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Data Science: Deep Learning and Neural Networks in Python

The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow
(7,607 reviews)
46,953 students
Created by


CourseMarks Score®







Platform: Udemy
Video: 11h 13m
Language: English
Next start: On Demand

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Detailed Analysis

CourseMarks Score®

9.4 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

10.0 / 10
This course was last updated on 8/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

9.2 / 10
We analyzed factors such as the rating (4.6/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

8.3 / 10
Video Score: 9.3 / 10
The course includes 11h 13m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 7 hours 24 minutes of 1,300 Python courses on Udemy.
Detail Score: 10.0 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 5.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
0 resource.
0 exercise.
0 test.

Table of contents


This course will get you started in building your FIRST artificial neural network using deep learning techniques. 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.

“If you can’t implement it, you don’t understand it”
•Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
•My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
•Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
•After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…

Suggested Prerequisites:
•calculus (taking derivatives)
•matrix arithmetic
•Python coding: if/else, loops, lists, dicts, sets
•Numpy coding: matrix and vector operations, loading a CSV file
•Be familiar with basic linear models such as linear regression and logistic regression

•Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)

You will learn

✓ 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


• Basic math (calculus derivatives, matrix arithmetic, probability)
• Install Numpy and Python
• Don’t worry about installing Tensor
• Flow, 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

This course is for

• 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.

How much does the Data Science: Deep Learning and Neural Networks in Python course cost? Is it worth it?

The course costs $14.99. And currently there is a 83% discount on the original price of the course, which was $89.99. So you save $75 if you enroll the course now.
The average price is $19.0 of 1300 Python courses on Udemy.

Does the Data Science: Deep Learning and Neural Networks in Python course have a money back guarantee or refund policy?

YES, Data Science: Deep Learning and Neural Networks in Python has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Data Science: Deep Learning and Neural Networks in Python course, but there is a $75 discount from the original price ($89.99). So the current price is just $14.99.

Who is the instructor? Is Lazy Programmer Inc. a SCAM or a TRUSTED instructor?

Lazy Programmer Inc. has created 29 courses that got 116,511 reviews which are generally positive. Lazy Programmer Inc. has taught 457,898 students and received a 4.6 average review out of 116,511 reviews. Depending on the information available, Lazy Programmer Inc. is a TRUSTED instructor.
Artificial intelligence and machine learning engineer
Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I’ve created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I’ve used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I’ve used MySQL, Postgres, Redis, MongoDB, and more.


CourseMarks Score®







Platform: Udemy
Video: 11h 13m
Language: English
Next start: On Demand

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