Artificial Intelligence (AI): Artificial Intelligence Beginner

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  • 8 Books | 78h 52m
  • Includes Lab
  • 11 Courses | 9h 36m 31s
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  • 7 Books | 18h 18m
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Artificial intelligence is the ability of machines to mimic human intelligence to reach solutions. Explore AI and its uses.

GETTING STARTED

Python AI Development: Introduction

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    Course Overview
    2m 23s
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    Overview of Python
    3m 24s
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Introduction to Artificial Intelligence

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    What Is Artificial Intelligence?
    5m 54s
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    Fields and Applications
    6m 28s
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OpenCV: Introduction

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    Course Overview
    2m 16s
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    Installing OpenCV
    5m 10s
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Understanding Bots: Chatbot Architecture

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    Course Overview
    1m 29s
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    Chatbot Use Cases
    4m 18s
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Artificial Intelligence: Basic AI Theory

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    Course Overview
    2m 13s
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    What Is Artificial Intelligence?
    3m 30s
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AI Fundamentals

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    Machine Learning and Artificial Intelligence Goals
    3m 50s
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    Essential Features of Artificial Intelligence
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OpenCV: Manipulating Images

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    Course Overview
    2m 25s
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    Performing Bitwise Operations on Images in OpenCV
    10m 50s
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Artificial Intelligence: Human-computer Interaction Overview

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    Course Overview
    2m 9s
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    HCI Introduction
    3m 18s
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AI in Industry

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    Course Overview
    1m 35s
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    AI in Industry
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TensorFlow: Introduction to Machine Learning

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    Course Overview
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    Introduction to Machine Learning Algorithms
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COURSES INCLUDED

Python AI Development: Introduction
Python is one of the most popular programming languages and programming AI in this language has many advantages. In this course, you'll learn about the differences between Python and other programming languages used for AI, Python's role in the industry, and cases where using Python can be beneficial. You'll also examine multiple Python tools, libraries, and use environments and recognize the direction in which this language is developing.
16 videos | 46m has Assessment available Badge
Python AI Development: Practice
In this course, you'll learn about development of AI with Python, starting with simple projects and ending with comprehensive systems. You'll examine various Python environments and ways to set them up and begin coding, leaving you with everything you need to begin building your own AI solutions in Python.
14 videos | 1h 26m has Assessment available Badge

COURSES INCLUDED

Introduction to Artificial Intelligence
The world of artificial intelligence (AI) includes many areas in computing, which makes it a complex field. Explore AI applications, techniques, environments, and behaviors.
11 videos | 44m has Assessment available Badge
Search Problems
Many problems faced by intelligent agents can be solved using searching methods. Explore search problems and useful methods to solve these problems.
13 videos | 43m has Assessment available Badge
Constraint Satisfaction Problems
Search algorithms provide solutions for many problems, but they aren't always the optimal solution. Discover how constraint satisfaction algorithms are better than search algorithms in some cases, and how to use them.
10 videos | 26m has Assessment available Badge
Adversarial Problems
Many problems occur in environments with more than one agent, such as games. Explore techniques used to solve adversarial problems to make agents play games, like chess.
12 videos | 39m has Assessment available Badge
Uncertainty
Many problems aren't fully observable and have some degree of uncertainty, which is challenging for AI to solve. Discover how to make agents deal with uncertainty and make the best decisions.
13 videos | 45m has Assessment available Badge
Machine Learning
Sometimes agents must learn how to associate certain conditions with actions and outcomes. Explore the principles of machine learning and how to use it to make smarter agents.
14 videos | 47m has Assessment available Badge
Reinforcement Learning
Some problems are too complicated to describe to a computer and to solve with traditional algorithms, which is why reinforcement learning is useful. Explore the fundamentals of reinforcement learning.
13 videos | 32m has Assessment available Badge
Introducing Natural Language Processing
Natural language is essential to human communication, which makes the ability to process it an important one for computers. Explore natural language processing and some of the basic tasks.
13 videos | 41m has Assessment available Badge
Planning AI Implementation
This 13-video course explores how artificial intelligence (AI) can be leveraged, how to plan an AI implementation from setup to architecture, and the issues surrounding incorporating it into an enterprise for machine learning. Learners will explore the three legs of AI: how it applies intelligence-like behavior to machines. You will then examine how machine learning adds to this intelligence-like behavior, and the next generation with deep learning. This course discusses strategies for implementation of AI, organizational challenges surrounding the adoption of AI, and the need for training of both personnel and machines. Next, learn the role of data and algorithms in AI implementation. Learners continue by examining several ways in which an organization can plan and develop AI capability; the elements organizations need to understand how to assess AI needs and tools; management challenges; and the impact on personnel. You will learn about pitfalls in using AI, and what to avoid. Finally, you will learn about data issues, data quality, training concepts, overfitting, and bias.
13 videos | 49m has Assessment available Badge
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COURSES INCLUDED

OpenCV: Introduction
A cross-platform library, OpenCV facilitates image processing and analysis. In this course, you'll discover fundamental concepts related to computer vision and the basic operations which can be performed on images using OpenCV. You'll begin by outlining how to read images from your file system into your Python source in the form of arrays and then save an image array into a local file. Next, you'll explore color images represented as a combination of blue, green, and red channels, how to convert color images to grayscale, and how grayscale images are defined. Finally, you'll perform basic operations on images by investigating how to combine two images using an add operation and make one of the added images more prominent than the other using a weighted addition. Conversely, you'll also perform a subtract operation using two images.
9 videos | 1h 10m has Assessment available Badge

