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Introduction to Deep Learning in Python Data Camp

Highlights
Tuition fee
Free
Free
Free
Unknown
Tuition fee
Free
Free
Free
Unknown
Duration
1 days
Duration
1 days
Apply date
Anytime
Unknown
Apply date
Anytime
Unknown
Start date
Anytime
Unknown
Start date
Anytime
Unknown
Taught in
English
Taught in
English

About

In this Introduction to Deep Learning in Python course offered by Data Camp you will learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.

Overview

Context

Deep learning is the machine learning technique behind the most exciting capabilities in robotics, natural language processing, image recognition, and artificial intelligence. In this Introduction to Deep Learning in Python course offered by Data Camp, you’ll gain hands-on practical knowledge of how to apply your Python skills to deep learning with the Keras 2.0 library.

Explore Keras Models with a Library Contributor

Taught by ex-Google data scientist and Keras contributor, Dan Becker, this deep learning course explores neural network models and how you can generate predictions with them. The first chapters will grow your understanding of both forward and backward propagation and how they work in practice.

Keras library is a Python library that can help you develop and review deep learning models. Like many Python libraries, it's free, open-source and very user friendly. You’ll start by creating a Keras model and will learn how to compile, fit, and classify it before making predictions.

Delve Further into Deep Learning

This course is part of several machine learning and deep learning tracks, offering you clear pathways to build your skills and experience in this area once you’ve completed the introductory course, whether you want to complete a personal project or move towards a career as a Machine Learning Scientist.

Programme Structure

Chapters include: 

  • Basics of deep learning and neural networks 
  • Building deep learning models with keras 
  • Optimizing a neural network with backward propagation 
  • Fine-tuning keras models

Key information

Duration

  • Part-time
    • 1 days

Start dates & application deadlines

You can apply for and start this programme anytime.

Language

English

Delivered

Online

Campus Location

  • New York City, United States

What students do after studying

Join for free or log in to access our complete career info list.

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Other requirements

General requirements

Prerequisites: 

  • Supervised Learning with scikit-learn

Tuition Fees

Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
  • International

    Non-residents
    Free
  • Out-of-State
    Free
  • Domestic

    In-State
    Free

Additional Details

This course can be accessed for free with the Data Camp Premium or Teams subscriptions

Funding

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