Artificial Intelligence (AI): Artificial Intelligence 2022 intermediate
Technology:
Expertise:
- 2 Courses | 2h 6m 29s
- 1 Book | 3h 19m
- 8 Courses | 4h 33m 52s
- 7 Books | 42h 49m
- 1 Audiobook | 8h 25m 48s
- 1 Course | 1h 7m 29s
- 3 Books | 9h 43m
- 4 Courses | 4h 21m 50s
- 7 Books | 19h 31m
- 3 Courses | 2h 53m 43s
- 4 Books | 19h 58m
- 5 Courses | 6h 1m 20s
- 2 Courses | 2h 29m 35s
- 3 Books | 9h 43m
- 12 Courses | 9h 26m 41s
- 8 Books | 78h 52m
- Includes Lab
- 11 Courses | 8h 24m 31s
- Includes Lab
- 18 Courses | 25h 18m 23s
- 7 Books | 35h 38m
- 8 Courses | 9h 17m 2s
- 8 Books | 23h 20m
- 1 Course | 41m 31s
- 5 Books | 22h 23m
Artificial intelligence is the ability of machines to mimic human intelligence to reach solutions. Explore AI and its uses.
GETTING STARTED
Understanding Bots: Chatbot Architecture
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1m 29s
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4m 18s
GETTING STARTED
Artificial Intelligence: Basic AI Theory
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2m 13s
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3m 30s
GETTING STARTED
Artificial Intelligence: Human-computer Interaction Overview
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2m 9s
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3m 18s
GETTING STARTED
TensorFlow: Introduction to Machine Learning
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2m 9s
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8m 21s
GETTING STARTED
Fundamentals of AI & ML: Introduction to Artificial Intelligence
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1m
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4m 53s
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
Assessment
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 20m
Assessment
Badge
COURSES INCLUDED
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 |
37m
Assessment
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 |
21m
Assessment
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 |
34m
Assessment
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 |
39m
Assessment
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 |
26m
Assessment
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 |
35m
Assessment
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 |
44m
Assessment
Badge
Automation Design & Robotics
In this 12-video course, you will examine the different uses of data science tools and the overall platform, as well as the benefits and challenges of machine learning deployment. The first tutorial explores what automation is and how it is implemented. This is followed by a look at the tasks and processes best suited for automation. This leads learners into exploring automation design, including what Display Status is, and also the Human-Computer Collaboration automation design principle. Next, you will examine the Human Intervention automation design principle; automated testing in software design and development; and also the role of task runners in software design and development. Task runners are used to automate repeatable tasks in the build process. Delve into DevOps and automated deployment in software design, development, and deployment. Finally, you will examine process automation using robotics, and in the last tutorial in the course, recognize how modern robotics and AI designs are applied. The concluding exercise involves recognizing automation and robotics design application.
13 videos |
34m
Assessment
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 7m
Assessment
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 |
56m
Assessment
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 |
56m
Assessment
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 23m
Assessment
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 5m
Assessment
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 4m
Assessment
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 |
47m
Assessment
Badge
Implementing Robotic Process Automation
Discover how to implement Robotic Process Automation (RPA) using Python, and explore various RPA frameworks with the practical implementation of UiPath.
10 videos |
1h 2m
Assessment
Badge
COURSES INCLUDED
AI Fundamentals
Discover the fundamental concepts of the technologies driving artificial Intelligence (AI).
10 videos |
1h 3m
Assessment
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 26m
Assessment
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 47m
Assessment
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 |
56m
Assessment
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 |
47m
Assessment
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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 20m
Assessment
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 8m
Assessment
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 |
55m
Assessment
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 |
56m
Assessment
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 |
38m
Assessment
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 |
43m
Assessment
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 |
36m
Assessment
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 |
43m
Assessment
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 |
26m
Assessment
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 7m
Assessment
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 |
41m
Assessment
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
Assessment
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 |
39m
Assessment
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 |
49m
Assessment
Badge
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COURSES INCLUDED
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 |
40m
Assessment
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 |
46m
Assessment
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 |
38m
Assessment
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 |
38m
Assessment
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 |
38m
Assessment
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 |
57m
Assessment
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 |
44m
Assessment
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 |
51m
Assessment
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 |
57m
Assessment
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 |
50m
Assessment
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 |
41m
Assessment
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COURSES INCLUDED
Deep Learning for NLP: Introduction
In recent times, natural language processing (NLP) has seen many advancements, most of which are in deep learning models. NLP as a problem is very complicated, and deep learning models can handle that scale and complication with many different variations of neural network architecture. Deep learning also has a broad spectrum of frameworks that supports NLP problem solving out-of-the-box. Explore the basics of deep learning and different architectures for NLP-specific problems. Examine other use cases for deep learning NLP across industries. Learn about various tools and frameworks used such as - Spacy, TensorFlow, PyTorch, OpenNMT, etc. Investigate sentiment analysis and explore how to solve a problem using various deep learning steps and frameworks. Upon completing this course, you will be able to use the essential fundamentals of deep learning for NLP and outline its various industry use cases, frameworks, and fundamental sentiment analysis problems.