COURSES INCLUDED

Understanding Bots: Chatbot Architecture
In this course, participants will examine chatbot use cases, the technology stack, and popular development and deployment tools with Amazon's Alexa on Amazon Web Services (AWS) and Google's Dialogflow. First, you will learn about chatbots and in what categories they are used and the different classifications of chatbots. You will explore the different technologies orchestrated to create chatbots. Look at conversation flow and learn about the conversational flow of the typical chatbot/human interface. Then examine Dialogflow building blocks and the elemental building blocks for a typical chatbot built with AWS Alexa Skills Kit. Next, you will set up the AWS developer account required for Alexa Skills development and use the account and an AWS Lambda service to develop Alexa Skills. Then explore the components of the Alexa Development Console. Learn how to configure an AWS Lambda function. After setting up a developer account on Google's Dialogflow, you will look into the Dialogflow developer console and its components. In a closing exercise, you will practice what you learned about chatbots and their architecture.
14 videos | 1h 2m has Assessment available Badge
Understanding Bots: Building Bots with Dialogflow
In this course, participants explore the development of chatbots with one of the main chatbot development frameworks, Google's Dialogflow Developer Console. Start by creating an agent for a chatbot and exploring default intents in Dialogflow. Intents map what a user says to what the bot should do. You will then create custom intents in Dialogflow. Participants then examine the important differences between developer and system entities in Dialogflow. Next, you will generate developer entities to extract information from user conversations in Dialogflow. Learn how to generate training phases, which are user expressions that a user might say when they want to invoke an intent. You will then work with the actions and parameters associated with each intent. Learn how to write static responses, which a bot can respond to a user with in Dialogflow. Enable the Small Talk feature for a chatbot and test its functionality in Dialogflow. Then learn how to write inline cloud functions to satisfy a fulfillment in Dialogflow. A concluding exercise deals with creating a chatbox in Dialogflow.
13 videos | 1h 1m has Assessment available Badge
Understanding Bots: Chatbot Advanced Concepts and Features
In this course, explore the advanced concepts and features for developing and deploying chatbots, working with contexts, integrating with alternate platforms, and deploying fulfillments. Begin by looking at linear and nonlinear human/chatbot conversations. Next, work with input and output contexts. Contexts represent the current state of a user's request in a dialogue. Move on to follow-up intents, which allow you to easily shape a conversation without needing to create and manage contexts manually. Create the entry point for a nonlinear conversation by using contexts, then carry those contexts on a chatbot dialog to produce nonlinear conversations. Explore how to integrate Dialogflow chatbots with other platforms and deploy a fulfillment in Dialogflow. Access and use Actions on Google in Dialogflow and test a chatbot by using Google Assistant. Integrate Dialogflow chatbots with Google Assistant. Learn about Chatfuel building blocks, examining the use of prebuilt flows and text and typing elements, quick reply images and send blocks in Chatfuel. In the closing exercise, describe chatbot linear and nonlinear conversations and build a basic chatbot with Chatfuel.
16 videos | 1h 30m has Assessment available Badge
Understanding Bots: Amazon Alexa Skills Development
In this course, participants examine the Amazon Web Services (AWS) Alexa Skills Kit, including the use of invocations, intents, utterances, and slots. Testing with Alexa Simulator and Echosim is also covered. Begin by creating a skill in Alexa Development Console and looking at the use of invocations with the Alexa skill. Then discover how built-in intents are used in Alexa Development Console. Next, create and use custom intents, utterances, and slots in Alexa Development Console. To review: an intent is a construct representing an action that fulfills a spoken request, utterances are related spoken phrases mapped to the intent, while slots are optional arguments also related to intent. You will learn how to build a Lambda function and integrate it with an Alexa skill, then test a skill by using Alexa Simulator and Echosim. You will configure a skill to use DynamoDB for persisting session data. Finally, create an Alexa skill that manages a multistage conversation. The concluding exercise directs you to create a skill by using the Skills Kit in the Alexa Development Console.
13 videos | 1h 11m has Assessment available Badge
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COURSES INCLUDED

Artificial Intelligence: Basic AI Theory
Artificial intelligence (AI) is transforming the way businesses and governments are developing and using information. This course offers an overview of AI, its history, and its use in real-world situations; prior knowledge of machine learning, neural network, and probabilistic approaches is recommended. There are multiple definitions of AI, but the most common view is that it is software which enables a machine to think and act like a human, and to think and act rationally. Because AI differs from plain programing, the programming language used will depend on the application. In this series of videos, you will be introduced to multiple tools and techniques used in AI development. Also discussed are important issues in its application, such as the ethics and reliability of its use. You will set up a programing environment for developing AI applications and learn the best approaches to developing AI, as well as common mistakes. Gain the ability to communicate the value AI can bring to businesses today, along with multiple areas where AI is already being used.
14 videos | 1h 10m has Assessment available Badge
Artificial Intelligence: Types of Artificial Intelligence
This course covers simple and complex types of AI (artificial intelligence) available in today’s market. In it, you will explore theories of mind research, self-aware AI, artificial narrow intelligence, artificial general intelligence, and artificial super intelligence. First, learn the ways in which AI is used today in agriculture, medicine, by the military, in financial services, and by governments. As a special field of computer science that uses mathematics, statistics, cognitive and behavioral sciences, AI uses unique applications to perform actions based on data it uses as an input, and does so by mimicking the activity within the human brain. No data can be 100 percent accurate, bringing a certain degree of uncertainty to any kind of AI application. So this course seeks to explain how and why AI needs to be developed for a particular use scenario, helping you understand the many aspects involved in AI programming and how AI performance needs to be good enough to complete a certain task.
14 videos | 53m has Assessment available Badge

