Introduction to Python
Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Python Data Science Toolbox (Part 1)
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Python Data Science Toolbox (Part 2)
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Statistical Thinking in Python (Part 1)
Build the foundation you need to think statistically and to speak the language of your data.
Machine Learning with scikit-learn
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
Unsupervised Learning in Python
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Linear Classifiers in Python
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Machine Learning with Tree-Based Models in Python
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Extreme Gradient Boosting with XGBoost
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Cluster Analysis in Python
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Dimensionality Reduction in Python
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Preprocessing for Machine Learning in Python
Learn how to clean and prepare your data for machine learning!
Machine Learning for Time Series Data in Python
This course focuses on feature engineering and machine learning for time series data.
Feature Engineering for Machine Learning in Python
Create new features to improve the performance of your Machine Learning models.
Model Validation in Python
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Introduction to Natural Language Processing in Python
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Feature Engineering for NLP in Python
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Introduction to TensorFlow in Python
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Introduction to Deep Learning in Python
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.
Introduction to Deep Learning with Keras
Learn to start developing deep learning models with Keras.
Advanced Deep Learning with Keras
Build multiple-input and multiple-output deep learning models using Keras.
Image Modeling with Keras
Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.
Hyperparameter Tuning in Python
Gain experience using techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
Introduction to PySpark
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Machine Learning with PySpark
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.