14 videos |
1h 17m
Assessment
Badge
Deep Learning for NLP: Neural Network Architectures
Natural language processing (NLP) is constantly evolving with cutting edge advancements in tools and approaches. Neural network architecture (NNA) supports this evolution by providing a method of processing language-based information to solve complex data-driven problems. Explore the basic NNAs relevant to NLP problems. Learn different challenges and use cases for single-layer perceptron, multi-layer perceptron, and RNNs. Analyze data and its distribution using pandas, graphs, and charts. Examine word vector representations using one-hot encodings, Word2vec, and GloVe and classify data using recurrent neural networks. After you have completed this course, you will be able to use a product classification dataset to implement neural networks for NLP problems.
19 videos |
2h 30m
Assessment
Badge
Deep Learning for NLP: Memory-based Networks
In the journey to understand deep learning models for natural language processing (NLP), the subsequent iterations are memory-based networks, which are much more capable of handling extended context in languages. While basic neural networks are better than machine learning (ML) models, they still lack in more significant and large language data problems. In this course, you will learn about memory-based networks like gated recurrent unit (GRU) and long short-term memory (LSTM). Explore their architectures, variants, and where they work and fail for NLP. Then, consider their implementations using product classification data and compare different results to understand each architecture's effectiveness. Upon completing this course, you will have learned the basics of memory-based networks and their implementation in TensorFlow to understand the effect of memory and more extended context for NLP datasets.
12 videos |
1h 27m
Assessment
Badge
Deep Learning for NLP: Transfer Learning
The essential aspect of human intelligence is our learning processes, constantly augmented with the transfer of concepts and fundamentals. For example, as a child, we learn the basic alphabet, grammar, and words, and through the transfer of these fundamentals, we can then read books and communicate with people. This is what transfer learning helps us achieve in deep learning as well. This course will help you learn the fundamentals of transfer learning for NLP, its various challenges, and use cases. Explore various transfer learning models such as ELMo and ULMFiT. Upon completing this course, you will understand the transfer learning methodology of solving NLP problems and be able to experiment with various models in TensorFlow.
16 videos |
2h 10m
Assessment
Badge
Deep Learning for NLP: GitHub Bug Prediction Analysis
Get down to solving real-world GitHub bug prediction problems in this case study course. Examine the process of data and library loading and perform basic exploratory data analysis (EDA) including word count, label, punctuation, and stop word analysis. Explore how to clean and preprocess data in order to use vectorization and embeddings and use counter vector and term frequency-inverse document frequency (TFIDF) vectorization methods with visualizations. Finally, assess different classifiers like logistic regression, random forest, or AdaBoost. Upon completing this course, you will understand how to solve industry-level problems using deep learning methodology in the TensorFlow ecosystem.
13 videos |
1h 55m
Assessment
Badge
Natural Language Processing: Getting Started with NLP
Enterprises across the world are creating large amounts of language data. There are many different kinds of data with language components including reports, word documents, operational data, emails, reviews, sops, and legal documents. This course will help you develop the skills to analyze this data and extract valuable and actionable insights. Learn about the various building blocks of natural language processing to help in understanding the different approaches used for solving NLP problems. Examine machine learning and deep learning approaches to handling NLP issues. Finally, explore common use cases that companies are approaching with NLP solutions. Upon completion of this course, you will have a strong foundation in the fundamentals of natural language processing, its building blocks, and the various approaches that can be used to architect solutions for enterprises in NLP domains.
12 videos |
40m
Assessment
Badge
Natural Language Processing: Linguistic Features Using NLTK & spaCy
Without fundamental building blocks and industry-accepted tools, it is difficult to achieve state-of-art analysis in NLP. In this course, you will learn about linguistic features such as word corpora, tokenization, stemming, lemmatization, and stop words and understand their value in natural language processing. Begin by exploring NLTK and spaCy, two of the most widely used NLP tools, and understand what they can help you achieve. Learn to recognize the difference between these tools and understand the pros and cons of each. Discover how to implement concepts like part of speech tagging, named entity recognition, dependency parsing, n-grams, spell correction, segmenting sentences, and finding similar sentences. Upon completion of this course, you will be able to build basic NLP applications on any raw language data and explore the NLP features that can help businesses take actionable steps with this data.