COURSES INCLUDED

AI Fundamentals
Discover the fundamental concepts of the technologies driving artificial Intelligence (AI).
10 videos | 1h 8m has Assessment available Badge
Machine Learning Implementation
Explore the various machine learning techniques and implementations using Java libraries, and learn to identify certain scenarios where you can implement algorithms.
12 videos | 1h 32m has Assessment available Badge
Neural Network & Neuroph Framework
Discover the essential features and capabilities of Neuroph framework and Neural Networks, and also how to work with and implement Neural Networks using Neuroph framework.
16 videos | 1h 55m has Assessment available Badge
Neural Network & NLP Implementation
Discover how to implement advanced neural network using DL4j and explore the concept of NLP and its implementation using OpenNLP Java library.
11 videos | 1h 1m has Assessment available Badge
Expert Systems & Reinforcement Learning
Explore the concepts of expert system along with its Implementation using Java based frameworks, and examine the implementation and usages of ND4J and Arbiter to facilitate optimization.
12 videos | 52m has Assessment available Badge
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COURSES INCLUDED

OpenCV: Manipulating Images
Images often require to be manipulated to extract meaningful portions of an image or prepare them for a machine learning pipeline. OpenCV can help with this. In this course, you'll investigate a variety of image manipulation operations using OpenCV. You'll begin by recognizing how to filter certain portions of an image using bitwise operations. Next, you'll explore the concept of masks and how to use them while extracting parts of an image. You'll then outline how to apply geometrical operations by resizing an image to specific dimensions and discover challenges that such operations present. You'll finish the course by examining image transformations such as rotations and translations to help orient an image to your requirements. Finally, you'll discover how to flip and warp images to present them from a different perspective.
10 videos | 1h 24m has Assessment available Badge
OpenCV: Advanced Image Operations
Many image processing operations involve complex math, but when using OpenCV, much of that is abstracted from the developer. In this course, you'll gain a high-level understanding of advanced image operations in OpenCV. You'll begin by recognizing how to apply different blur operations to an image. These range from simple blurs to Gaussian and median blurs. While doing so, you'll examine their specific advantages and disadvantages and how to distinguish between them. Moving on, you'll outline how to highlight objects in an image using edge detection and augment images by adding shapes and objects to them. Finally, you'll discover how to work with pre-trained classifiers to detect people in an image and perform morphological transformations to emphasize or suppress specific parts of an image.
9 videos | 1h 12m has Assessment available Badge

