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.
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!
This course focuses on feature engineering and machine learning for time series data.
Learn how to make predictions with Apache Spark.
Understand the fundamentals of Machine Learning and how it's applied in the business world.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
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.
Build multiple-input and multiple-output deep learning models using Keras.
Learn to build pipelines that stand the test of time.
Sharpen your knowledge in machine learning, and prepare for any potential question you might get in a machine learning interview in Python.
Prepare for your upcoming machine learning interview by working through these practice questions that span across important topics in machine learning.
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.
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 to create deep learning models with the PyTorch library.
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.
Learn to streamline your machine learning workflows with tidymodels.
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 to build recommendation engines in Python using machine learning techniques.
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
Imitate Shakespear, translate language and autocomplete sentences using Deep Learning in Python.
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 powerful techniques for image analysis in Python using deep learning and convolutional neural networks in Keras.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Learn how to use TensorFlow, a state-of-the-art machine learning framework that specializes in the ability to develop deep learning neural networks.
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.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
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 build and tune predictive models and evaluate how well they'll perform on unseen data.
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.
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Learn to process, transform, and manipulate images at your will.
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
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.
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.