October 06, 2020 at 10:00PM - The Complete Machine Learning A to Z Bundle (96% discount) Ashraf

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Chatbots are voice-aware bots, i.e. computer programs designed to simulate human conversations with users. Chatbots have become ubiquitous across sites and apps and a multitude of AI platforms exist which help you get up and running with a chatbot quickly. This course introduces DialogFlow, a conversational interface for bots, devices and applications. It’s Google’s bot technology and a direct rival of Amazon Lex.

• Access 35 lectures & 4 hours of content 24/7
• Discuss voice & text interfaces and current trends in human-computer interaction
• Explore interaction models such as intents, entities, contexts & their resolution into API calls
• Manage the flow of conversations using linear & non-linear dialogs
• Use webhooks to fulfill user intents & learn how to connect to external services to respond to queries
• Deploy a flask app to Heroku
• Understand how a chatbot can be added to your Slack workspace

Chatbots are a hot new technology. They’re a great way to convey complex information to your customers in a free-flowing, conversational way. Amazon Lex, an AWS service for building conversational interfaces for any application using voice and text, is one of the leading ways to build them. Here, you’ll learn how to do it!

• Access 43 lectures & 4 hours of content 24/7
• Discuss voice & text interfaces and current trends in human-computer interaction
• Understand how Alexa, Lex, Echo & the other bits of the Amazon ecosystem come together
• Explore interaction models such as utterances, intents, slots, prompts & their resolution into API calls
• Use AWS Lambdas to fulfill user intents, & learn how AWS lambdas provide smooth, no-ops, auto-scaling code endpoints
• Discover how a chatbot can be added to your Slack workspace

Alexa, Siri, Cortana and Google Now — voice-activated personal assistants are one of the hottest trends in technology these days. They are a great way to convey complex information to your customers in a free-flowing, conversational way. Alexa is a great way to build them — an AWS service for building conversational interfaces for Echo, FireTV and a host of Alexa-aware devices. In this course, you’ll learn how to start building apps for use with Alexa.

• Access 37 lectures & 2 hours of content 24/7
• Cover voice & text interfaces and current trends in human-computer interaction
• Discover how Alexa, Lex, Echo, & other bits of the Amazon ecosystem come together
• Explore interaction models like utterances, intents, slots, prompts, & their resolution into API calls
• Learn about fulfillment models

Deep learning isn’t just about helping computers learn from data—it’s about helping those machines determine what’s important in those datasets. This is what allows for Tesla’s Model S to drive on its own and for Siri to determine where the best brunch spots are. Using the machine learning workhorse that is TensorFlow, this course will show you how to build deep learning models and explore advanced AI capabilities with neural networks.

• Access 73 lectures & 8 hours of content 24/7
• Understand the anatomy of a TensorFlow program & basic constructs such as graphs, tensors, and constants
• Create regression models w/ TensorFlow
• Learn how to streamline building & evaluating models w/ TensorFlow’s estimator API
• Use deep neural networks to build classification & regression models

Classification models play a key role in helping computers accurately predict outcomes, like when a banking program identifies loan applicants as low, medium, or high credit risks. This course offers an overview of machine learning with a focus on implementing classification models via Python’s scikit-learn. If you’re an aspiring developer or data scientist looking to take your machine learning knowledge further, this course is for you.

• Access 18 lectures & 2 hours of content 24/7
• Tackle basic machine learning concepts, including supervised & unsupervised learning, regression, and classification
• Learn about support vector machines, decision trees & random forests using real data sets
• Discover how to use decision trees to get better results

Fast, scalable, and packed with an intuitive API for machine learning, Apache MXNet is a deep learning framework that makes it easy to build machine learning applications that learn quickly and can run on a variety of devices. This course walks you through the Apache MXNet essentials so you can start creating your own neural networks, the building blocks that allow AI to learn on their own.

• Access 31 lectures & 2 hours of content 24/7
• Explore neurons & neural networks and how they factor into machine learning
• Walk through the basic steps of training a neural network
• Dive into building neural networks for classifying images & voices
• Refine your training w/ real-world examples & datasets

More companies are using the power of deep learning and neural networks to create advanced AI that learns on its own. From speech recognition software to recommendation systems, deep learning frameworks, like PyTorch, make creating these products easier. Jump in, and you’ll get up to speed with PyTorch and its capabilities as you analyze a host of real-world datasets and build your own machine learning models.

• Access 41 lectures & 3 hours of content 24/7
• Understand neurons & neural networks and how they factor into machine learning
• Explore the basic steps involved in training a neural network
• Familiarize yourself w/ PyTorch & Python 3
• Analyze air quality data, salary data & more real-world datasets

In addition to handling vast amounts of batch data, Spark has extremely powerful support for continuous applications, or those with streaming data that is constantly updated and changes in real-time. Using the new and improved Spark 2.x, this course offers a deep dive into stream architectures and analyzing continuous data. You’ll also follow along a number of real-world examples, like analyzing data from restaurants listed on Zomato and real-time Twitter data.

• Access 36 lectures & 2 hours of content 24/7
• Familiarize yourself w/ Spark 2.x & its support for continuous applications
• Learn how to analyze data from real-world streams
• Analyze data from restaurants listed on Zomato & real-time Twitter data

One of the most popular data analytics engines out there, Spark has become a staple in many a data scientist’s toolbox; and the latest version, Spark 2.x, brings more efficient and intuitive features to the table. Jump into this comprehensive course, and you’ll learn how to better analyze mounds of data, extract valuable insights, and more with Spark 2.x. Plus, this course comes loaded with hands-on examples to refine your knowledge, as you analyze data from restaurants listed on Zomato and churn through historical data from the Olympics and the FIFA world cup!

• Access 27 lectures & 3 hours of content 24/7
• Explore what Spark 2.x can do via hands-on projects
• Learn how to analyze data at scale & extract insights w/ Spark transformations and actions
• Deepen your understanding of data frames & Resilient Distributed Datasets

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