COURSES INCLUDED

Artificial Intelligence: Human-computer Interaction Overview
In developing AI (artificial intelligence) applications, it is important to play close attention to human-computer interaction (HCI) and design each application for specific users. To make a machine intelligent, a developer uses multiple techniques from an AI toolbox; these tools are actually mathematical algorithms that can demonstrate intelligent behavior. The course examines the following categories of AI development: algorithms, machine learning, probabilistic modelling, neural networks, and reinforcement learning. There are two main types of AI tools available: statistical learning, in which large amount of data is used to make certain generalizations that can be applied to new data; and symbolic AI, in which an AI developer must create a model of the environment with which the AI agent interacts and set up the rules. Learn to identify potential AI users, the context of using the applications, and how to create user tasks and interface mock-ups.
14 videos | 1h 1m has Assessment available Badge
Artificial Intelligence: Human-computer Interaction Methodologies
Human computer interaction (HCI) design is the starting point for an artificial intelligence (AI) program. Overall HCI design is a creative problem-solving process oriented to the goal of satisfying largest number of customers. In this course, you will cover multiple methodologies used in the HCI design process and explore prototyping and useful techniques for software development and maintenance. First, learn how the anthropomorphic approach to HCI focuses on keeping the interaction with computers similar to human interactions. The cognitive approach pays attention to the capacities of a human brain. Next, learn to use the empirical approach to HCI to quantitatively evaluate interaction and interface designs, and predictive modeling is used to optimize the screen space and make interaction with the software more intuitive. You will examine how to continually improve HCI designs, develop personas, and use case studies and conduct usability tests. Last, you will examine how to improve the program design continually for AI applications; develop personas; use case studies; and conduct usability tests.
14 videos | 1h 2m has Assessment available Badge
Computer Vision: Introduction
In this course, you'll explore basic Computer Vision concepts and its various applications. You'll examine traditional ways of approaching vision problems and how AI has evolved the field. Next, you'll look at the different kinds of problems AI can solve in vision. You'll explore various use cases in the fields of healthcare, banking, retail cybersecurity, agriculture, and manufacturing. Finally, you'll learn about different tools that are available in CV.
15 videos | 45m has Assessment available Badge
Computer Vision: AI & Computer Vision
In this course, you'll explore Computer Vision use cases in fields like consumer electronics, aerospace, automotive, robotics, and space. You'll learn about basic AI algorithms that can help you solve vision problems and explore their categories. Finally, you'll apply hands-on development practices on two interesting use cases to predict lung cancer and deforestation.
15 videos | 50m has Assessment available Badge
Cognitive Models: Overview of Cognitive Models
To implement cognitive modeling inside AI systems, a developer needs to understand the major differences between commonly used cognitive models and their best qualities. Today cognitive models are actively utilized in healthcare, neuroscience, manufacturing and psychology and their importance compared to other AI approaches is expected to rise. Developing a firm understanding of cognitive modeling and its use cases is essential to anyone involved in creating AI systems. In this course, you'll identify unique features of cognitive models, which help create even more intelligent software systems. First you will learn about the different types of cognitive models and the disciplines involved in cognitive modeling. Further, you will discover main use cases for cognitive models in the modern world and learn about the history of cognitive modeling and how it is related to computer science and AI.
14 videos | 42m has Assessment available Badge
Cognitive Models: Approaches to Cognitive Learning
Practice plays an important role in AI development and helps one get familiarized with commonly used tools and frameworks. Knowing which methods to apply and when is critical to completing projects quickly and efficiently. Based on code examples provided, you will be able to quickly learn important cognitive modeling libraries and apply this knowledge to new projects in the field. In this course, you'll learn the essentials of working with cognitive models in a software system. First, you will get a detailed overview of each type of learning used in cognitive modeling. Further, you will learn about the toolset used for cognitive modeling with Python and recall which role cognitive models play in AI and business. Finally, you will go through various cognitive model implementations to develop skills necessary to implement cognitive modeling in real world.
13 videos | 48m has Assessment available Badge
Elements of an Artificial Intelligence Architect
An Artificial Intelligence (AI) Architect works and interacts with various groups in an organization, including IT Architects and IT Developers. It is important to differentiate between the work activities performed by these groups and how they work together. This course will introduce you to the AI Architect role. You’ll discover what the role is, why it's important, and who the architect interacts with on a daily basis. We will also examine and categorize their daily work activities and will compare those activities with those of an IT Architect and an IT Developer. The AI Architect helps many groups within the organization, and we will examine their activities within those groups as well. Finally, we will highlight the roles the AI Architect plays in the organizations which they are a member of.
7 videos | 28m has Assessment available Badge
AI Enterprise Planning
In this course, you'll be introduced to the concepts, methodologies, and tools required for effectively and efficiently incorporating AI into your IT enterprise planning. You'll look at enterprise planning from an AI perspective, and view projects in tactical/strategic and current, intermediate, or future state contexts. You'll explore how to use an AI Maturity Model to conduct an AI Maturity Assessment of the current and future states of AI planning, and how to conduct a gap analysis between those states. Next, you'll learn about the components of a discovery map, project complexity, and a variety of graphs and tables that enable you to handle complexity. You'll see how complexity can be significantly reduced using AI accelerators and how they affect specific phases of the AI development lifecycle. You'll move on to examine how to create an AI enterprise roadmap using all of the artifacts just described, plus a KPIs/Value Metrics table, and how both of these can be used as inputs to an analytics dashboard. Finally, you'll explore numerous examples of AI applications of different types in diverse business areas.
12 videos | 1h 12m has Assessment available Badge
Explainable AI
The inner workings of many deep learning systems are complicated, if not impossible, for the human mind to comprehend. Explainable Artificial Intelligence (XAI) aims to provide AI experts with transparency into these systems. In this course, you'll describe what Explainable AI is, how to use it, and the data structures behind XAI's preferred algorithms. Next, you'll explore the interpretability problem and today's state-of-the-art solutions to it. You'll identify XAI regulations, define the "right to explanation", and illustrate real-world examples where this has been applicable. You'll move on to recognize both the Counterfactual and Axiomatic methods, distinguishing their pros and cons. You'll investigate the intelligible models method, along with the concepts of monotonicity and rationalization. Finally, you'll learn how to use a Generative Adversarial Network.
11 videos | 46m has Assessment available Badge
Working with Google BERT: Elements of BERT
Adopting the foundational techniques of natural language processing (NLP), together with the Bidirectional Encoder Representations from Transformers (BERT) technique developed by Google, allows developers to integrate NLP pipelines into their projects efficiently and without the need for large-scale data collection and processing. In this course, you'll explore the concepts and techniques that pave the foundation for working with Google BERT. You'll start by examining various aspects of NLP techniques useful in developing advanced NLP pipelines, namely, those related to supervised and unsupervised learning, language models, transfer learning, and transformer models. You'll then identify how BERT relates to NLP, its architecture and variants, and some real-world applications of this technique. Finally, you'll work with BERT and both Amazon review and Twitter datasets to develop sentiment predictors and create classifiers.
15 videos | 1h has Assessment available Badge
Evaluating Current and Future AI Technologies and Frameworks
Solid knowledge of the AI technology landscape is fundamental in choosing the right tools to use as an AI Architect. In this course, you'll explore the current and future AI technology landscape, comparing the advantages and disadvantages of common AI platforms and frameworks. You'll move on to examine AI libraries and pre-trained models, distinguishing their advantages and disadvantages. You'll then classify AI datasets and see a list of dataset topics. Finally, You'll learn how to make informed decisions about which AI technology is best suited to your projects.
13 videos | 45m has Assessment available Badge
Leveraging Reusable AI Architecture Patterns
