On the day I met Sebastian Thrun in Palo Alto, the State of California legalized self-driving cars. Gov. Jerry Brown arrived at the Google campus in one of the company's computer-controlled Priuses to sign the bill into law. "California is a big deal," said Thrun, the founder of Google's autonomous-car program, "because it tends to be hard to legislate here." He said it with typical understatement. An idea that was in its technological infancy a decade ago, when Thrun and his colleagues were racing to develop a vehicle that could drive itself more than a few miles on a desert test course, was now being officially sanctioned by the country's most populous state.

Link: Bayesian Machine Learning in Python: A/B Testing Udemy In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things. First, we'll see if we can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma. Bestseller Created by Lazy Programmer Inc What you'll learn Use adaptive algorithms to improve A/B testing performance Understand the difference between Bayesian and frequentist statistics Apply Bayesian methods to A/B testing In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things. First, we'll see if we can improve on traditional A/B testing with adaptive methods.

BESTSELLER 4.5 (26,962 ratings) 122,893 students enrolled Created by 365 Careers, 365 Careers Team What you'll learn The course provides the entire toolbox you need to become a data scientist Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow Impress interviewers by showing an understanding of the data science field Learn how to pre-process data Understand the mathematics behind Machine Learning (an absolute must which other courses don't teach!) Start coding in Python and learn how to use it for statistical analysis Perform linear and logistic regressions in Python Carry out cluster and factor analysis Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn Apply your skills to real-life business cases Use state-of-the-art Deep Learning frameworks such as Google's TensorFlowDevelop a business intuition while coding and solving tasks with big data Unfold the power of deep neural networks Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations Requirements No prior experience is required. We will start from the very basics You'll need to install Anaconda. We will show you how to do that step by step Microsoft Excel 2003, 2010, 2013, 2016, or 365 Each of these topics builds on the previous ones. And you risk getting lost along the way if you don't acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics.

Learn Docker "infrastructure as code" technology to define a system for performing standard but non-trivial data tasks on medium- to large-scale data sets, using Jupyter as the master controller. It is not uncommon for a real-world dataset to fail to be easily managed. The set may not fit well into access memory or may require prohibitively long processing. These are significant challenges to skilled software engineers and they can render the standard Jupyter system unusable. As a solution to this problem, Docker for Data Science proposes using Docker.

Online 1 on 1 class is created for more personalized learning experience. It demands a large number of teaching resources, which are scarce in China. To alleviate this problem, we build a platform (marketplace), i.e., \emph{Dahai} to allow college students from top Chinese universities to register as part-time instructors for the online 1 on 1 classes. To warn the unqualified instructors and ensure the overall education quality, we build a monitoring and alerting system by utilizing multimodal information from the online environment. Our system mainly consists of two key components: banned word detector and class quality predictor. The system performance is demonstrated both offline and online. By conducting experimental evaluation of real-world online courses, we are able to achieve 74.3\% alerting accuracy in our production environment.

How do you prepare to solve these problems and be ready for the workforce? Learn these three skills, which all are foundational, and build on each other, from easiest, to hardest. The really key thing about all of these skills is that they are also fundamental and critical to software development outside of data science, meaning that, in case you can't find a data science job, you can transition quickly to software development, or devops. I consider this flexibility just as important as training for a specific data-related gig. From HN: "Is there some kind of small and easy JS and/or PHP program allowing some easy work on a database?"

Following the success of previous deep learning tutorials like "Building RNNs is Fun with PyTorch and Google Colab" and "A Simple Neural Network from Scratch with PyTorch and Google Colab", I am excited to introduce a new series of tutorials on all things PyTorch and deep learning. In this first code tutorial, we will learn how to quickly train a deep learning model to understand some of PyTorch's basic building blocks. This notebook is inspired by the "Tensorflow 2.0 Quickstart for experts" notebook. After completion of this tutorial, you should be able to import data, transform it, and efficiently feed the data in batches to a convolution neural network (CNN) model for image classification. A new feature in these new tutorials is the introduction of exercises.

Machine learning is a set of techniques that allow machines to learn from data and experience, rather than requiring humans to specify the desired behavior by hand. Over the past two decades, machine learning techniques have become increasingly central both in AI as an academic field, and in the technology industry. This course provides a broad introduction to some of the most commonly used ML algorithms. It also serves to introduce key algorithmic principles which will serve as a foundation for more advanced courses, such as CSC412/2506 (Probabilistic Learning and Reasoning) and CSC421/2516 (Neural Networks and Deep Learning).

Why this is important Types of Natural Language Processing Classical vs. Deep Learning Models End to End Deep Learning Models Seq2Seq Architecture & Training Beam Search Decoding Requirements Just some high school mathematics level Basic Python programming knowledge We've talked about, speculated and often seen different applications for Artificial Intelligence - But what about one piece of technology that will not only gather relevant information, better customer service and could even differentiate your business from the crowd? ChatBots are here, and they came change and shape-shift how we've been conducting online business. Fortunately technology has advanced enough to make this a valuable tool something accessible that almost anybody can learn how to implement. If you want to learn one of the most attractive, customizable and cutting edge pieces of technology available, then this course is just for you! Why this is important Types of Natural Language Processing Classical vs. Deep Learning Models End to End Deep Learning Models Seq2Seq Architecture & Training Beam Search Decoding

You must have heard it several times that Artificial Intelligence is the future of growth. You must have read pieces that talked about how AI is helping businesses in increasing their sales, taking their customer experience to another level, improving their marketing, and reducing their operating costs. You decided to implement Artificial Intelligence in your business, but the results are far from expected. This is what might have gone wrong and what you can do about it. Everyone is talking about AI, but there are very few people who know what AI can actually do for them.