4.45 out of 5
4.45
50 reviews on Udemy

Ultimate Neural Nets and Deep Learning Masterclass in Python

The best way to learn machine learning and AI using python: simply and fully explained concepts with practical exercises
Instructor:
John Harper
375 students enrolled
English [Auto-generated]
Confidently code neural nets
Create your own machine learning programs
Able to construct AI architectures
You will have a full portfolio to maximise your employability
An understanding of the overlying strategies for solving real world problems
Knowledgable in ways that you can monetise your new skill
Fully able to code image classifiers from scratch
A fundamental understanding of the underlying mathematical concepts

USED BY SOFTWARE STUDENTS AT CAMBRIDGE UNIVERSITY – WORLD CLASS DEEP LEARNING COURSE – UPDATED CONTENT January 2018

Master practical deep learning and neural network concepts and fundamentals

My course does exactly what the title describes in a simple, relatable way. I help you to grasp the complete start to end concepts of fundamental deep learning.

Why you need this course

Coming to grips with python isn’t always easy. On your own it can be quite confusing, difficult and frustrating. I’ve been through the process myself, and with the help of lifelong … I want to share this with my fellow beginners, developers, AI aspirers, with you.

What you will get out of this course

I will give you straightforward examples, instructions, advice, insights and resources for you to take simple steps to create your own neural networks from scratch. By the end of the course you will be able to create neural networks to create your very own image classifier, able to work on your own images.

I personally provide support within the course, answering questions and giving feedback on what you’re discovering/creating along the way. I don’t just throw you in at the deep end – I provide you with the resources to learn and develop what you need at a pace to work for you and then help you stroll through to the finish line. Studies have shown that to learn effectively from online courses tutorials should last around ten minutes each. Therefore to maximise your learning experience all of the lectures in this course have been created around this amount of time. 

My course integrates all of the aspects required to get you on the road becoming a successful deep learning developer. I teach and I preach, with live, practical exercises and walkthroughs at the end of each section!

Why this price?

As a professional AI developer I have over five years in Senior positions in software development and technology entrepreneurship, with experience in tutoring and creating online courses, catering to thousands of students. Face to face I charge $50 per hour for a student. To complete the curriculum that I offer it would cost them between $500 – $1000.

To reach more people than I could face to face I decided to create this course. As I add more content I intend to raise the price but for now I’ve decided on this price – the cost of around just three lessons.

By paying a small cost for this course I believe you will get your value back, with a lot more by the time you have completed it.

Ask yourself – how much is mastering the fundamentals of python (and setting up your skills for AI engineering) worth to you?

How long will it take?

Although everyone is different, on average it has taken existing students between 4 – 6 weeks to complete the course, whilst developing their skills and knowledge along the way.

Who this is not for

This course is not for anyone looking for a one-click fix. Although I provide you with a path walked enough times that it can be a relatively smooth journey it still requires a lot of time and effort from you to make it happen. If you’re not interested in putting in your energy to truly better yours skills in python then this may not be the right course for you.

Is there a money back guarantee if I’m not happy?

Absolutely. I am confident that my course will bring you more value than you spend on the course. As one of the previously top featured Udemy Instructors my motto is ‘your success is my success’. If within the first 30 days you feel my course is not going to help you to achieve your goals in python programming then you get a no questions asked, full discount.

What materials are included?

The majority of my lectures I have chosen to be in video format so that you can hear and see me when we’re going through each and every area of the course.

Aswell as the course lectures, presentations, scripts and quizzes the course will soon also offers my full support as an instructor to answer questions, provide feedback and support

I will be constantly adding more valuable content and resources to the course as time goes by. Keep checking back here if you’re not sure right now and feel free to send me a message with any questions or requests you may have.

So go ahead and click the ‘Take this course‘ button at the top right of your screen. I look forward to seeing you on the course.

1
About the course
2
How to maximise your learning

The basic theory

1
What is deep learning, machine learning, AI?
2
Real world applications of neural nets
3
Linear regression
4
Line of best fit
5
Linear regression with big data
6
Overfitting, training and evaluation data
7
Cost and loss
8
How neural nets learn

Neural networks - the theory

1
Neural networks recap
2
Training wheels off - linear regression to neural nets
3
Adding an activation function
4
*Milestone - First neural network*
5
Multiple inputs
6
Hidden layers
7
Your neural network learning, back propagation

Setting up your computer

1
Installing python, tensorflow and other libraries
2
Jupyter notebook
3
Working from the command line
4
Introducing you to AWS

The fun stuff - creating your first neural network

1
'Hello world' for tensorflow
2
Feeding data and running sessions
3
Data structures
4
Loading data into tensorflow
5
Loading data part 2
6
One hot encoding
7
Neural network - let's go!
8
Give your network a brain - backpropagation, loss and optimiser
9
Running your model
10
Using other frameworks

Portfolio project #1 - Creating an image classifier using Keras

1
Loading handwritten digits using Keras
2
Creating the model
3
Running your model

Hyperparameters and bias

1
Hyperparameters
2
Bias and variance
3
Strategies for reducing bias and variance
4
Activation functions

Conclusions

1
What we have covered in this course
2
How to continue progressing effectively
3
Thank you

Convolutional neural networks - high power image classifiers

1
What are CNNs? how are they useful?
2
How do CNNs work
3
Filters
4
Pooling
5
One full forward pass
6
Hyperparameters of CNNs
7
Practical #1 - Image classifier using Tensorflow
8
Preparing the data
9
Creating the CNN architecture
10
Adding the 'brain'
11
Running your model
You can view and review the lecture materials indefinitely, like an on-demand channel.
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Includes

7 hours on-demand video
Full lifetime access
Access on mobile and TV
Certificate of Completion