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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.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!
An introduction to machine learning with no coding involved.
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
In this course, youll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.
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!
Understand the fundamentals of Machine Learning and how its applied in the business world.
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.
In this course you will learn the basics of machine learning for classification.
Learn to process, transform, and manipulate images at your will.
Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.
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.
This course focuses on feature engineering and machine learning for time series data.
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.
Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Learn about ARIMA models in Python and become an expert in time series analysis.
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.