Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, you'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.

Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.

Discover how you can use deep learning to run natural language processing, image recognition, and artificial intelligence with Python package, Keras 2.0.

Introduction to Deep Learning in Python

Every day we interact with the cloud—whether it’s using Google Drive, apps like Salesforce, or accessing our favorite websites. Cloud computing has become the norm for many companies, but what exactly is the cloud and why is everyone rushing to adopt it? Designed for absolute novices, this course breaks down what the cloud is and explains terminology such as scalability, latency, and high-availability. You'll learn about the many advantages including ease of remote collaboration, how there are no hardware limitations, and reliable disaster recovery. You'll also discover the range of tools provided by major cloud providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. By the end of this course, you'll be able to confidently explain how cloud tools can increase productivity and save money, as well as ask the right questions about how to optimize your use of cloud tools.

A non-coding introduction to the world of cloud computing.

Discover what the cloud is, how it works, and what its benefits are. You’ll also learn about terminology like scalability, latency, and high availability.

Cloud Computing for Everyone

Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start DataCamp’s online Python curriculum now.

Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.

Discover how to use Python for data science, learning about the ways to store and manipulate data in the environment to begin conducting your own analyses.

Introduction to Python

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib's functions and get acquainted with data structures such as the dictionary and the pandas DataFrame. After covering key concepts such as boolean logic, control flow, and loops in Python, you'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

In this intermediate Python course, learn to visualize real data with Matplotlib's functions and get to know new data structures such as the pandas DataFrame.

Intermediate Python

Deep learning methods use data to train neural network algorithms to do a variety of machine learning tasks, such as classification of different classes of objects. Convolutional neural networks are deep learning algorithms that are particularly powerful for analysis of images. This course will teach you how to construct, train and evaluate convolutional neural networks. You will also learn how to improve their ability to learn from data, and how to interpret the results of the training.

Learn powerful techniques for image analysis in Python using deep learning and convolutional neural networks in Keras.

Learn to construct, train and evaluate convolutional neural networks and improve their ability to learn from data to conduct image analysis using Keras.

Image Processing with Keras in Python

So what is all this AI fuss about? Machine Learning, Deep Learning, Predictive Analytics -- what is the reality behind the hype? How do machines actually learn and what are their limits? How can we use Machine Learning to recognize written digits, predict customer churn and find structure in Elon Musk's tweets? All this -- and much more -- is the topic of this course, which will introduce you to the world of AI in a gentle, but firm and very practical manner.

Learn the fundamentals of AI. No programming experience required!

Learn to work with labeled data, train regression models, divide data into clusters, detect anomalies, select the right model, and create neural networks.

AI Fundamentals

Machine Learning models are based on numerical values to make predictions/classification, but how can computers deal with text data? With the huge increase of available text data, applications such as automatic document classification, text generation, and neural machine translation became possible. In this course, you will learn how to use Recurrent Neural Networks to classify text (binary and multiclass), generate phrases simulating the character Sheldon from The Big Bang Theory TV Show, and translate Portuguese sentences into English. Are you ready to start your journey into Language Models using Keras and Python? Dive in!

Use RNNs to classify text sentiment, generate sentences, and translate text between languages.

Recurrent Neural Networks for Language Modeling in Python

Shiny is an R package that makes it easy to build highly interactive web apps directly in R. Using Shiny, data scientists can create interactive web apps that allow your team to dive in and explore your data as dashboards or visualizations. If you want to bring your data to life, Shiny is the way to go! Using data about baby names, food ingredients, and UFO sightings, you'll build a variety of different Shiny apps that leverage different inputs and outputs. You’ll also learn the basics of reactive expressions. By the end of this course, you’ll have the Shiny skills you need to build your first app in R.

Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations. 

Building Web Applications with Shiny in R

pandas is the world's most popular Python library, used for everything from data manipulation to data analysis. In this course, you'll learn how to manipulate DataFrames, as you extract, filter, and transform real-world datasets for analysis. Using pandas you’ll explore all the core data science concepts. Using real-world data, including Walmart sales figures and global temperature time series, you’ll learn how to import, clean, calculate statistics, and create visualizations—using pandas to add to the power of Python!

Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.

Data Manipulation with pandas

Statistics is the study of how to collect, analyze, and draw conclusions from data. It’s a hugely valuable tool that you can use to bring the future into focus and infer the answer to tons of questions. For example, what is the likelihood of someone purchasing your product, how many calls will your support team receive, and how many jeans sizes should you manufacture to fit 95% of the population? In this course, you'll discover how to answer questions like these as you grow your statistical skills and learn how to calculate averages, use scatterplots to show the relationship between numeric values, and calculate correlation. You'll also tackle probability, the backbone of statistical reasoning, and learn how to use Python to conduct a well-designed study to draw your own conclusions from data.

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

Introduction to Statistics in Python

Over 50% of all corporate data is stored in the cloud. As cloud computing becomes more and more ubiquitous, mastering at least one cloud platform is now crucial for data practitioners. In this course, you’ll get to grips with AWS—the market-leading cloud provider. You’ll also learn about its main core services, best practices to design AWS applications, and the benefits of using AWS for businesses.
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This course is a great starting point to prepare you for the AWS Cloud Practitioner Certification.


Learn the fundamentals of cloud computing with AWS.

Learn about AWS cloud computing including benefits, applications, economics, and core services like storage, management, and well-architected framework.

AWS Cloud Concepts

When working with big data, you’ll face two common obstacles: using too much memory and long runtimes. The Dask library can lower your memory use by loading chunks of data only when needed. It can lower runtimes by using all your available computing cores in parallel. Best of all, it requires very few changes to your existing Python code. In this course, you use Dask to analyze Spotify song data, process images of sign language gestures, calculate trends in weather data, analyze audio recordings, and train machine learning models on big data.

Learn to upscale your Python workflows to efficiently handle big data with Dask.

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