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!
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 how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
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.
Learn how to clean and prepare your data for machine learning!
In this course you will learn the details of linear classifiers like logistic regression and SVM.
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
This course focuses on feature engineering and machine learning for time series data.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Learn to process, transform, and manipulate images at your will.
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.
Create new features to improve the performance of your Machine Learning models.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Gain experience using techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
Learn about ARIMA models in Python and become an expert in time series analysis.
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.
Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
In this course you'll learn to use and present logistic regression models for making predictions.
Learn how to detect fraud using Python.
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Learn to build recommendation engines in Python using machine learning techniques.
Learn to build pipelines that stand the test of time.
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Learn how to approach and win competitions on Kaggle.
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.
Learn how to build a model to automatically classify items in a school budget.
This course covers everything you need to know to build a basic machine learning monitoring system in Python
In this course you'll learn how to apply machine learning in the HR domain.
Learn to process sensitive information with privacy-preserving techniques.
Learn how to prepare and organize your data for predictive analytics.
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.
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.
Build a binary classifier to predict if a blood donor is likely to donate again.
Build a deep learning model that can automatically detect honey bees and bumble bees in images.
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?
Rock or rap? Apply machine learning methods in Python to classify songs into genres.
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.
Join us at a leading insurance company, where we'll craft a model to predict customer charges and test it with new client data
Leverage machine learning algorithms and models for marketing analytics tasks in a streaming platform.
Automate e-commerce processes with image classification.
Solve the Taxi-v3 environment using Q-learning, ensuring efficient AI-driven transportation.
Help the bank monitoring their fraud detection model and figuring out why it's not performing as expected.
Build models predicting customer churn for Indian telecom customers.
Build a machine learning model to predict if a credit card application will get approved.
Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means Clustering
Build a regression model for a DVD rental firm to predict rental duration. Evaluate models to recommend the best one.
Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.
Perform a machine learning experiment to find the best model that predicts the temperature in London!
Clean customer data and use logistic regression to predict whether people will make a claim on their car insurance!
Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.