From self-driving cars and a cucumber sorter to disaster-prediction programs
and cancer-detection systems
, current applications of artificial intelligence technology would have The Jetsons blushing and Asimov
deeply shook. According to one survey of industry experts
at an AI conference, intelligent machines will be able to perform any intellectual task a human can perform by the year 2050.
As such, there's a growing need among companies for AI professionals that know the ins and outs of machine learning (ML) - giving a device access to data and letting it learn for itself - as well as its newer subset, deep learning. Capable of making independent decisions about unstructured data, deep learning networks have been described by Forbes
as being capable of unlocking "the treasure trove of unstructured big data for those with the imagination to use (them)."
If launching a lucrative career in the ever-expanding field of AI sounds right up your alley, The Deep Learning and Artificial Intelligence Introductory Bundle
is the perfect place to start. This collection of four online classes covers the knowledge and skill set you need to start exploring deep learning applications that you can use in the real world - until the robots take over, at least.
Here's what's included:
Deep Learning Prerequisites: Linear Regression in Python
Understanding linear regression, or using probability theory to find that coveted "line of best fit," is the first step in building intelligent machines that act like neurons in a neural network. In this course, you'll learn how to construct a linear regression module in Python - knowledge that you'll later apply to a program that predicts a patient's systolic blood pressure given their age and weight.
Deep Learning Prerequisites: Logistic Regression in Python
A core facet of machine learning, data science, and statistics, logistic regression can be used to create a classification algorithm that behaves like a biological neuron. This three-hour class aims to give you real-world examples of logistic regression that can be applied to a deeper understanding of deep learning. You'll start by coding your own logistic regression module in Python, and then work your way up to building a course project that predicts user actions on a website given user data.
Data Science: Deep Learning in Python
This course will teach you how to build your first artificial neural network - the same kind of technology that's used in Google Translate, self-driving cars, and Apple's personal assistant, Siri - using tools like Numpy and Google's TensorFlow library.
Data Science: Practical Deep Learning in Theano & TensorFlow
Once you've mastered the basics, you'll delve into more advanced deep learning concepts in this set of 23 lectures. Using TensorFlow and Theano, you'll learn how to build a neural network while exploring techniques such as dropout regularization and batch and stochastic gradient descent. ("What are those?" you may ask. Well, you'll find out in this class!)
Instead of buying these courses individually for $120 apiece, head on over to the Mashable Shop to purchase lifetime access to The Deep Learning and Artificial Intelligence Introductory Bundle for only $39 - a savings of 91%.