New Courses: Building Chatbots & Parallel Computing

We just launched 2 new courses, Building Chatbots in Python & Parallel Computing with Dask! Learn how to build a sophisticated chatbot in Python & how to use Daska, a flexible parallel computing library for analytic computing  ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ [DataCamp]( New Courses! [ ]( [Play Now]( [ ]( Building Chatbots in Python Alan Nichol, Co-founder and CTO of Rasa How do you turn human language into machine instructions? In this course, you'll tackle this first with rule-based systems and then with machine learning. Some chat systems are designed to be useful, while others are just good fun. You will build one of each, and finally put everything together to make a helpful, friendly chatbot! [Start Learning]( [Parallel Computing with Dask]( Parallel Computing with Dask Dhavide Aruliah & Matthew Rocklin This course, built in partnership with Anaconda, will introduce you to Dask, a flexible parallel computing library for analytic computing. With Dask, you will be able to take the Python workflows you currently have and easily scale them up to large datasets without the need to migrate to a distributed computing environment. [Start Learning]( Building Chatbots in Python: What You'll Learn Chapter 1: [Chatbots 101]( In this chapter, you'll learn how to build your first chatbot! You'll set up a basic structure for receiving text and responding to users, and then learn how to add the basic elements of personality. Chapter 2: [Understanding natural language]( Here, you'll use machine learning to turn natural language into structured data. You'll start with a refresher on the theoretical foundations, and then move on to building models using the ATIS dataset. Chapter 3: [Building a virtual assistant]( In this chapter, you're going to build a personal assistant to help you plan a trip. It will be able to respond to questions by looking inside a database for matching results. Chapter 4: [Dialogue]( Everything you've built so far has statelessly mapped intents to actions & responses. But here you will add some statefulness to build a more sophisticated bot that helps users order coffee. Have fun! [Play Now]( Parallel Computing with Dask: What You'll Learn Chapter 1: [Working with Big Data]( In this chapter, you'll learn how to leverage traditional Python techniques for processing large datasets. Followed by learning how the Dask library can be used to execute a pipeline of Python functions in parallel with processing large amounts of data. Chapter 2: [Working with Dask Arrays]( In this chapter, you'll explore how you can use dask.array to read multiple data sources and perform computations with them as a single data array. You'll also examine climate patterns in the US from monthly weather data in the US. Chapter 3: [Working with Dask DataFrames]( In this chapter you'll learn how to build a pipeline of delayed computation with Dask DataFrame, and you'll use these skills to study how much NYC taxi riders tip their drivers. Chapter 4: [Working with Dask Bags for Unstructured Data]( In this chapter, you'll use the Dask Bag to read raw text files and perform simple text processing workflows over large datasets in parallel. Chapter 5: [Case Study: Analyzing Flight Delays]( Here, you'll put your new skills together to search for correlations between flight delays and reported weather events at selected airports. [Play Now]( [DataCamp] [DataCamp]( DataCamp Inc. | 350 Fifth Avenue | Suite 7730 | New York, NY 10118 [Facebook] [Facebook]( [Twitter] [Twitter]( [LinkedIn] [LinkedIn]( [YouTube] [YouTube]( [Unsubscribe](