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Bayesian Machine Learning in Python: A/B Testing

KEY FEATURES

A/B testing is used everywhere, from marketing, retail, news feeds, online advertising, and much more. If you’re a data scientist, and you want to tell the rest of the company, “logo A is better than logo B,” you’re going to need numbers and stats to prove it. That’s where A/B testing comes in. In this course, you’ll do traditional A/B testing in order to appreciate its complexity as you elevate towards the Bayesian machine learning way of doing things.

Access 40 lectures & 3.5 hours of content 24/7
Improve on traditional A/B testing w/ adaptive methods
Learn about epsilon-greedy algorithm & improve upon it w/ a similar algorithm called UCB1
Understand how to use a fully Bayesian approach to A/B testing

PRODUCT SPECS

Details & Requirements

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion not included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels, but knowledge of calculus, probability, Python, Numpy, Scipy, and Matplotlib is expected
All code for this course is available for download here, in the directory ab_testing

Compatibility

Internet required

THE EXPERT

The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master’s thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

Deep Learning: GANs and Variational Autoencoders

KEY FEATURES

Variational autoencoders and GANs have been two of the most interesting recent developments in deep learning and machine learning. GAN stands for generative adversarial network, where two neural networks compete with each other. Unsupervised learning means you’re not trying to map input data to targets, you’re just trying to learn the structure of that input data. In this course, you’ll learn the structure of data in order to produce more stuff that resembles the original data.

Access 41 lectures & 5.5 hours of content 24/7
Incorporate ideas from Bayesian Machine Learning, Reinforcement Learning, & Game Theory
Discuss variational autoencoder architecture
Discover GAN basics

PRODUCT SPECS

Details & Requirements

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion not included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels, but knowledge of calculus, probability, object-oriented programming, Python, Numpy, linear regression, gradient descent, and how to build a feedforward and convolutional neural network in Theano and TensorFlow is expected
All code for this course is available for download here, in the directory unsupervised_class3

Compatibility

Internet required

THE EXPERT

Advanced AI: Deep Reinforcement Learning in Python

KEY FEATURES

This course is all about the application of deep learning and neural networks to reinforcement learning. The combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Unlike supervised and unsupervised learning algorithms, reinforcement learning agents have an impetus—they want to reach a goal. In this course, you’ll work with more complex environments, specifically, those provided by the OpenAI Gym.

Access 52 lectures & 5 hours of content 24/7
Extend your knowledge of temporal difference learning by looking at the TD Lambda algorithm
Explore a special type of neural network called the RBF network
Look at the policy gradient method
Examine Deep Q-Learning

PRODUCT SPECS

Details & Requirements

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion not included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels, but knowledge of calculus, probability, object-oriented programming, Python, Numpy, linear regression, gradient descent, how to build a feedforward and convolutional neural network in Theano and TensorFlow, Markov Decision Processes, and how to implement Dynamic Programming, Monte Carlo, and Temporal Difference is expected
All code for this course is available for download here, in the directory rl2

Compatibility

Internet required

THE EXPERT

Artificial Intelligence: Reinforcement Learning in Python

KEY FEATURES

When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing—playing chess and Go, driving cars, etc. Reinforcement learning has recently become popular for doing all of that and more. Reinforcement learning opens up a whole new world. It’s lead to new and amazing insights both in behavioral psychology and neuroscience. It’s the closest thing we have so far to a true general artificial intelligence, and this course will be your introduction.

Access 71 lectures & 5.5 hours of content 24/7
Discuss the multi-armed bandit problem & the explore-exploit dilemma
Learn ways to calculate means & moving averages and their relationship to stochastic gradient descent
Explore Markov Decision Processes, Dynamic Programming, Monte Carlo, & Temporal Difference Learning
Understand approximation methods

PRODUCT SPECS

Details & Requirements

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion not included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels, but knowledge of calculus, probability, object-oriented programming, Python, Numpy, linear regression, and gradient descent is expected
All code for this course is available for download here, in the directory rl

Compatibility

Internet required

THE EXPERT

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