Last week I had an amazing opportunity to present about machine learning and innovation to 110 people leaders at my company. It was a mixed audience of technical and non technical people. My message is that machine learning needs to be understood by everyone in the business, not just the data scientists. Domain knowledge with machine learning will really enable successful data projects. A few months ago I followed a tutorial on http://course.fast.ai

It can be difficult to understand how to prepare your sequence data for input to an LSTM model. Often there is confusion around how to define the input layer for the LSTM model. There is also confusion about how to convert your sequence data that may be a 1D or 2D matrix of numbers to the required 3D format of the LSTM input layer. In this tutorial, you will discover how to define the input layer to LSTM models and how to reshape your loaded input data for LSTM models. How to Reshape Input for Long Short-Term Memory Networks in Keras Photo by Global Landscapes Forum, some rights reserved.

Prerequisites: This is an advanced course, meant for upper-level undergraduates and beginning graduate students, but it is open to anyone. Good familiarity with algorithmic concepts is necessary. We will try to recall the concepts needed along the way. However, having taken a course in algorithms such as CS161 is particularly useful, otherwise it may be tough to follow the material. There is no textbook for the course, but we will catalogue some reading material found on the web as we go, and we will have lecture notes.

Raphael Goldsworthy is the Managing Director of Better Boards Australasia, convenor of the largest annual gathering of Not-For-Profit (NFP) and For-Purpose directors in the Southern Hemisphere, the Better Boards Conference. Raphael has spent almost 10 years working closely with, and curating educational programs for, NFP directors, boards and executives. Raphael has a deep interest in the intersection of technology, decision making, investment and behavioural economics. He regularly writes and speaks on technology in the boardroom, decision making and biases, governance, leadership and related NFP matters.

March 6, 2017 Written by: Susan C. Daffron If you've had to deal with managing streaming data, maybe you've heard of PubNub. Now it's easy to add Watson-powered machine intelligence to those streams with BLOCKS, a feature of the PubNub Data Stream Network (DSN) that makes the network programmable. Using BLOCKS, developers can easily deploy functions on the PubNub network to modify messages without the need to manage their own infrastructure. In a new episode of the Building with Watson webinar series, Josh Marinacci, Head of Developer Relations at PubNub demonstrates how he used the Watson Conversation PubNub BLOCK to build a geology-themed chatbot called Mr. Rockbot. When you're building a chatbot, you need to remember that a chatbot involves constant communication between the user and your bot. To tie these elements together, you'll need a real-time, low-latency and high security infrastructure.

There didn't seem to be anything strange about the new teaching assistant, Jill Watson, who messaged students about assignments and due dates in professor Ashok Goel's artificial intelligence class at the Georgia Institute of Technology. Her responses were brief but informative, and it wasn't until the semester ended that the students learned Jill wasn't actually a "she" at all, let alone a human being. Jill was a chatbot, built by Goel to help lighten the load on his eight other human TAs. "We thought that if an AI TA would automatically answer routine questions that typically have crisp answers, then the (human) teaching staff could engage the students on the more open-ended questions," Goel told Digital Trends. "It is only later that we became motivated by the goal of building human-like AI TAs so that the students cannot easily tell the difference between human and AI TAs. Now we are interested in building AI TAs that enhance student engagement, retention, performance, and learning."

As more companies begin to experiment with and deploy machine learning in different settings, it's good to look ahead at what future systems might look like. Today, the typical sequence is to gather data, learn some underlying structure, and deploy an algorithm that systematically captures what you've learned. Gathering, preparing, and enriching the right data--particularly training data--is essential and remains a key bottleneck among companies wanting to use machine learning. I take for granted that future AI systems will rely on continuous learning as opposed to algorithms that are trained offline. Humans learn this way, and AI systems will increasingly have the capacity to do the same.

Hi! I'm Jose Portilla and I'm an instructor on Udemy with over 250,000 students enrolled across various courses on Python for Data Science and Machine Learning, R Programming for Data Science, Python for Big Data, and many more. What should I do to become a data scientist? In this post, I'll try my best to help answer this question and point to resources that can help guide you to an answer, also hopefully this post serves as something I can quickly link to my students:) I've broken down the steps into some key topics and discussed helpful details for each. "The secret of getting ahead is getting started." If you are interested in becoming a data scientist the best advice is to begin preparing for your journey now!

When I was studying political science in college, I had no intention of going into the field of technology. I had friends in STEM, but I was sure I either wanted to pursue a career in politics or business. However, when I saw an opportunity to enter a rotational program at IBM Watson starting in the summer of 2014, I knew I had to pursue it. I got the job and rotated through the sales and marketing departments, where I began learning more about AI technology. As I talked to developers both inside and outside of the company, I found myself wanting to learn how to code with the Watson API's and create a new product or app.

Tree based learning algorithms are considered to be one of the best and mostly used supervised learning methods. Tree based methods empower predictive models with high accuracy, stability and ease of interpretation. Unlike linear models, they map non-linear relationships quite well. They are adaptable at solving any kind of problem at hand (classification or regression). Methods like decision trees, random forest, gradient boosting are being popularly used in all kinds of data science problems. Hence, for every analyst (fresher also), it's important to learn these algorithms and use them for modeling. This tutorial is meant to help beginners learn tree based modeling from scratch. After the successful completion of this tutorial, one is expected to become proficient at using tree based algorithms and build predictive models. Note: This tutorial requires no prior knowledge of machine learning.