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!
An introduction to machine learning with no coding involved.
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.
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.
Learn how to clean and prepare your data for machine learning!
Understand the fundamentals of Machine Learning and how it's applied in the business world.
In this course you will learn the basics of machine learning for classification.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.
Learn to process, transform, and manipulate images at your will.
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.
Create new features to improve the performance of your Machine Learning models.
Gain experience using techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.
Learn about ARIMA models in Python and become an expert in time series analysis.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Learn to build recommendation engines in Python using machine learning techniques.
In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.
In this course you'll learn to use and present logistic regression models for making predictions.
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Learn how to detect fraud using Python.
Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Learn how to approach and win competitions on Kaggle.
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
Learn to build pipelines that stand the test of time.
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Learn how to build a model to automatically classify items in a school budget.
Learn to streamline your machine learning workflows with tidymodels.
Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.
Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.
Use tree-based machine learning methods to identify the characteristics of legendary Pokémon.
Process ingredient lists for cosmetics on Sephora then visualize similarity using t-SNE and Bokeh.
Predict the impact of climate change on bird distributions using spatial data and machine learning.
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.
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.
Use regression trees and random forests to find places where New York taxi drivers earn the most.
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.
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.
Use PySpark to build an e-commerce forecasting model!
Help the bank monitoring their fraud detection model and figuring out why it's not performing as expected.
Help a fast food chain save money and place more accurate orders by building a model to predict food sales.
Automate e-commerce processes with image classification.
Cluster NBA players by performance stats in the 2022-2023 season.
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!
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.