Ultimate Neural Nets and Deep Learning Masterclass in Python

Ultimate Neural Nets and Deep Learning Masterclass in Python, available at $19.99, has an average rating of 4.45, with 52 lectures, based on 71 reviews, and has 470 subscribers.

You will learn about 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 This course is ideal for individuals who are Any of the following: or Anyone with an interest in machine learning or Those familiar with python and looking to level up their skills or Those coming from a mathematical or engineering background It is particularly useful for Any of the following: or Anyone with an interest in machine learning or Those familiar with python and looking to level up their skills or Those coming from a mathematical or engineering background.

Enroll now: Ultimate Neural Nets and Deep Learning Masterclass in Python

Summary

Title: Ultimate Neural Nets and Deep Learning Masterclass in Python

Price: $19.99

Average Rating: 4.45

Number of Lectures: 52

Number of Published Lectures: 52

Number of Curriculum Items: 52

Number of Published Curriculum Objects: 52

Original Price: ￡199.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Any of the following:

Anyone with an interest in machine learning

Those familiar with python and looking to level up their skills

Those coming from a mathematical or engineering background

Target Audiences

Any of the following:

Anyone with an interest in machine learning

Those familiar with python and looking to level up their skills

Those coming from a mathematical or engineering background

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.

Course Curriculum

Lecture 1: About the course

Lecture 2: How to maximise your learning

Chapter 1: The basic theory

Lecture 1: What is deep learning, machine learning, AI?

Lecture 2: Real world applications of neural nets

Lecture 3: Linear regression

Lecture 4: Line of best fit

Lecture 5: Linear regression with big data

Lecture 6: Overfitting, training and evaluation data

Lecture 7: Cost and loss

Lecture 8: How neural nets learn

Chapter 2: Neural networks – the theory

Lecture 1: Neural networks recap

Lecture 2: Training wheels off – linear regression to neural nets

Lecture 3: Adding an activation function

Lecture 4: *Milestone – First neural network*

Lecture 5: Multiple inputs

Lecture 6: Hidden layers

Lecture 7: Your neural network learning, back propagation

Chapter 3: Setting up your computer

Lecture 1: Installing python, tensorflow and other libraries

Lecture 2: Jupyter notebook

Lecture 3: Working from the command line

Lecture 4: Introducing you to AWS

Chapter 4: The fun stuff – creating your first neural network

Lecture 1: Hello world for tensorflow

Lecture 2: Feeding data and running sessions

Lecture 3: Data structures

Lecture 4: Loading data into tensorflow

Lecture 5: Loading data part 2

Lecture 6: One hot encoding

Lecture 7: Neural network – lets go!

Lecture 8: Give your network a brain – backpropagation, loss and optimiser

Lecture 9: Running your model

Lecture 10: Using other frameworks

Chapter 5: Portfolio project #1 – Creating an image classifier using Keras

Lecture 1: Loading handwritten digits using Keras

Lecture 2: Creating the model

Lecture 3: Running your model

Chapter 6: Hyperparameters and bias

Lecture 1: Hyperparameters

Lecture 2: Bias and variance

Lecture 3: Strategies for reducing bias and variance

Lecture 4: Activation functions

Chapter 7: Conclusions

Lecture 1: What we have covered in this course

Lecture 2: How to continue progressing effectively

Lecture 3: Thank you

Chapter 8: Convolutional neural networks – high power image classifiers

Lecture 1: What are CNNs? how are they useful?

Lecture 2: How do CNNs work

Lecture 3: Filters

Lecture 4: Pooling

Lecture 5: One full forward pass

Lecture 6: Hyperparameters of CNNs

Lecture 7: Practical #1 – Image classifier using Tensorflow

Lecture 8: Preparing the data

Lecture 9: Creating the CNN architecture

Lecture 10: Adding the brain

Lecture 11: Running your model

Instructors

John Harper
Cambridge Python and Machine Learning Engineer/entrepreneur

Rating Distribution

1 stars: 1 votes

2 stars: 2 votes

3 stars: 10 votes

4 stars: 23 votes

5 stars: 35 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!