Learn Deep Learning
This course has been designed to learn deep learning from A-Z, which includes Real-World Case Studies. In the present scenario, artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors, and Google Deep mind’s AlphaGo beat the World champion at Go – a game where intuition plays a key role.
But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that’s why it’s at the heart of Artificial intelligence.
Mastering Deep Learning is not just about knowing the intuition and tools, it’s also about being able to apply these models to real-world scenarios and derive actual measurable results for the business or project. That’s why in this course you will have Hands-on with six exciting challenges such as:
- Artificial Neural Networks to solve a Customer Churn problem.
- Convolutional Neural Networks for Image Recognition.
- Recurrent Neural Networks to predict Stock Prices.
- Self-Organizing Maps to investigate Fraud.
- Boltzmann Machines to create a Recommender System.
- Stacked Autoencoders to take on the challenge for the Netflix $1 Million prizes.
Stacked Autoencoders is a brand new technique in Deep Learning which didn’t even exist a couple of years ago.
What are Artificial Neural Networks?
Artificial neural networks are one of the main tools used in machine learning. As the “neural” part of their name suggests, they are brain-inspired systems that are intended to replicate the way that we humans learn. Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use. They are excellent tools for finding patterns that are far too complex or numerous for a human programmer to extract and teach the machine to recognize.
Why learn Deep Learning A-Z?
Here are five reasons to think Deep Learning A-Z™ really is different and stands out from the crowd of other training programs out there:
- ROBUST STRUCTURE
- INTUITION TUTORIALS
- EXCITING PROJECTS
- HANDS-ON CODING
- IN-COURSE SUPPORT
What you will learn from this Deep Learning course?
- Understand the intuition behind Artificial Neural Networks, Recurrent Neural Networks & Convolutional Neural Networks.
- Apply Artificial & Recurrent Neural Networks in practice.
- Understand the intuition behind Self-Organizing Maps.
- Apply Self-Organizing Maps in practice.
- Understand the intuition behind Boltzmann Machines.
- Apply Boltzmann Machines in practice.
- Understand the intuition behind Auto Encoders.
- Apply Auto Encoders in practice.
Who this course is for
As you can see, there are lots of different tools in the space of Deep Learning, and in this course certainly, they will show you the most important and most progressive ones so that when you learn Deep Learning from this course, your skills are on the cutting edge of today’s technology.
- Students who have at least high school knowledge in math and who want to learn Deep Learning.
- Any intermediate level people who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning.
- Anyone who is not that comfortable with coding but who is interested to learn Deep Learning and wants to apply it easily on datasets.
- Students in college who want to start a career in Data Science.
- Data analysts who want to level up in Deep Learning.
- People who are not satisfied with their job and who want to become a Data Scientist.
- Any people who want to create added value to their business by using powerful Deep Learning tools.
- Business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business.
- Entrepreneurs who wants to create disruption in an industry using the most cutting-edge Deep Learning algorithms.
About the Instructors
Kirill Eremenko (Data Scientist)
He is a Data Science management consultant with over five years of experience in finance, retail, transport, and other industries and they are trained by the best analytics mentors at Deloitte Australia and today.
Hadelin de Ponteves (AI Entrepreneur)
Hadelin is the co-founder and CEO at BlueLife AI, which leverages the power of cutting-edge Artificial Intelligence to empower businesses to make massive profits by innovating, automating processes, and maximizing efficiency.
This Deep Learning Course has been divided into 6 parts, where you will learn:
Part 1- Learn Deep Learning: Artificial Neural Network
- ANN Intuition.
- Building an ANN.
Part 2- Learn Deep Learning: Convolutional Neural Networks
- CNN Intuition and Building a CNN.
Part 3- Learn Deep Learning: Recurrent Neural Network
- RNN Intuition and Building an RNN.
- Evaluating and Improving the RNN.
Part 4- Self Organizing Maps
- SOMs Intuition and Building a SOM.
- Mega Case Study.
Part 5- Boltzmann Machines
- Boltzmann Machines Intuition.
- Building a Boltzmann Machines.
Part 6- AutoEncoder
- Auto Encoders Intuition.
- Building an Auto Encoder.
Annex – Get the Machine Learning Basics
- Regression and Classification Intuition.
- Data pre-processing Template.
- Logistic Regression Implementation.
In order to learn Deep Learning or to enroll in this program, one should be familiar with high school mathematics and basic Python programming knowledge.
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- Online Course
- All levels
- 1-4 Weeks
- Paid Course (Paid certificate)
- Basic Scripting in Python High School Level Maths
- Data Science Deep learning Machine learning Neural Networks