Goto

Collaborating Authors

specialization


12 Best Machine Learning Books For Beginners

#artificialintelligence

If you're looking to expanding your work beyond basic programming, it might be a great tome to dip into machine learning. Machine learning is the use of existing data to explain unknown data and predict future scenarios. It is also an integral part of technology and tech-related fields. If you're looking to pick up a useful skill or expand your tech prowess, learning about machine learning may broaden your career possibilities. With that in mind, we've crafted a list of the best machine learning books for beginners to get you started.


Top Online Courses to Learn Data Science with Certifications - GeeksforGeeks

#artificialintelligence

Data Science is a big deal these days! So it stands to reason that you might want to learn it because of its amazing potential and popularity in the technical market. But you don't need to spend thousands of dollars on getting a university degree to learn Data Science. It's even predicted that "armchair data scientists" who don't have any formal qualifications in Data Science but the skills to analyze data will become even more popular than "traditional data scientists". So you can easily learn the basics of Data Science from online courses and then build upon those basics by practice.


TensorFlow: Advanced Techniques

#artificialintelligence

Offered by DeepLearning.AI. About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. About you This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate. Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.


The predictive value of social media data - MODULE 3 - Data Prep: Preparing the Training Data

#artificialintelligence

Machine learning runs the world. It generates predictions for each individual customer, employee, voter, and suspect, and these predictions drive millions of business decisions more effectively, determining whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date, or medicate. But, to make this work, you've got to bridge what is a prevalent gap between business leadership and technical know-how. Launching machine learning is as much a management endeavor as a technical one. Its success relies on a very particular business leadership practice.


The new world of L&D needs Hybrid Training Strategy

#artificialintelligence

The new world of L&D is all about making people adapt to the demand in Cross-Skilling & Up-Skilling their resources. Organization are struggling to keep pace with the unprecedented demands in bringing together the skill requirement to meet the business outcome. Moving away from Ad-hoc & calendar programs to Strategy Solutions do post as a challenge. As a result, it's mission-critical that workforce transformation has a significant role to play in the future, keeping up with the pace of business demand. What is Workforce Transformation Model?


Project management overview - MODULE 2 - Scoping, Greenlighting, and Managing Machine Learning Initiatives

#artificialintelligence

Machine learning runs the world. It generates predictions for each individual customer, employee, voter, and suspect, and these predictions drive millions of business decisions more effectively, determining whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date, or medicate. But, to make this work, you've got to bridge what is a prevalent gap between business leadership and technical know-how. Launching machine learning is as much a management endeavor as a technical one. Its success relies on a very particular business leadership practice.


Machine Learning with TensorFlow on Google Cloud Platform Specialization Review

#artificialintelligence

When you finish every course and complete the hand on project, you'll earn a Certificate that you can share with prospective employers and your professional network. This online specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the specialization and earn your certificate. These Career Credentials will help you to unlock access to work in top universities and organizations as well as you can get a chance to get a career credential from the world's best educational institution. Starting a career in Machine Learning with TensorFlow opens doors to a great career.


Specialization in Hierarchical Learning Systems

arXiv.org Machine Learning

Joining multiple decision-makers together is a powerful way to obtain more sophisticated decision-making systems, but requires to address the questions of division of labor and specialization. We investigate in how far information constraints in hierarchies of experts not only provide a principled method for regularization but also to enforce specialization. In particular, we devise an information-theoretically motivated on-line learning rule that allows partitioning of the problem space into multiple sub-problems that can be solved by the individual experts. We demonstrate two different ways to apply our method: (i) partitioning problems based on individual data samples and (ii) based on sets of data samples representing tasks. Approach (i) equips the system with the ability to solve complex decision-making problems by finding an optimal combination of local expert decision-makers. Approach (ii) leads to decision-makers specialized in solving families of tasks, which equips the system with the ability to solve meta-learning problems. We show the broad applicability of our approach on a range of problems including classification, regression, density estimation, and reinforcement learning problems, both in the standard machine learning setup and in a meta-learning setting.


Deep Neural Networks Are Helping Decipher How Brains Work

WIRED

In the winter of 2011, Daniel Yamins, a postdoctoral researcher in computational neuroscience at the Massachusetts Institute of Technology, would at times toil past midnight on his machine vision project. He was painstakingly designing a system that could recognize objects in pictures, regardless of variations in size, position, and other properties--something that humans do with ease. The system was a deep neural network, a type of computational device inspired by the neurological wiring of living brains. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research develop ments and trends in mathe matics and the physical and life sciences. "I remember very distinctly the time when we found a neural network that actually solved the task," he said.


Are big data and machine learning methods enough? Part 1

#artificialintelligence

Sir David Hand gave a brilliant plenary talk and set the stage for a great panel discussion by cautioning us to remember that thinking is required and to be aware of all the dark data out there -- the data that we don't see, but that we need to take into account. Dark Data: Why What You Don't Know Matters is his latest book (see a blog post about it; if you haven't read it, you can get a sample excerpt). The panelists included Cameron Willden, statistician at W.L. Gore, who supports engineers and scientists across many different product lines; Sam Gardner, founder of Wildstats Consulting, with more than 30 years of experience doing statistical problem solving for government and industry; and JMP's Jason Wiggins, a 20-year US Synthetic veteran with expertise in process optimization, measurement systems analysis and predictive modeling/data mining. We ran out of time before we could answer all the questions from the livestream audience, but our panelists have kindly agreed to provide answers to many of them, further sharing the wisdom from their collective experiences. The questions are grouped by topic -- there were so many, we are doing two posts.