13 videos |
1h 10m
Assessment
Badge
Text Mining and Analytics: Pattern Matching & Information Extraction
Sometimes, business wants to find similar-sounding words, specific word occurrences, and sentiment from the raw text. Having learned to extract foundational linguistic features from the text, the next objective is to learn the heuristic approach to extract non-foundational features which are subjective. In this course, learn how to extract synonyms and hypernyms with WordNet, a widely used tool from the Natural Language Toolkit (NLTK). Next, explore the regex module in Python to perform NLTK chunking and to extract specific required patterns. Finally, you will solve a real-world use case by finding sentiments of movies using WordNet. After comleting this course, you will be able to use a heuristic approach of natural language processing (NLP) and to illustrate the use of WordNet, NLTK chunking, regex, and SentiWordNet.
12 videos |
1h 52m
Assessment
Badge
Text Mining and Analytics: Machine Learning for Natural Language Processing
Machine learning (ML) is one of the most important toolsets available in the enterprise world. It gives predictive powers to data that can be leveraged to investigate future behaviors and patterns. It can help companies proactively improve their business and help optimize their revenue. Learn how to leverage machine learning to make predictions with language data. Explore the ML pipelines and common models used for Natural Language Processing (NLP). Examine a real-world use case of identifying sarcasm in text and discover the machine learning techniques suitable for NLP problems. Learn different vectorization and feature engineering methods for text data, exploratory data analysis for text, model building, and evaluation for predicting from text data and how to tune those models to achieve better results. After completing this course, you'll be able to illustrate the use of machine learning to solve NLP problems and demonstrate the use of NLP feature engineering.
13 videos |
2h 2m
Assessment
Badge
Text Mining and Analytics: Natural Language Processing Libraries
There are many tools available in the Natural Language Processing (NLP) tool landscape. With single tools, you can do a lot of things faster. However, using multiple state-of-art tools together, you can solve many problems and extract multiple patterns from your data. In this course, you will discover many important tools available for NLP such as polyglot, Genism, TextBlob, and CoreNLP. Explore their benefits and how they stand against each other for performing any NLP task. Learn to implement core linguistic features like POS tags, NER, and morphological analysis using the tools discussed earlier in the course. Discover defining features of each tool such as multiple language support, language detection, topic models, sentiment extractions, part of speech (POS) driven patterns, and transliterations. Upon completion of this course, you will feel confident with the Python tool ecosystem for NLP and will be able to perform state-of-art pattern extraction on any kind of text data.
13 videos |
1h 59m
Assessment
Badge
Text Mining and Analytics: Hotel Reviews Sentiment Analysis
Using natural language processing (NLP) tools, an organization can analyze their review data and predict the sentiments of their customers. In this course, we'll learn how to implement NLP tools to solve a business problem end-to-end. To begin, learn about loading, exploring, and preprocessing business data. Next, explore various linguistic features and feature engineering methods for data and practice building machine learning (ML) models for sentiment prediction. Finally, examine the automation options available for building and deploying models. After completing this course, you will be able to solve NLP problems for enterprises end-to-end by leveraging a variety of concepts and tools.
11 videos |
1h 7m
Assessment
Badge
Advanced NLP: Introduction to Transformer Models
With recent advancements in cheap GPU compute power and natural language processing (NLP) research, companies and researchers have introduced many powerful models and architectures that have taken NLP to new heights. In this course, learn about Transformer models like Bert and GPT and the maturity of AI in NLP areas due to these models. Next, examine the fundamentals of Transformer models and their architectures. Finally, discover the importance of attention mechanisms in the Transformer architecture and how they help achieve state-of-the-art results in NLP tasks. Upon completing this course, you'll be able to understand different aspects of Transformer architectures like the self-attention layer and encoder-decoder models.
12 videos |
40m
Assessment
Badge
Advanced NLP: Introduction to BERT
In every domain of artificial intelligence, there is one algorithm that transforms the entire field into an industry-matured tool to be used across a broad spectrum of use cases. BERT is that algorithm for natural language processing (NLP). In this course, explore the fundamentals of BERT architecture, including variations, transfer learning capabilities, and best practices. Examine the Hugging Face library and its role in sentiment analysis problems. Practice model setup, pre-processing, sentiment classification training, and evaluating models using BERT. Finally, take a critical look to recognize the challenges of using BERT. Upon completing this course, you'll be able to demonstrate how to solve simple sentiment analysis problems.
12 videos |
1h 14m
Assessment
Badge
Advanced NLP: Introduction to GPT
Generative Pre-trained Transformer (GPT) models go beyond classifying and predicting text behavior to helping actually generate text. Imagine an algorithm that can produce articles, songs, books, or code - anything that humans can write. That is what GPT can help you achieve. In this course, discover the key concepts of language models for text generation and the primary features of GPT models. Next, focus on GPT-3 architecture. Then, explore few-shot learning and industry use cases and challenges for GPT. Finally, practice decoding methods with greedy search, beam search, and basic and advanced sampling methods. Upon completing this course, you will understand the fundamentals of the GPT model and how it enables text generation.