AI architecture patterns, some of which have been known for many years, have been formally identified as such only in the last couple of years. In this course, you'll identify 12 reusable, standard AI architecture patterns, and 3 AI architecture anti-patterns frequently used to architect common AI applications. You'll learn to differentiate between architecture and design patterns and explore how they're used. Next, you'll examine the structure of an AI architecture pattern, and that of an anti-pattern and its different parts. You'll identify when specific patterns should or can be used, when they need to be avoided, and how to avoid using anti-patterns. You will also learn that even good patterns can become anti-patterns when applied to solve a problem they were not intended for.
14 videos | 55m has Assessment available Badge
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AI in Industry
Designing successful and competitive AI products involves thorough research on its existing application in various markets. Most large scale businesses use AI in their workflows to optimize business operations. AI Architects should be aware of all possible applications of AI so they can look at market trends and come up with the most appropriate, novel, and useful AI solutions for their industry. In this course, you'll explore examples of standard AI applications in various industries like Finance, Marketing, Sales, Manufacturing, Transportation, Cybersecurity, Pharmaceutical, and Telecommunications. You'll examine how AI is utilized by leading AI companies within each of these industries. You'll identify which AI technologies are common across all industries and which are industry-specific. Finally, you'll recognize why AI is imperative to the successful operation of many industries.
12 videos | 45m has Assessment available Badge
The AI Practitioner: Role & Responsibilities
AI Practitioner is a cross-industry advanced AI Developer position that has a growing demand in the modern world. Candidates for this role need to demonstrate proficiency in optimizing and tuning AI solutions to deliver the best possible performance in the real world. AI Practitioners require more advanced knowledge of algorithm implementations and should have a firm knowledge of latest toolsets available. In this course, you'll be introduced to the AI Practitioner role in the industry. You'll examine an AI Practitioner's skillset and responsibilities in relation to AI Developers, Data Scientists, and ML and AI Engineers. Finally, you'll learn about the scope of work for AI Practitioners, including their career opportunities and pathways.
14 videos | 52m has Assessment available Badge
The AI Practitioner: Optimizing AI Solutions
Optimization is required for any AI model to deliver reliable outcomes in most of the use cases. AI Practitioners use their knowledge of optimization techniques to choose and apply various solutions and improve accuracy of existing models. In this course, you'll learn about advanced optimization techniques for AI Development, including multiple optimization approaches like Gradient Descent, Momentum, Adam, AdaGrad and RMSprop optimization. You'll examine how to determine the preferred optimization technique to use and the overall benefits of optimization in AI. Lastly, you'll have a chance to practice implementing optimization techniques from scratch and applying them to real AI models.
14 videos | 44m has Assessment available Badge
AI Framework Overview: AI Developer Role
Any aspiring AI developer has to clearly understand the responsibilities and expectations when entering the industry in this role. AI Developers can come from various backgrounds, but there are clear distinctions between this role and others like Software Engineer, ML Engineer, Data Scientist, or AI Engineers. Therefore, any AI Developer candidate has to posses the required knowledge and demonstrate proficiency in certain areas. In this course you will learn about the AI Developer role in the industry and compare the responsibilities of AI Developers with other engineers involved in AI development. After completing the course, you will recognize the mindset required to become a successful AI Developer and become aware of multiple paths for career progression and future opportunities
14 videos | 44m has Assessment available Badge
AI Framework Overview: Development Frameworks
A working knowledge of multiple AI development frameworks is essential to AI developers. Depending on the particular focus, you may decide on a particular framework of your choice. However, various companies in the industry tend to use different frameworks in their products, so knowing the basics of each framework is quite helpful to the aspiring AI Developer. In this course you will explore popular AI frameworks and identify key features and use cases. You will identify main differences between AI frameworks and work with Microsoft CNTK and Amazon SageMaker to implement model flow.
15 videos | 44m has Assessment available Badge
Applying AI to Robotics
Robots can utilize machine learning, deep learning, reinforcement learning, as well as probabilistic techniques to achieve intelligent behavior. This application of AI to robotic systems is found in the automotive, healthcare, logistics, and military industries. With increasing computing power and sophistication in small robots, more industry use cases are likely to emerge, making AI development for robotics a useful AI developer skill. In this course, you'll explore the main concepts, frameworks, and approaches needed to work with robotics and apply AI to robots. You'll examine how AI and robotics are used across multiple industries. You'll learn how to work with commonly used algorithms and strategies to develop simple AI systems that improve the performance of robots. Finally, you'll learn how to control a robot in a simulated environment using deep Q-networks.
17 videos | 1h 4m has Assessment available Badge
Implementing AI Using Cognitive Modeling
Cognitive modeling can provide additional human qualities to AI systems. It is traditionally used in cognitive machines and expert systems. However, with extra computing power, it can be applied to more profound AI approaches like neural networks and reinforcement learning systems. Knowledge of cognitive modeling applications is essential to any AI developer aspiring to design AI architectures and develop large-scale applications. In this course, you'll examine the role of cognitive modeling in AI development and its possible applications in NLP, image recognition, and neural networks. You'll outline core cognitive modeling concepts and significant industry use cases. You'll list open source cognitive modeling frameworks and explore cognitive machines, expert systems, and reinforcement learning in cognitive modeling. Finally, you'll use cognitive models to solve real-world problems.
18 videos | 52m has Assessment available Badge
Using Intelligent Information Systems in AI
The world of technology continues to transform at a rapid pace, with intelligent technology incorporated at every stage of the business process. Intelligent information systems (IIS) reduce the need for routine human labor and allow companies to focus instead on hiring creative professionals. In this course, you'll explore the present and future roles of intelligent informational systems in AI development, recognizing the current demand for IIS specialists. You'll list several possible IIS applications and learn about the roles AI and ML play in creating them. Next, you'll identify significant components of IIS and the purpose of these components. You'll examine how you would go about creating a self-driving vehicle using IIS components. Finally, you'll work with Python libraries to build high-level components of a Markov decision process.
15 videos | 57m has Assessment available Badge
AI Practitioner: BERT Best Practices & Design Considerations
Bidirectional Encoder Representations from Transformers (BERT), a natural language processing technique, takes the capabilities of language AI systems to great heights. Google's BERT reports state-of-the-art performance on several complex tasks in natural language understanding. In this course, you'll examine the fundamentals of traditional NLP and distinguish them from more advanced techniques, like BERT. You'll identify the terms "attention" and "transformer" and how they relate to NLP. You'll then examine a series of real-life applications of BERT, such as in SEO and masking. Next, you'll work with an NLP pipeline utilizing BERT in Python for various tasks, namely, text tokenization and encoding, model definition and training, and data augmentation and prediction. Finally, you'll recognize the benefits of using BERT and TensorFlow together.
17 videos | 1h 5m has Assessment available Badge
AI Practitioner: Practical BERT Examples
Bidirectional Encoder Representations from Transformers (BERT) can be implemented in various ways, and it is up to AI practitioners to decide which one is the best for a particular product. It is also essential to recognize all of BERT's capabilities and its full potential in NLP. In this course, you'll outline the theoretical approaches to several BERT use cases before illustrating how to implement each of them. In full, you'll learn how to use BERT for search engine optimization, sentence prediction, sentence classification, token classification, and question answering, implementing a simple example for each use case discussed. Lastly, you'll examine some fundamental guidelines for using BERT for content optimization.
16 videos | 57m has Assessment available Badge
The AI Practitioner: Tuning AI Solutions
Tuning hyper parameters when developing AI solutions is essential since the same models might behave quite differently with different parameters set. AI Practitioners recognize multiple hyper parameter tuning approaches and are able to quickly determine best set of hyper parameters for particular models using AI toolbox. In this course, you'll learn advanced techniques for hyper parameter tuning for AI development. You'll examine how to recognize the hyper parameters in ML and DL models. You'll learn about multiple hyper parameter tuning approaches and when to use each approach. Finally, you'll have a chance to tune hyper parameters for a real AI project using multiple techniques.
14 videos | 47m has Assessment available Badge
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COURSES INCLUDED

