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Associate AI Engineer for Data Scientists

Begin building AI solutions and gain the career-building skills you need to succeed as an AI Engineer, from model development to deployment into production! In this track, you’ll train and evaluate robust predictive models on real-world datasets across a variety of domains. Starting with the fundamentals of machine learning, you’ll get hands-on with some of the most popular Python libraries for machine learning and deep learning, including scikit-learn, PyTorch, and many more. As you progress, you’ll get hands-on with Large Language Models (LLMs) for a variety of natural language tasks. You'll learn to fine-tune Llama 3 on custom data and integrate this into a LangChain application to begin surfacing predictions to end-users. Finally, you'll discover what it takes to move an AI model from a notebook into production. You'll build foundational skills in MLOps, including testing, version control, and monitoring performance in production.

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1
Python
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
Python
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

3
Python
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

4
Python
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

5
Python
Developing Multi-Input Models For OCR

Develop a multi-input model to classify characters from scanned documents.

2 hours

6
Theory
Responsible AI Data Management

Learn the theory behind responsibly managing your data for any AI project, from start to finish and beyond.

4 hours

Maria Prokofieva Headshot

Maria Prokofieva

Lead ML Engineer

7
Python
Introduction to LLMs in Python

Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!

4 hours

Iván P.C. Headshot

Iván P.C.

Senior Data Science & AI Manager

8
Python
Analyzing Car Reviews with LLMs

Use LLMs to solve diverse language tasks for a car dealership company.

2 hours

9
Python
Working with Llama 3

Explore the latest techniques for running the Llama LLM locally, fine-tuning it, and integrating it within your stack.

4 hours

Imtihan Ahmed Headshot

Imtihan Ahmed

Machine Learning Engineer

10
Theory
MLOps Concepts

Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

2 hours

Folkert Stijnman Headshot

Folkert Stijnman

ML Engineer

11
Theory
MLOps Deployment and Life Cycling

In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.

4 hours

Nemanja Radojković Headshot

Nemanja Radojković

Senior Data Scientist @ Euroclear

12
Git
Introduction to Git

Familiarize yourself with Git for version control. Explore how to track, compare, modify, and revert files, as well as collaborate with colleagues using Git.

4 hours

George Boorman Headshot

George Boorman

Curriculum Manager, DataCamp

13
Python
Software Engineering Principles in Python

Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.

4 hours

Adam Spannbauer Headshot

Adam Spannbauer

Machine Learning Engineer at Eastman

14
Python
Introduction to Testing in Python

Master Python testing: Learn methods, create checks, and ensure error-free code with pytest and unittest.

4 hours

Alexander Levin Headshot

Alexander Levin

Senior Data Scientist

15
Theory
Monitoring Machine Learning Concepts

Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

2 hours

Hakim Elakhrass Headshot

Hakim Elakhrass

Co-founder and CEO of NannyML

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