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.
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'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
In this course you'll learn how to get your cleaned data ready for modeling.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
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 how to build and tune predictive models and evaluate how well they'll perform on unseen data.
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.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Create new features to improve the performance of your Machine Learning models.
Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Learn about ARIMA models in Python and become an expert in time series analysis.
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Learn how to approach and win competitions on Kaggle.
Learn to build pipelines that stand the test of time.
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
Learn how to detect fraud using Python.
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
Learn to tune hyperparameters in 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.
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
In this course you'll learn to use and present logistic regression models for making predictions.
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.
Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Learn how to build a model to automatically classify items in a school budget.
Learn how to prepare and organize your data for predictive analytics.
In this course you'll learn how to apply machine learning in the HR domain.
Learn to process sensitive information with privacy-preserving techniques.
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end 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.
Are you curious about the inner workings of the models that are behind products like Google Translate?
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 machine learning model to predict if a credit card application will get approved.
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.
Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.
Use data manipulation, cleaning, and feature engineering skills to prepare a payment dataset for fraud prediction modeling.