TensorFlow: Introduction to Machine Learning
Explore the concept of machine learning in TensorFlow, including TensorFlow installation and configuration, the use of the TensorFlow computation graph, and working with building blocks.
19 videos | 1h 49m has Assessment available Badge
TensorFlow: Simple Regression & Classification Models
Explore how to how to build and train the two most versatile and ubiquitous types of deep learning models in TensorFlow.
19 videos | 1h 44m has Assessment available Badge
TensorFlow: Deep Neural Networks & Image Classification Using Estimators
Discover how to apply deep learning techniques to images, and how to leverage TensorFlow estimators in building image classification models.
15 videos | 1h 17m has Assessment available Badge
TensorFlow: Convolutional Neural Networks for Image Classification
Examine how to work with Convolutional Neural Networks, and discover how to leverage TensorFlow to build custom CNN models for working with images.
17 videos | 1h 29m has Assessment available Badge
TensorFlow: Word Embeddings & Recurrent Neural Networks
Explore how to model language and text with word embeddings and how to use those embeddings in Recurrent Neural Networks. Leveraging TensorFlow to build custom RNN models is also covered.
11 videos | 47m has Assessment available Badge
TensorFlow: Sentiment Analysis with Recurrent Neural Networks
Discover how to construct neural networks for sentiment analysis. How to generate word embeddings on training data and use pre-trained word vectors for sentiment analysis is also covered.
12 videos | 1h 2m has Assessment available Badge
TensorFlow: K-means Clustering
Discover how to differentiate between supervised and unsupervised machine learning techniques. The construction of clustering models and their application to classification problems is also covered.
15 videos | 1h 5m has Assessment available Badge
TensorFlow: Building Autoencoders
Explore how to perform dimensionality reduction using powerful unsupervised learning techniques such as Principal Components Analysis and autoencoding.
10 videos | 50m has Assessment available Badge
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EARN A DIGITAL BADGE WHEN YOU COMPLETE THESE COURSES

