## Supervised Learning with 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!

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71 results ## Supervised Learning with scikit-learn

## Understanding Machine Learning

## Unsupervised Learning in Python

## Introduction to Deep Learning in Python

## Machine Learning with Tree-Based Models in Python

## Introduction to Natural Language Processing in Python

## Cluster Analysis in Python

## Supervised Learning in R: Classification

## Linear Classifiers in Python

## Extreme Gradient Boosting with XGBoost

## Machine Learning with scikit-learn

## Unsupervised Learning in R

## Machine Learning for Time Series Data in Python

## Introduction to Deep Learning with Keras

## Image Processing in Python

## Machine Learning for Business

## Introduction to TensorFlow in Python

## Deep Learning with PyTorch

## Preprocessing for Machine Learning in Python

## Model Validation in Python

## Machine Learning with caret in R

## Supervised Learning in R: Regression

## AI Fundamentals

## Dimensionality Reduction in Python

## Machine Learning with Tree-Based Models in R

## Feature Engineering for Machine Learning in Python

## Cluster Analysis in R

## Image Processing with Keras in Python

## Advanced Deep Learning with Keras

## ARIMA Models in Python

## Feature Engineering for NLP in Python

## Advanced NLP with spaCy

## Sentiment Analysis in Python

## MLOps Concepts

## Machine Learning with PySpark

## Hyperparameter Tuning in Python

## Building Chatbots in Python

## Customer Segmentation in Python

## Modeling with tidymodels in R

## Winning a Kaggle Competition in Python

## Supply Chain Analytics in Python

## Building Recommendation Engines in Python

## Introduction to Predictive Analytics in Python

## Text Mining with Bag-of-Words in R

## Sentiment Analysis in R

## Machine Learning in the Tidyverse

## Machine Learning for Finance in Python

## Ensemble Methods in Python

## Fraud Detection in Python

## Practicing Machine Learning Interview Questions in Python

## Market Basket Analysis in Python

## Machine Learning for Marketing in Python

## Data Privacy and Anonymization in Python

## Support Vector Machines in R

## Designing Machine Learning Workflows in Python

## Recurrent Neural Networks (RNN) for Language Modeling in Python

## Introduction to Natural Language Processing in R

## Fraud Detection in R

## Machine Learning for Marketing Analytics in R

## Building Recommendation Engines with PySpark

## Practicing Machine Learning Interview Questions in R

## Intermediate Predictive Analytics in Python

## HR Analytics: Predicting Employee Churn in Python

## Natural Language Generation in Python

## Hyperparameter Tuning in R

## Advanced Dimensionality Reduction in R

## Introduction to TensorFlow in R

## Machine Translation in Python

## Topic Modeling in R

## Predicting CTR with Machine Learning in Python

## Introduction to Feature Engineering in R

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!

4 hoursMachine LearningGeorge Boormancourses

An introduction to machine learning with no coding involved.

2 hoursMachine LearningHadrien Lacroixcourses

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

4 hoursMachine LearningBenjamin Wilsoncourses

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

4 hoursMachine LearningDan Beckercourses

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

5 hoursMachine LearningElie Kawerkcourses

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

4 hoursMachine LearningKatharine Jarmulcourses

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

4 hoursMachine LearningShaumik Daityaricourses

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

4 hoursMachine LearningBrett Lantzcourses

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

4 hoursMachine LearningMike Gelbartcourses

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

4 hoursMachine LearningSergey Fogelsoncourses

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

4 hoursMachine LearningHugo Bowne-Andersoncourses

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

4 hoursMachine LearningHank Roarkcourses

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

4 hoursMachine LearningChris Holdgrafcourses

Learn to start developing deep learning models with Keras.

4 hoursMachine LearningMiguel Estebancourses

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

4 hoursMachine LearningRebeca Gonzalezcourses

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

2 hoursMachine LearningKarolis Urbonascourses

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

4 hoursMachine LearningIsaiah Hullcourses

Learn to create deep learning models with the PyTorch library.

