The free instructional videos and case studies focus on the less technical aspects of the technology as it applies to top execs attempting to integrate AI, including strategy, company culture and ethical responsibilities, into their operations. They're the latest in a series of instructional materials Microsoft has released as it looks to address a general lack of educational resources and talent in the AI field. The material was inspired by conversations Microsoft has had over the past three years with client executives, whom the company said felt there was a dearth of educational resources on AI that reached beyond the nuts-and-bolts technical level, according to Mitra Azizirad, Microsoft's corporate vp of AI marketing and productization. "[The goal] really was to approach the very distinct business needs that we saw all of our business leaders have been asking about over and over again," Azizirad said. "We wanted to make sure we were meeting the needs of business leaders and really empowering them, no matter where they were on their journey, to drive an AI transformation with a focus on strategy, culture and governance."

Microsoft today introduced the AI Business School, a series of case studies and free instructional videos made to help business executives design and successfully implement an AI strategy within their organization. The Microsoft AI Business School is born out of three years of conversations with customers and follows the launch of an AI school for developers and AI School first introduced last year. The AI Business School follows the lead of similar instructional guides, such as the AI Transformation Playbook from Andrew Ng. Unlike others, AI Business School material draws on three years of conversations with customers implementing AI, as well as lessons learned from AI solutions Microsoft introduced internally, Microsoft vice president of AI marketing and productization Mitra Azizirad told VentureBeat in a phone interview. Course content will focus on four main areas: strategy, culture, technology basics, and responsible AI.

While delivering my Practical Data Science course to over 300 new data scientists over the last year-and-a-half, I have been updating and improving (I hope) some of the key demos that I use for teaching. As I have been often asked to share it with the attendees, I decided to make it available more broadly. I have just uploaded the code for: performing classification diagnostics and plotting classifier performance using R, sample DMX that shows how to correctly query an association rules model to make cross-sell predictions with and without demographic (user-level) data, and an example in SQL Server R Services that shows the four different ways how to analyse a 10 million row data set, containing mortgage default risk information, using logistic regression. Click here to read more.

Combine Python & TensorFlow powers to build projects. In this course, you will learn how to code in Python, calculate linear regression with TensorFlow, and use AI for automation. Together with a professional you will perform CIFAR 10 image data and recognition and analyze credit card fraud by building practical projects. We explain everything in a straightforward teaching style that is easy to understand. Join Mammoth Interactive in this course, where we blend theoretical knowledge with hands-on coding projects to teach you everything you need to know as a beginner to credit card fraud detection What you'll learn Learn how to code in Python, a popular coding language used for websites like YouTube and Instagram.

In this paper, we aim at providing an introduction to the gradient descent based optimization algorithms for learning deep neural network models. Deep learning models involving multiple nonlinear projection layers are very challenging to train. Nowadays, most of the deep learning model training still relies on the back propagation algorithm actually. In back propagation, the model variables will be updated iteratively until convergence with gradient descent based optimization algorithms. Besides the conventional vanilla gradient descent algorithm, many gradient descent variants have also been proposed in recent years to improve the learning performance, including Momentum, Adagrad, Adam, Gadam, etc., which will all be introduced in this paper respectively.

Skills in deep learning are in great demand, although these skills can be challenging to identify and to demonstrate. Explaining that you are familiar with a technique or type of problem is very different to being able to use it effectively with open source APIs on real datasets. Perhaps the most effective way of demonstrating skill as a deep learning practitioner is by developing models. A practitioner can practice on standard publicly available machine learning datasets and build up a portfolio of completed projects to both leverage on future projects and to demonstrate competence. In this post, you will discover how you can use small projects to demonstrate basic competence for using deep learning for predictive modeling.

Use Python & Keras to do 24 Projects - Recognition of Emotions, Age, Gender, Object Detection, Segmentation, Face Aging Master Computer Vision using Deep Learning in Python. You'll be learning to use the following Deep Learning frameworks. In this course, you will discover the power of Computer Vision in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.Computer vision applications involving Deep Learning are booming! Having Machines that can'see' will change our world and revolutionize almost every industry out there. Machines or robots that can see will be able to: Perform surgery and accurately analyze and diagnose you from medical scans.

Lois Jean Brady has over 25 years of experience as a practicing speech-language pathologist, assistive technology specialist and Certified Autism Specialist (CAS). Lois is a proud board member for California Communications Access Foundation (CCAF) and Board of Advisors for International Board of Credentialing and Continuing Education Standards (IBCCES). Career accomplishments include winner of three Autism Hackathons, Benjamin Franklin Award for Apps for Autism, and an Ursula Award for Autism Today TV. In addition to Apps for Autism, she has co-authored Speech in Action and Speak, Move, Play and Learn with Children on the Autism Spectrum. She has authored two professional development courses on the topics of technology and animal-assisted therapy.

See the full picture: Learn how to combine the three most important tools in data science: Python, SQL, and Tableau Python, SQL, and Tableau are three of the most widely used tools in the world of data science. Python is the leading programming language, SQL is the most widely used means for communication with database systems, Tableau is the preferred solution for data visualization, To put it simply – SQL helps us store and manipulate the data we are working with, Python allows us to write code and perform calculations, and then Tableau enables beautiful data visualization. A well-thought-out integration stepping on these three pillars could save a business millions of dollars annually in terms of reporting personnel. Therefore, it goes without saying that employers are looking for Python, SQL, and Tableau when posting Data Scientist and Business Intelligence Analyst job descriptions. What you'll learn How to use Python, SQL, and Tableau together Software integration Data preprocessing techniques Apply machine learning Create a module for later use of the ML model Connect Python and SQL to transfer data from Jupyter to Workbench Visualize data in Tableau Analysis and interpretation of the exercise outputs in Jupyter and Tableau Get Udemy Discount 94% off Python SQL Tableau: Integrating Python, SQL, and Tableau

Setting up your Python environment for Machine Learning can be a tricky task. If you've never set up something like that before, you might spend hours fiddling with different commands trying to get the thing to work. But we just want to get right to the ML! In this tutorial, you will learn how to set up a stable Python Machine Learning development environment. You'll be able to get right down into the ML and never have to worry about installing packages ever again.