Skip to main content
HomePython

track

Machine Learning Scientist in Python

Discover machine learning with Python and work towards becoming a machine learning scientist. Explore supervised, unsupervised, and deep learning.
Start Track for Free

Included withPremium or Teams

PythonMachine Learning85 hours27,900

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
Group

Training 2 or more people?

Try DataCamp for Business

Loved by learners at thousands of companies

Track Description

Machine Learning Scientist in Python

Master the essential Python skills to land a job as a machine learning scientist! With this track, you'll gain a comprehensive introduction to machine learning in Python. You’ll augment your existing Python programming skill set with the tools needed to perform supervised, unsupervised, and deep learning. You'll learn how to process data for features, train your models, assess performance, and tune parameters for better performance. This track also covers topics including tree-based machine learning models, cluster analysis, preprocessing for machine learning, and more. By the time you finish, you’ll have the confidence to use Python for machine learning, working with real data sets, linear classifiers, gradient boosting, and more. In the process, you'll get an introduction to natural language processing, image processing, and popular Python machine learning packages such as scikit-learn, Spark, and Keras.

Prerequisites

There are no prerequisites for this track
  • Course

    1

    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!

  • Project

    bonus

    Predictive Modeling for Agriculture

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

  • Course

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

  • Course

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

  • Course

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

  • Course

    10

    Dimensionality Reduction in Python

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

  • Course

    Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.

  • Course

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

  • Course

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

  • Course

    Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

Machine Learning Scientist in Python
21 courses
Track
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review

Included withPremium or Teams

Enroll Now

FAQs

Join over 15 million learners and start Machine Learning Scientist in Python today!

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.