neural network to classify images of letters from American Sign Language.
How can we find a good strategy for reducing traffic-related deaths?
How can we find a good strategy for reducing traffic-related deaths?
Build a model that can automatically detect honey bees and bumble bees in images.
Load, transform, and understand images of honey bees and bumble bees in Python.