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BOOKS INCLUDED

Book

Practical Python AI Projects: Mathematical Models of Optimization Problems with Google OR-Tools
Written by an industry expert and teacher, this guide is a very practical, hands-on Python book with several projects or case studies to build, and provides real-world templates that you may re-purpose for your own coding projects.
Book Duration 3h 19m Book Authors By Serge Kruk

BOOKS INCLUDED

Book

Artificial Intelligence for Dummies
Making AI more accessible than ever, this hands-on book provides a clear overview of the technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field.
Book Duration 5h 52m Book Authors By John Paul Mueller, Luca Massaron

Book

Foundations of Artificial Intelligence and Expert Systems
Presenting an integrated, real-life case study incorporating all of the related managerial aspects, this book covers all facets of artificial intelligence (AI) and expert systems in a lucid and coherent manner.
Book Duration 3h 58m Book Authors By K Sarukesi, P Gopalakrishnan, V S Janakiraman

Book

Artificial Intelligence Safety and Security
Including contributions from leading scholars in a diverse set of fields, this resource is comprised of chapters addressing different aspects of the AI control problem as it relates to the development of safe and secure artificial intelligence.
Book Duration 14h 43m Book Authors By Roman V. Yampolskiy

Book

Artificial Intelligence and Problem Solving
Offering insight into solving some well-known AI problems using the most efficient problem-solving methods by humans and computers, this book discusses the importance of developing critical-thinking methods and skills, and develops a consistent approach toward each problem.
Book Duration 4h 17m Book Authors By Christopher Pileggi, Danny Kopec, David Ungar, Shweta Shetty

Book

Artificial Intelligence and Security Challenges in Emerging Networks
An essential reference source that discusses applications of artificial intelligence, machine learning, and data mining, this book discusses other tools and strategies to protect networks against security threats and solve security and privacy problems.
Book Duration 5h 7m Book Authors By Ryma Abassi

Book

Beginning Artificial Intelligence with the Raspberry Pi
Providing a gentle introduction to the world of Artificial Intelligence (AI) using the Raspberry Pi as the computing platform, this book explores most of the major AI topics, including expert systems, machine learning both shallow and deep, fuzzy logic control, and more.
Book Duration 4h 28m Book Authors By Donald J. Norris

Book

Artificial Intelligence for .NET: Speech, Language, and Search: Building Smart Applications with Microsoft Cognitive Services APIs
With examples in C# that can easily be applied across a wide range of platforms, including web, desktop, and mobile, this book is your accessible and practical guide to building AI-powered applications using the easy-to-use Cognitive Services APIs from Microsoft.
Book Duration 4h 24m Book Authors By Nishith Pathak
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BOOKS INCLUDED

Book

Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs
Teaching you how to build practical applications of computer vision using the OpenCV library with Python, this book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples.
Book Duration 1h 21m Book Authors By Sunila Gollapudi

Book

Building Computer Vision Applications Using Artificial Neural Networks: With Step-by-Step Examples in OpenCV and TensorFlow with Python
Containing real examples that you can implement and modify to build useful computer vision systems, this book will teach you to apply computer vision and machine learning concepts in developing business and industrial applications using a practical, step-by-step approach.
Book Duration 4h 57m Book Authors By Shamshad Ansari

Book

A Practical Introduction to Computer Vision with OpenCV
A heavily illustrated, practical introduction to an exciting field, this detailed resource explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries.
Book Duration 3h 25m Book Authors By Kenneth Dawson-Howe

BOOKS INCLUDED

Book

Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition with TensorFlow and Keras
Exploring deep learning applications using frameworks such as TensorFlow and Keras, this book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications.
Book Duration 1h 28m Book Authors By Navin Kumar Manaswi

Book

Build Better Chatbots: A Complete Guide to Getting Started with Chatbots
Taking you through the history of chatbots, including when they were invented and how they became popular, this book will show how to build a chatbot for your next project using best practices and focusing on the technological implementation and UX.
Book Duration 1h 27m Book Authors By Anik Das, Rashid Khan

Book

Chatbots for eCommerce: Learn How to Build a Virtual Shopping Assistant
If you are a developer interested in learning how to build your own conversational bot from scratch, this book is for you. Upon completion, you will be able to build a text-based Facebook Messenger bot and a voice-based custom skill for Amazon’s Alexa Voice Service.
Book Duration 1h 13m Book Authors By Amit Kothari, Joshua Hoover, Rania Zyane

Book

Beginning AI Bot Frameworks: Getting Started with Bot Development
Providing a comprehensive look at all the major bot frameworks available, this book will teach you the basics for each framework helping you to get a clear picture for which one is best for your needs.
Book Duration 57m Book Authors By Manisha Biswas

Book

Developing Bots with QnA Maker Service: Integration with Azure Bot Service and Microsoft Bot Framework
Containing real-world examples throughout, this practical book will teach you how to develop bots with zero coding knowledge using the Azure Cognitive QnA Maker service, a GUI cognitive service from Microsoft.
Book Duration 1h 25m Book Authors By Kasam Shaikh

Book

Building Telegram Bots: Develop Bots in 12 Programming Languages Using the Telegram Bot API
Showing how you can use bots for just about everything, this book teaches you about bot programming, using all the latest and greatest programming languages, including Python, Go, and Clojure, so you can feel at ease writing your Telegram bot in a way that suits you.
Book Duration 1h 54m Book Authors By Nicolas Modrzyk

Book

Developing Enterprise Chatbots: Learning Linguistic Structures
Looking at the popular paradigms for chatbot construction, this book will provide a comprehensive source of algorithms and architectures for building chatbots for various domains based on the recent trends in computational linguistics and machine learning.
Book Duration 11h 7m Book Authors By Boris Galitsky
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BOOKS INCLUDED

Book

Artificial Intelligence Basics: A Non-Technical Introduction
This book has arrived to equip you with a fundamental, timely grasp of AI and its impact.
Book Duration 3h 20m Book Authors By Tom Taulli

Book

Artificial Intelligence for Dummies
Making AI more accessible than ever, this hands-on book provides a clear overview of the technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field.
Book Duration 5h 52m Book Authors By John Paul Mueller, Luca Massaron

Book

The AI Advantage: How to Put the Artificial Intelligence Revolution to Work
Cutting through the hype, a practical guide to using artificial intelligence for business benefits and competitive advantage.
Book Duration 4h 11m Book Authors By Thomas H. Davenport

