Artificial Intelligence (AI): Java SE 8 intermediate

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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

  • 2m 23s
  • 3m 24s

GETTING STARTED

Search Problems

  • 5m 27s
  • 3m 22s

GETTING STARTED

OpenCV: Introduction

  • 2m 16s
  • 5m 10s

GETTING STARTED

Understanding Bots: Chatbot Architecture

  • 1m 29s
  • 4m 18s

GETTING STARTED

Introduction to the OpenAI API

  • 49s
  • 5m 54s

GETTING STARTED

An Introduction to Generative AI

  • 2m 23s
  • 10m 59s

GETTING STARTED

Artificial Intelligence: Basic AI Theory

  • 2m 13s
  • 3m 30s

GETTING STARTED

AI Fundamentals

  • 3m 50s
  • 3m 21s

GETTING STARTED

OpenCV: Manipulating Images

  • 2m 25s
  • 10m 50s

GETTING STARTED

Artificial Intelligence: Human-computer Interaction Overview

  • 2m 9s
  • 3m 18s

GETTING STARTED

AI in Industry

  • 1m 35s
  • 2m 50s

GETTING STARTED

Deep Learning for NLP: Introduction

  • 1m 21s
  • 4m 26s

GETTING STARTED

TensorFlow: Introduction to Machine Learning

  • 2m 9s
  • 8m 21s

GETTING STARTED

Fundamentals of AI & ML: Foundational Data Science Methods

  • 43s
  • 8m 58s

GETTING STARTED

Text, Image, & Audio Generation with OpenAI

  • 56s
  • 6m 15s

GETTING STARTED

Prompt Engineering: Ethical Hacking & Generative AI Fusion

  • 1m 5s
  • 5m 15s

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 20m has Assessment available 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 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 | 21m 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 | 34m 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 | 39m 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 | 26m 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 | 35m 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 | 44m has Assessment available 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 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 7m 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 | 56m 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 | 56m 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 23m 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 5m has Assessment available Badge
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COURSES INCLUDED

Introduction to the OpenAI API
OpenAI offers an Application Programming Interface (API) that allows users to create, manipulate, and translate text using its available models and endpoints. Understanding how the API works, its limits, and how to effectively use best practices will help you get the most from the interface. In this course, you will explore OpenAI's API, generate an API key, and learn about the impact of social bias and blindness in models. Then, you will discover the ethical usage policy and safety and privacy concerns of OpenAI. Next, you will examine available models and endpoints. You will create a simple text completion, parse a response, troubleshoot common errors, and apply parameters to improve your results. Finally, you will use the language translation API to translate to and from English and identify organizational best practices when using OpenAI to handle scaling, latency, and limits.
14 videos | 1h 16m has Assessment available Badge

