Machine Learning Scientist in Python

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!

Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means Clustering

In this course you will learn the details of linear classifiers like logistic regression and SVM.

In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.

Build a regression model for a DVD rental firm to predict rental duration. Evaluate models to recommend the best one.

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

Learn how to clean and prepare your data for machine learning!

This course focuses on feature engineering and machine learning for time series data.

Create new features to improve the performance of your Machine Learning models.

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 fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.

Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

Learn how to build your first neural network, adjust hyperparameters, and tackle classification and regression problems in PyTorch.

Learn about fundamental deep learning architectures such as CNNs, RNNs, LSTMs, and GRUs for modeling image and sequential data.

Learn to process, transform, and manipulate images at your will.

Learn to implement distributed data management and machine learning in Spark using the PySpark package.

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.