Are you curious about Machine Learning but have no idea where to start?
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Thanks to machine learning, there's never been a more exciting time in the history of computer science. Every day, new breakthroughs are changing what's possible with computers.
You might be intimidated by machine learning or think it's something that only the top companies and research institutions can use, but that's not true. Machine Learning is for everyone—and it's fun!
Machine Learning Tutorials
De-mystify the latest breakthroughs in Machine Learning with these fun tutorials.
Have you heard people talking about machine learning but only have a fuzzy idea of what that means? Are you tired of nodding your way through conversations with co-workers? Let’s change that!
This guide is for anyone who is curious about machine learning but has no idea where to start.
Learn how about neural networks and how recurrent neural networks can be used to general new data based on existing data.
Learn how Convolutional Neural Networks work and how they are used to recognize objects in photographs.
Let’s learn how modern face recognition works! But just recognizing your friends would be too easy. We can push this tech to the limit to solve a more challenging problem — telling Will Ferrell (famous actor) apart from Chad Smith (famous rock musician)!
Sequence-to-sequence learning is a very powerful technique that revolutionizing language translation. After we see how it is used for translation, we’ll also learn how the exact same algorithm can be used to write AI chat bots and describe pictures.
But speech recognition has been around for decades, so why is it just now hitting the mainstream? The reason is that deep learning finally made speech recognition accurate enough to be useful outside of carefully controlled environments. Let’s learn how to do speech recognition with deep learning!
Deep Convolutional Generative Adversarial Networks (or DCGANs for short) are one of the most exciting new areas of machine learning research. DCGANs are able to hallucinate original photo-realistic pictures by using a clever combination of two deep neural networks that compete with each other. Let’s use generative models to do something a bit more silly — make artwork for 8-bit video games!
Python Tips and Tricks
Python is one of the most popular programming language for Machine Learning. So the more you know about Python, the better!
To make it simple for anyone to play around with machine learning, I’ve put together a simple virtual machine image that you can download and run without any complicated installation steps.
One common complaint about the Python language is that variables are Dynamically Typed. That means you declare variables without giving them a specific data type. But with Python 3.6, you now have the choice of using Static Type Checking which can automatically catch many common errors while coding!
If you have a computer made in the last decade, there’s a good chance it has 4 (or more) CPU cores. That means that 75% or more of your computer’s power is sitting there nearly idle while you are waiting for your program to finish running! Let’s learn how to take advantage of the full processing power of your computer by running Python functions in parallel.
Video Courses on Lynda.com
In each of my courses, you will get your hands dirty and code a real Machine Learning project from start to finish. And best of all, you can take all these courses for free when you sign up using this special free 30-day trial offer!
In this project-based course, discover how to use machine learning to build a value estimation system that can deduce the value of a home. Follow Adam Geitgey as he walks through how to use sample data to build a machine learning model, and then use that model in your own programs.
Recommendation systems are a key part of almost every modern consumer website. The systems help drive customer interaction and sales by helping customers discover products and services they might not ever find themselves. By the end of the course, you'll be equipped to use machine learning yourself to solve recommendation problems.