4 hoursMachine LearningIsmail Elezicourses

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

4 hoursMachine LearningDataCamp Content Creatorcourses

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

4 hoursMachine LearningKasey Jonescourses

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

4 hoursMachine LearningZachary Deane-Mayercourses

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

4 hoursMachine LearningJohn Mountcourses

Learn the fundamentals of AI. No programming experience required!

4 hoursMachine LearningNemanja Radojkovićcourses

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

4 hoursMachine LearningJeroen Boeyecourses

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

4 hoursMachine LearningSandro Raabecourses

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

4 hoursMachine LearningRobert O'Callaghancourses

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

4 hoursMachine LearningDmitriy Gorenshteyncourses

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

4 hoursMachine LearningAriel Rokemcourses

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

4 hoursMachine LearningZachary Deane-Mayercourses

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

4 hoursMachine LearningJames Fultoncourses

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

4 hoursMachine LearningRounak Banikcourses

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

5 hoursMachine LearningInes Montanicourses

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

4 hoursMachine LearningVioleta Mishevacourses

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

3 hoursMachine LearningFolkert Stijnmancourses

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

4 hoursMachine LearningAndrew Colliercourses

Learn to tune hyperparameters in Python.

4 hoursMachine LearningAlex Scrivencourses

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

4 hoursMachine LearningAlan Nicholcourses

Learn how to segment customers in Python.

4 hoursMachine LearningKarolis Urbonascourses

Learn to streamline your machine learning workflows with tidymodels.

4 hoursMachine LearningDavid Svancercourses

Learn how to approach and win competitions on Kaggle.

4 hoursMachine LearningYauhen Babakhincourses

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

4 hoursMachine LearningAaren Stubberfieldcourses

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

4 hoursMachine LearningRobert O'Callaghancourses

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

4 hoursMachine LearningNele Verbiestcourses

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

4 hoursMachine LearningTed Kwartlercourses

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

4 hoursMachine LearningTed Kwartlercourses

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

5 hoursMachine LearningDmitriy Gorenshteyncourses

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

4 hoursMachine LearningNathan Georgecourses

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

4 hoursMachine LearningRomán de las Herascourses

Learn how to detect fraud using Python.

4 hoursMachine LearningCharlotte Wergercourses

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

4 hoursMachine LearningLisa Stuartcourses

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

4 hoursMachine LearningIsaiah Hullcourses

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

4 hoursMachine LearningKarolis Urbonascourses

Learn to process sensitive information with privacy-preserving techniques.

4 hoursMachine LearningRebeca Gonzalezcourses

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

4 hoursMachine LearningKailash Awaticourses

Learn to build pipelines that stand the test of time.

4 hoursMachine LearningChristoforos Anagnostopouloscourses

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

4 hoursMachine LearningDavid Cecchinicourses

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

4 hoursMachine LearningKasey Jonescourses

Learn to detect fraud with analytics in R.

4 hoursMachine LearningBart Baesenscourses

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

4 hoursMachine LearningVerena Pfliegercourses

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

4 hoursMachine LearningJamen Longcourses

Prepare for your upcoming machine learning interview by working through these practice questions that span across important topics in machine learning.

4 hoursMachine LearningRafael Falconcourses

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

4 hoursMachine LearningNele Verbiestcourses

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

4 hoursMachine LearningHrant Davtyancourses

Imitate Shakespear, translate language and autocomplete sentences using Deep Learning in Python.

4 hoursMachine LearningBiswanath Haldercourses

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

4 hoursMachine LearningShirin Elsinghorst (formerly Glander)courses

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

4 hoursMachine LearningFederico Castanedocourses

Learn how to use TensorFlow, a state-of-the-art machine learning framework that specializes in the ability to develop deep learning neural networks.

4 hoursMachine LearningColleen Bobbiecourses

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

4 hoursMachine LearningThushan Ganegedaracourses

Learn how to fit topic models using the Latent Dirichlet Allocation algorithm.

4 hoursMachine LearningPavel Oleinikovcourses

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.

4 hoursMachine LearningKevin Huocourses

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

4 hoursMachine LearningJose Hernandezcourses