• Application Deadline
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Get Up to 90% Off! The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow in the Data Science - Deep Learning in Python at Udemy.


What Will I Learn In The Data Science - Deep Learning in Python at Udemy?

  • Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)
  • Learn how a neural network is built from basic building blocks (the neuron)
  • Code a neural network from scratch in Python and numpy
  • Code a neural network using Google's TensorFlow
  • Describe different types of neural networks and the different types of problems they are used for
  • Derive the backpropagation rule from first principles
  • Create a neural network with an output that has K > 2 classes using softmax
  • Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"
  • Install TensorFlow

This course will get you started in building your FIRST artificial neural network using deep learningtechniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

Next, we implement a neural network using Google's new TensorFlow library.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!

After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for.


If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow.

I have other courses that cover more advanced topics, such as Convolutional Neural Networks, Restricted Boltzmann Machines, Autoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

Detailed Programme Facts

Programme Structure

Courses include:
  • Classifying more than 2 things at a time 
  • Training a neural network 
  • Practical Machine Learning 
  • TensorFlow, exercises, practice, and what to learn next 
  • Project: Facial Expression Recognition 
  • Backpropagation Supplementary Lectures 


  • Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course
  • Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

English Language Requirements

This programme requires students to demonstrate proficiency in English.

Schedule IELTS

Academic Requirements

  • How to take partial derivatives and log-likelihoods (ex. finding the maximum likelihood estimations for a die)
  • Install Numpy and Python (approx. latest version of Numpy as of Jan 2016)
  • Don't worry about installing TensorFlow, we will do that in the lectures.
  • Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course

Hard Prerequisites:

  • calculus
  • linear algebra
  • probability
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy coding: matrix and vector operations, loading a CSV file

Tuition Fee

  • International Applies to you

    180 EUR/full
    Tuition Fee
    Based on the original amount of 180 EUR for the full programme and a duration of 3 days.
  • EU/EEA Applies to you

    180 EUR/full
    Tuition Fee
    Based on the original amount of 180 EUR for the full programme and a duration of 3 days.
We've labeled the tuition fee that applies to you because we think you are from and prefer over other currencies.
Udemy continuously offers discounts up to 90% off with full lifetime access to the course. Check the discounts available by clicking "Visit Programme Website".


Check the programme website for information about funding options.

StudyPortals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.

The Global Study Awards: get funded with up to £10,000 to study abroad

Together with the ISIC Association and British Council IELTS, StudyPortals offers you the chance to receive up to £10000 to expand your horizon and study abroad. We want to ultimately encourage you to study abroad in order to experience and explore new countries, cultures and languages.