This learning path will introduce you to neural networks, TensorFlow, and Google Cloud Machine Learning Engine. Even if you don't have any previous experience with machine learning, that's okay, because these courses cover the basic concepts. The first course explains the fundamentals of neural networks and how to implement them using TensorFlow. Then it shows you how to train and deploy a model using Cloud ML Engine. The second course explains how to build convolutional neural networks, which are very effective at performing object detection in images, among other tasks.

This course introduces Python programming as a way to have hands-on experience with Data Science. It starts with a few basic examples in Python before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you'll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We're approaching a new era where only apps and games that are considered "smart" will survive.

Randomness is a big part of machine learning. Randomness is used as a tool or a feature in preparing data and in learning algorithms that map input data to output data in order to make predictions. In order to understand the need for statistical methods in machine learning, you must understand the source of randomness in machine learning. The source of randomness in machine learning is a mathematical trick called a pseudorandom number generator. In this tutorial, you will discover pseudorandom number generators and when to control and control-for randomness in machine learning.

Despite being around for decades, AI is currently one of the most popular topics in business with Gartner predicting that by 2020 AI will be a top five investment priority for more than 20% of CIOs. Join Joey Moore, Director of Product Marketing and Greg Moore, Manager for Personalisation and Campaign Strategy of Episerver for a practical webinar on The 3 key ways to improve the customer journey. AI is currently closing the gap between detecting patterns from large data sets and predicting intent (a role traditionally reserved for human marketers and merchandizers). AI-powered technologies are replacing the manual work traditionally completed by merchandizers to make product recommendations and marketers to make ad spend decisions. For now, AI will not completely replace all human effort, but it will dramatically improve the effectiveness of ecommerce teams that use it while enhancing the experience of shoppers who purchase from AI-centered ecommerce businesses.

The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both what and how to imitate. We pursue an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its experience into a goal-conditioned skill policy with a novel forward consistency loss. In our framework, the role of the expert is only to communicate the goals (i.e., what to imitate) during inference. The learned policy is then employed to mimic the expert (i.e., how to imitate) after seeing just a sequence of images demonstrating the desired task. Our method is "zero-shot" in the sense that the agent never has access to expert actions during training or for the task demonstration at inference. We evaluate our zero-shot imitator in two real-world settings: complex rope manipulation with a Baxter robot and navigation in previously unseen office environments with a TurtleBot. Through further experiments in VizDoom simulation, we provide evidence that better mechanisms for exploration lead to learning a more capable policy which in turn improves end task performance. Imitating expert demonstration is a powerful mechanism for learning to perform tasks from raw sensory observations. The current dominant paradigm in learning from demonstration (LfD) (Ar-gall et al., 2009; Ng & Russell, 2000; Pomerleau, 1989; Schaal, 1999) requires the expert to either manually move the robot joints (i.e., kinesthetic teaching) or teleoperate the robot to execute the desired task. The expert typically provides multiple demonstrations of a task at training time, and this generates data in the form of observation-action pairs from the agent's point of view. Such a heavily supervised approach, where it is necessary to provide demonstrations by controlling the robot, is incredibly tedious for the human expert. Moreover, for every new task that the robot needs to execute, the expert is required to provide a new set of demonstrations. Instead of communicating how to perform a task via observation-action pairs, a more general formulation allows the expert to communicate onlywhat needs to be done by providing the observations of the desired world states via a video or a sparse sequence of images. This way, the agent is required to infer how to perform the task (i.e., actions) by itself.

About this course: If you're a software developer and new to blockchain, this is the course for you. Several experienced IBM blockchain developer advocates will lead you through a series of videos that describe high-level concepts, components, and strategies on building blockchain business networks. You'll also get hands-on experience modeling and building blockchain networks as well as create your first blockchain application. The first part of this course covers basic concepts of blockchain, and no programming skills are required. However, to complete three of the four labs, you must understand basic software object-oriented programming and how to use the command line. It's also helpful, but not required, that you can write code in JavaScript.

About this course: In this course, we will learn all the core techniques needed to make effective use of H2O. Even if you have no prior experience of machine learning, even if your math is weak, by the end of this course you will be able to make machine learning models using a variety of algorithms. We will be using linear models, random forest, GBMs and of course deep learning, as well as some unsupervised learning algorithms. You will also be able to evaluate your models and choose the best model to suit not just your data but the other business restraints you may be under.

GitHub is launching a new bot-powered learning lab to help budding developers get up to speed on all things GitHub. The San Francisco-based code-hosting platform, which hosts some 80 million repositories and claims 27 million users, recently celebrated its tenth year in business. It has been rolling out a bunch of collaboration-focused tools of late, including a team discussions tool to help plan projects and share information, while its Atom text editor now lets developers collaborate on code simultaneously. Indeed, collaboration between coders and teams serves as a core underpinning facet of what makes GitHub tick, and this extends into training, with a dedicated training team previously helping to run in-person and remote training sessions on how to use the GitHub platform. The GitHub Learning Lab, which officially launches today, builds on GitHub's prior history of training people, except this time GitHub is using bots to expedite the learning process.

As a data scientist who has been in the profession for several years now, I am often approached for career advice or guidance in course selection related to machine learning by students and career switchers on LinkedIn and Quora. Some questions revolve around educational paths and program selection, but many questions focus on what sort of algorithms or models are common in data science today. With a glut of algorithms from which to choose, it's hard to know where to start. Courses may include algorithms that aren't typically used in industry today, and courses may exclude very useful methods that aren't trending at the moment. Software-based programs may exclude important statistical concepts, and mathematically-based programs may skip over some of the key topics in algorithm design. I've put together a short guide for aspiring data scientists, particularly focused on statistical models and machine learning models (supervised and unsupervised); many of these topics are covered in textbooks, graduate-level statistics courses, data science bootcamps, and other training resources (some of which are included in the reference section of the article).