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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.

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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!

4 hours

George Boorman Headshot

George Boorman

Curriculum Manager, DataCamp

2
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.

2 hours

3
Unsupervised Learning in Python

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

4 hours

Benjamin Wilson Headshot

Benjamin Wilson

Director of Research at lateral.io

4
Clustering Antarctic Penguin Species

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

2 hours

Amir Parizi Headshot

Amir Parizi

Data Scientist at Staples US Retail

5
Linear Classifiers in Python

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

4 hours

Mike Gelbart Headshot

Mike Gelbart

Instructor, the University of British Columbia

6
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.

5 hours

Elie Kawerk Headshot

Elie Kawerk

Senior Data Scientist

7
Predicting Movie Rental Durations

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

2 hours

Rohan Chatterjee Headshot

Rohan Chatterjee

Risk Modeler

8
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.

4 hours

Sergey Fogelson Headshot

Sergey Fogelson

Head of Data Science, TelevisaUnivision

9
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.

4 hours

Shaumik Daityari Headshot

Shaumik Daityari

Business Analyst at American Express

10
Dimensionality Reduction in Python

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

4 hours

Jeroen Boeye Headshot

Jeroen Boeye

Machine Learning Engineer @ Faktion

11
Preprocessing for Machine Learning in Python

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

4 hours

James Chapman Headshot

James Chapman

Curriculum Manager, DataCamp

12
Machine Learning for Time Series Data in Python

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

4 hours

Chris Holdgraf Headshot

Chris Holdgraf

Fellow at the Berkeley Institute for Data Science

13
Feature Engineering for Machine Learning in Python

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

4 hours

Robert O'Callaghan Headshot

Robert O'Callaghan

Director of Data Science, Ordergroove

14
Model Validation in Python

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

4 hours

Kasey Jones Headshot

Kasey Jones

Research Data Scientist

15
Hyperparameter Tuning in Python

Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

4 hours

Alex Scriven Headshot

Alex Scriven

Senior Data Scientist @ Atlassian

16
Machine Learning Fundamentals in Python
17
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.

4 hours

Katharine Jarmul Headshot

Katharine Jarmul

Founder, kjamistan

18
Natural Language Processing with spaCy

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

4 hours

Azadeh Mobasher Headshot

Azadeh Mobasher

Principal Data Scientist

19
Feature Engineering for NLP in Python

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

4 hours

Rounak Banik Headshot

Rounak Banik

Data Scientist at Fractal Analytics

20
Introduction to Deep Learning with PyTorch

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

4 hours

Maham Khan Headshot

Maham Khan

Senior Data Scientist, YouView TV

21
Intermediate Deep Learning with PyTorch

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

4 hours

Michał Oleszak Headshot

Michał Oleszak

Machine Learning Engineer

22
Image Processing in Python

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

4 hours

Rebeca Gonzalez Headshot

Rebeca Gonzalez

Data Scientist, Hiberus Tecnologia

23
Introduction to PySpark

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

4 hours

Lore Dirick Headshot

Lore Dirick

Director of Data Science Education at Flatiron School

24
Machine Learning with PySpark

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

4 hours

Andrew Collier Headshot

Andrew Collier

Data Scientist @ Exegetic Analytics

25
Winning a Kaggle Competition in Python

Learn how to approach and win competitions on Kaggle.

4 hours

Yauhen Babakhin Headshot

Yauhen Babakhin

Kaggle Grandmaster

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Instructors

  • George Boorman Headshot
    George BoormanCurriculum Manager, DataCampSee George Boorman's Portfolio
  • Mohammed Abufouda Headshot
    Mohammed AbufoudaSee Mohammed Abufouda's Portfolio
  • Benjamin Wilson Headshot
    Benjamin WilsonDirector of Research at lateral.ioSee Benjamin Wilson's Portfolio
  • Amir Parizi Headshot
    Amir PariziData Scientist at Staples US RetailSee Amir Parizi's Portfolio

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