12 videos |
1h 9m
Assessment
Badge
Advanced NLP: Language Translation Using Transformer Model
Translating from one language to another is a common task in Natural Language Processing (NLP). The transformer model works by passing multiple words through a neural network simultaneously and is one of the newest models propelling a surge of progression, sometimes referred to as transformer AI. In this course, you will solve real-world machine translation problems, translating from English to French. Explore machine translation problem formulation, notebook setup, and data pre-processing. Then, learn to tokenize and vectorize data into a sequence of integers, where each integer represents the index of a word in a vocabulary. Discover transformer encoder-decoder and see how to produce input and output sequences. Finally, define the attention layer and assemble, train, and evaluate the translation model end to end. Upon completing this course, you will be able to solve industry-level problems using deep learning methodology in the TensorFlow ecosystem.
13 videos |
1h 29m
Assessment
Badge
NLP Case Studies: News Scraping Translation & Summarization
Keeping up with current events can be challenging, especially when you live or work in a country where you do not speak the language. Learning a new language can be difficult and time-consuming when you have a busy schedule. In this course, you will learn how to scrape news articles written in Arabic from websites, translate them into English, and then summarize them. First, focus on the overall architecture of your summarization application. Next, discover the Transformers library and explore its role in translation and summarization tasks. Then, create a user interface for the application using Gradio. Upon completion of this course, you'll be able to use an application to scrape data written in Arabic from any URL, translate it into English, and summarize it
7 videos |
43m
Assessment
Badge
NLP Case Studies: Article Text Comprehension & Question Answering
Most current question answering datasets will frame the task as reading comprehension, where the question is about a paragraph or document and the answer often is a span in the document. Some specific tasks of reading comprehension include multi-modal machine reading comprehension and textual machine reading comprehension, among others. This course focuses on the architecture of the Q&A pipeline. First, install the Transformers library and import a text comprehension model to create your Q&S pipeline. Then, use Gradio to develop a user interface for answering questions about a given article. Upon completion, you'll be able to develop an application that can answer questions asked by a user about a given article.
7 videos |
28m
Assessment
Badge
NLP Case Studies: Developing an AI Chatbot
An AI chatbot is a program within a website or app that simulates human conversations using natural language processing (NLP). Chatbots are programmed to address users' needs independently of a human operator. Common chatbot functions include answering frequently asked questions and helping users navigate a website or app. In this course, explore the AI chatbot application flow and learn about data loading and text preprocessing. Next, discover how to transform the data into numeric values and perform one-hot data encoding. Finally, practice creating and training models, loading a trained model, defining a response function, and setting test questions. Upon completion, you'll be able to develop a simple chatbot using transformers that will automatically reply to user questions.
9 videos |
1h 19m
Assessment
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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 40m
Assessment
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 36m
Assessment
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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 11m
Assessment
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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 21m
Assessment
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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 |
42m
Assessment
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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 |
57m
Assessment
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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 |
59m
Assessment
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TensorFlow: Building Autoencoders
Explore how to perform dimensionality reduction using powerful unsupervised learning techniques such as Principal Components Analysis and autoencoding.
10 videos |
46m
Assessment
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COURSES INCLUDED
Fundamentals of AI & ML: Introduction to Artificial Intelligence
Artificial intelligence (AI) provides cutting-edge tools to help organizations predict behaviors, identify key patterns, and drive decision-making in a world that is increasingly made up of data. In this course, you will explore the full definition of AI, how it works, and when it can be used. You will identify the types of data tools and technologies AI uses to operate. Next, you will discover a framework for using the AI life cycle and data science process. Finally, you'll consider what you need to keep in mind as you implement AI techniques in your organization. Upon completion of this course, you'll be familiar with common concepts and use cases of artificial intelligence (AI) and be able to outline strategies for each part of the AI life cycle.
11 videos |
41m
Assessment
Badge
EARN A DIGITAL BADGE WHEN YOU COMPLETE THESE COURSES
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.
Digital badges are yours to keep, forever.BOOKS INCLUDED
Book
Practical Python AI Projects: Mathematical Models of Optimization Problems with Google OR-ToolsWritten 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.
3h 19m
By Serge Kruk
BOOKS INCLUDED
Book
Artificial Intelligence for DummiesMaking 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.
5h 52m
By John Paul Mueller, Luca Massaron
Book
Foundations of Artificial Intelligence and Expert SystemsThis title was removed from the Skillsoft library on September 13, 2023.
3h 58m
By K Sarukesi, P Gopalakrishnan, V S Janakiraman
Book
Artificial Intelligence Safety and SecurityIncluding 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.
14h 43m
By Roman V. Yampolskiy
Book
Artificial Intelligence and Problem SolvingOffering 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.
4h 17m
By Christopher Pileggi, Danny Kopec, David Ungar, Shweta Shetty
Book