Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. This course takes you through the various different concepts that get you acquainted and working with the different aspects of Machine Learning. You'll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics with Pandas. From there, you will be introduced to supervised learning, where you will explore the principles of machine learning and train different machine learning models. After that, you'll work with algorithms for regression analysis, and employ different types of regression, such as ridge and lasso regression, and spline interpolation using SciPy.

About this course: With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering.

We have spoken about machine learning and the internet of things as tools to optimize location analytics in logistics and supply chain management. It's an accepted fact that technology, especially cloud-based, can benefit companies by optimizing routes and predicting the accurate estimated time of arrivals (ETAs). The direct business value of this optimization lies in the streamlining of various fixed and variable costs associated with logistics. The Internet of Things (IoT) world may be exciting, but there are serious technical challenges that need to be addressed, especially by developers. In this handbook, learn how to meet the security, analytics, and testing requirements for IoT applications.

Timelinely lets you add highlights to interesting points in a video with pictures, links, maps, other videos, comments, questions and (polls/quizzes coming soon). Just paste a YouTube URL, and you're ready to create.

Today we are launching the 2018 edition of Cutting Edge Deep Learning for Coders, part 2 of fast.ai's Just as with our part 1 Practical Deep Learning for Coders, there are no pre-requisites beyond high school math and 1 year of coding experience--we teach you everything else you need along the way. This course contains all new material, including new state of the art results in NLP classification (up to 20% better than previously known approaches), and shows how to replicate recent record-breaking performance results on Imagenet and CIFAR10. The main libraries used are PyTorch and fastai (we explain why we use PyTorch and why we created the fastai library in this article). Each of the seven lessons includes a video that's around two hours long, an interactive Jupyter notebook, and a dedicated discussion thread on the fast.ai

Welcome to this epic masterclass on Keras (and so much more) with our #1 data scientist and app developer Nimish Narang, creator of over 20 Mammoth Interactive courses and a top-seller on Udemy. Anyone can take this course. If you already have experience using PyCharm and running Python files and programs on the interface, you can simply skip ahead to whatever section best suits your needs. Or, you can follow the progression of this meticulously curated course especially designed to take any absolute beginner off the street and make them a data modeler. This course is divided into days, but of course you can learn at your own pace.

Booz Allen and NVIDIA are offering deep learning training. NVIDIA is working with Booz Allen Hamilton to rapidly build solutions that are needed in cyberdefense for both government and commercial customers. Now, certified Deep Learning Institute instructors from NVIDIA and Booz Allen are offering training to a variety of customers on how to build your own effective deep learning and data-driven solutions. 'Deep Learning Demystified,' hosted by Booz Allen and NVIDIA, will provide instructor-led and hands-on deep learning training. Introduce yourself to key terminology, use cases from various industries, and learn how to effectively train, optimize, and deploy a neural network.

The Facebook Field Guide to Machine Learning is a six-part video series developed by the Facebook ads machine learning team. The series shares best real-world practices and provides practical tips about how to apply machine-learning capabilities to real-world problems. Machine learning and artificial intelligence are in the headlines everywhere today, and there are many resources to teach you about how the algorithms work and demonstrations of the latest cutting-edge research. However, if you're interested in using machine learning to enhance your product in the real world, it's important to understand how the entire development process works. It's not only what happens during the training of your models, but everything that comes before and after, and how each step can either set you up for success or doom you to fail.

Carnegie Mellon University today announced it will offer an undergraduate degree in artificial intelligence. The college claims the degree will be the first of its kind in the United States. The first courses for the Bachelor of Science degree will be offered this fall. Based in Pittsburgh, Carnegie Mellon University is a storied institution in AI circles, a central figure in the development of autonomous vehicles today and historically led by figures like Herbert Simon and Alan Newell, creators of some of the earliest forms of AI. A study by U.S. News and World Report released in March declared Carnegie Mellon the best computer science college in the U.S. for artificial intelligence, followed by Massachusetts Institute of Technology (MIT), Stanford University, and the University of California, Berkeley.

A wildly successful Kickstarter funded this Mammoth Interactive course. "It's simple and relaxing-- just what the doctor ordered." Enroll now to learn in-demand skills that employers are seeking. Data scientists make an average of $120,000 annually. With this course we will help get you there!