Book

Autonomy and Artificial Intelligence: A Threat or Savior?
Written by world-class researchers and scientists, this book explores how Artificial Intelligence (AI), by leading to an increase in the autonomy of machines and robots, is offering opportunities for an expanded but uncertain impact on society by humans, machines, and robots.
Book Duration 6h 35m Book Authors By Donald Sofge, Ranjeev Mittu, Stephen Russell (eds), W.F. Lawless
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FREE ACCESS

BOOKS INCLUDED

Book

Building Computer Vision Applications Using Artificial Neural Networks: With Step-by-Step Examples in OpenCV and TensorFlow with Python
Containing real examples that you can implement and modify to build useful computer vision systems, this book will teach you to apply computer vision and machine learning concepts in developing business and industrial applications using a practical, step-by-step approach.
Book Duration 4h 57m Book Authors By Shamshad Ansari

Book

Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs
Teaching you how to build practical applications of computer vision using the OpenCV library with Python, this book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples.
Book Duration 1h 21m Book Authors By Sunila Gollapudi

Book

A Practical Introduction to Computer Vision with OpenCV
A heavily illustrated, practical introduction to an exciting field, this detailed resource explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries.
Book Duration 3h 25m Book Authors By Kenneth Dawson-Howe

BOOKS INCLUDED

Book

Artificial Intelligence Basics
Designed as a self-teaching introduction to the fundamental concepts of artificial intelligence, this book’s coverage includes searching processes, knowledge representation, machine learning, expert systems, programming, and robotics.
Book Duration 2h 37m Book Authors By Neeru Gupta, Ramita Mangla

Book

Artificial Intelligence Basics: A Non-Technical Introduction
This book has arrived to equip you with a fundamental, timely grasp of AI and its impact.
Book Duration 3h 20m Book Authors By Tom Taulli

Book

Foundations of Artificial Intelligence and Expert Systems
Presenting an integrated, real-life case study incorporating all of the related managerial aspects, this book covers all facets of artificial intelligence (AI) and expert systems in a lucid and coherent manner.
Book Duration 3h 58m Book Authors By K Sarukesi, P Gopalakrishnan, V S Janakiraman

Book

Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, Volume I
Highlighting relevant technologies, uses, and techniques across various industries and settings, this publication provides a comprehensive overview of the latest breakthroughs and recent progress in artificial intelligence.
Book Duration 15h 4m Book Authors By Information Resources Management Association (IRMA)

Book

Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, Volume II
Highlighting relevant technologies, uses, and techniques across various industries and settings, this publication provides a comprehensive overview of the latest breakthroughs and recent progress in artificial intelligence.
Book Duration 16h 40m Book Authors By Information Resources Management Association (IRMA)

Book

Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, Volume III
Highlighting relevant technologies, uses, and techniques across various industries and settings, this publication provides a comprehensive overview of the latest breakthroughs and recent progress in artificial intelligence.
Book Duration 17h 16m Book Authors By Information Resources Management Association (IRMA)

Book

Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, Volume IV
Highlighting relevant technologies, uses, and techniques across various industries and settings, this publication provides a comprehensive overview of the latest breakthroughs and recent progress in artificial intelligence.
Book Duration 16h 55m Book Authors By Information Resources Management Association (IRMA)

Book

Computer Vision Using Deep Learning: Neural Network Architectures with Python and Keras
Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems.
Book Duration 3h 2m Book Authors By Vaibhav Verdhan
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BOOKS INCLUDED

Book

TensorFlow 2.0: Pocket Primer
As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to basic machine learning algorithms using TensorFlow 2.
Book Duration 3h 42m Book Authors By Oswal Campesato

Book

TensorFlow for Dummies
Providing a friendly, easy-to-follow book on TensorFlow, this thorough resource tames this sometimes intimidating technology and explains, in simple steps, how to write TensorFlow applications.
Book Duration 4h 30m Book Authors By Matthew Scarpino

Book

TensorFlow for Machine Intelligence: A Hands-On Introduction to Learning Algorithms
For anyone who knows a little machine learning (or not) and who has found the TensorFlow documentation too daunting to approach, this book introduces the TensorFlow framework and the underlying machine learning concepts that are important to harness machine intelligence.
Book Duration 3h 26m Book Authors By Ariel Scarpinelli, Danijar Hafner, Erik Erwitt, Sam Abrahams

Book

Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python
Covering the basics of Reinforcement Learning with the help of the Python programming language, this book touches on several aspects, such as Q learning, MDP, RL with Keras, and OpenAI Gym and OpenAI Environment, and also cover algorithms related to RL.
Book Duration 1h 6m Book Authors By Abhishek Nandy, Manisha Biswas

Book

Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition with TensorFlow and Keras
Exploring deep learning applications using frameworks such as TensorFlow and Keras, this book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications.
Book Duration 1h 28m Book Authors By Navin Kumar Manaswi

Book

Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python
Introducing readers to the latest version of the TensorFlow library, this book will teach you how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples.
Book Duration 1h 14m Book Authors By Avinash Manure, Pramod Singh

Book

Python for TensorFlow Pocket Primer
Intended for software developers who are advanced beginners, this book is designed to prepare programmers for machine learning and deep learning/TensorFlow topics.
Book Duration 2h 52m Book Authors By Oswald Campesato
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AUDIOBOOKS INCLUDED

Audiobook

Artificial Intelligence Basics: A Non-Technical Introduction
This audio edition equips you with a fundamental, timely grasp of AI and its impact.
Audiobook Duration 8h 25m 48s Audiobook Authors By Tom Taulli