COURSES INCLUDED

An Introduction to Generative AI
Generative artificial intelligence (AI) focuses on creating models that can generate content such as text, images, or even multimedia. Unlike discriminative models that classify or label existing data, generative models operate by learning patterns from the provided data and producing novel outputs. You'll begin this course with an overview of generative. You will explore some notable examples of generative models, including OpenAI's ChatGPT and Google Bard. Next, you will look at the use of prompt engineering when interacting with AI chatbots. Then, you will then delve into the history and evolution of generative AI models including important milestones that culminated in the conversational agents that we work with today. Finally, you will explore the risks and ethical considerations associated with generative AI, such as unintentional use of copyrighted data, the use of personal data for training, and the creation of malicious deepfakes using AI. You will also learn how you can mitigate some of these risks while working with generative technologies.
11 videos | 1h 40m has Assessment available Badge
Generative AI APIs for Practical Applications: An Introduction
Generative artificial intelligence (AI) has taken the tech and business world by storm. It currently can create stories, text, images, summaries, essays, and much more, with sometimes nothing more than a few words to describe what you want. Unfortunately, it can also be used in ways that can be harmful, such as creating deepfakes and false information. In this course, you will discover the differences between generative AI and general AI and look at the history and future of generative AI. You will explore applications of generative AI and the ethical, safety, security, and privacy concerns associated with its use. Then you will identify common generative AI application programming interfaces (APIs) and best practices when using generative AI. Next, you will find out how to create images and text with generative AI, and you will focus on the challenges of AI integration into processes and workflows. Finally, you will learn how to integrate generative AI APIs to create tools like chatbots.
14 videos | 1h 14m has Assessment available Badge
Google Generative AI APIs: Introduction to Google Bard
Google Bard is a generative artificial intelligence (AI) that uses a large language model to facilitate answering questions and creating content for a wide range of topics. Understanding how the models work, its limitations, and what functionality the service provides enables anyone to optimize their usage of the service to accomplish a multitude of tasks. In this course, you will explore the Bard interface and learn to use Bard to answer questions and create content while also understanding Bard's limitations, features, and best practices. Additionally, you will explore the ethics, privacy, and security concerns that can come with using a generative AI like Bard.
14 videos | 1h 11m has Assessment available Badge
Google Generative AI APIs: Bard Fundamentals
Google Bard can be used to write creative content, but it also allows you to share that content, adjust content to reflect a tone, and translate text to and from English. These capabilities can be used by almost anyone in virtually any industry to expedite tasks. In this course, you will learn how to use Bard to create poems, stories, lyrics and other content. You will also learn to create summaries and outlines. Next, you will discover Bard's image object recognition and finding capabilities. Finally, you will be introduced to Bard's translation capabilities.
15 videos | 1h 23m has Assessment available Badge
Google Generative AI APIs: Bard & the PaLM API Fundamentals
Google Bard is a useful tool for content creation, translation, and analysis; however, using the PaLM 2 API it is possible to integrate Bard directly into your own processes via the provided application programming interface (API) or the client libraries that are ready to go. This does require some programming and command line interface (CLI) experience but even a small amount should be sufficient to follow along. In this course you will learn about Bard's analytical capabilities, the PaLM 2 API, and how to use the API to accomplish tasks programmatically rather than through the Bard web interface. Additionally, you will explore the PaLM models, support languages and libraries, and the interfaces used for communicating with PaLM.
14 videos | 1h 10m has Assessment available Badge
Google Generative AI APIs: Advanced Features of Bard & PaLM
Python and Google Bard can be combined to create applications and programs via the PaLM 2 API. These programs can solve problems or integrate Bard into workflows or processes. In this course, you will learn to solve code problems with Bard and how to use the Python Client API library to connect and use PaLM to create applications that integrate Bard. In particular, you will explore how to programmatically check content for appropriate communications, adjust parameters to fine-tune responses, troubleshoot common problems, add security to a process, and create a simple chatbot.
14 videos | 1h 21m has Assessment available Badge
Getting Started with Prompt Engineering
Generative artificial intelligence (AI) can create new content, such as text, images, and music. It is powered by machine learning (ML) models that have been trained on massive datasets of existing content. Prompt engineering is the process of designing and crafting prompts that guide generative AI models to produce the desired output. You will start this course by learning how you can leverage prompt engineering to improve your day-to-day and work-related tasks. Next, you will explore how to use generative AI chatbots such as ChatGPT, Google Bard, and Microsoft Bing Chat. You will create an account with OpenAI and explore ChatGPT's interface, before diving into natural-language conversation. You will then explore Google Bard and Bing Chat for conversational AI. Finally, you will work with Perplexity AI.
15 videos | 2h 1m has Assessment available Badge
Exploring the OpenAI Playground
The OpenAI Playground is a web-based tool that lets you experiment with large language models (LLMs) to generate text, translate languages, write creative content, and answer your questions in an informative way. With the Playground, you can input text prompts and receive real-time outputs, and you can adjust hyperparameters to control the creativity, randomness, length, and repetition of the model responses. In this course, you will begin by creating an account to use the OpenAI Playground and you will learn how you are billed for its usage. Next, you will explore the different chat modes and models and work with the hyperparameters that allow you to configure creativity, randomness, repetition, and the length of model responses. You will also use stop sequences, which terminate the output when a specific phrase is reached, as well as the frequency and presence penalty, which penalize repetition of words and topics. Finally, you will learn how to view probabilities in generated text and explore how to use presets to share prompts and prompt parameters with other people.
14 videos | 1h 46m has Assessment available Badge
Prompt Engineering for Git: Leveraging Prompt Engineering to Learn Git
Version control systems allow you to track changes to your code over time and collaborate on projects. They are widely used in software development, but can also be used for other purposes, such as tracking changes to documentation, website code, or other types of files. Git is a popular version control system that has a steep learning curve for beginners but with help from generative AI tools you'll find that learning Git is easy and intuitive. In this course, you will start with the basics of Git and learn the difference between local Git repositories and remote repositories on hosting services such as GitHub and GitLab. You will develop prompts with generative AI tools such as ChatGPT and use their responses to guide you while you are exploring Git commands. Next, you will learn how to use Git for version control and how to add files to the staging area. After that, you will commit your files to your repository and view all of the commits. Finally, you will learn how to perform operations such as restoring and modifying staged files and how to use commit hashes to uniquely identify commits and perform operations on them.
11 videos | 1h 15m has Assessment available Badge
Introduction to Generative AI on Azure
In today's rapidly evolving technological landscape, generative artificial intelligence (AI) has gained significant attention for its ability to create intelligent solutions. This path focuses on leveraging the Azure cloud platform to explore and harness the power of generative AI. In this course, you'll explore how generative AI works and types of generative AI models. Then, you'll be introduced to Azure services for generative AI, including Azure OpenAI service, Azure Bot service, and Azure Machine Learning. Finally, you'll learn about privacy and policy considerations for generative AI, chatbot creation, personalized marketing content, new product development, and training and tuning generative AI models.
14 videos | 1h 24m has Assessment available Badge
Generative AI with Azure OpenAI
Artificial intelligence (AI) is being harnessed everywhere today for a myriad of different practical applications. Microsoft Azure's OpenAI service is a key component in the development of AI apps in Azure and has gained significant attention for its ability to create intelligent solutions. In this course, you'll learn about Azure OpenAI service, including models, practical uses, and AI content generation principles with OpenAI. Then, you'll explore integration with Azure OpenAI, text and question answering, OpenAI vs. other generative AI services, and OpenAI pricing. Finally, you'll dig into limitations of OpenAI and what the future holds for Azure OpenAI.
12 videos | 1h has Assessment available Badge
Prompt Engineering for Git: Working with Remote Repositories & Generative AI
GitHub, in conjunction with Git, provides a powerful framework for collaboration in software development. Git handles version control locally, while GitHub extends this functionality by serving as a remote repository, enabling teams to collaborate seamlessly by sharing, reviewing, and managing code changes. In this course, you will begin by setting up a GitHub account and authenticating yourself from the local repo using personal access tokens. You will then push your code to the remote repository and view the commits. Next, you will explore additional features of Git and GitHub using generative AI tools as a guide. You will also create another user to collaborate on your remote repository, and you'll sync changes made by other users to your local repo. Finally, you will explore how to merge divergent branches. You will discover how to resolve a divergence using the merge method with help from ChatGPT and bring your local repository in sync with remote changes.
12 videos | 1h 29m has Assessment available Badge
Prompt Engineering for Git: Using Prompt Engineering to Work with Git Branches
Branches are separate, independent lines of development for people working on different features. Once you have finished your work, you can merge all your branches together. You will start this course by creating separate feature branches on Git and pushing commits to these branches. You will use prompt engineering to get the right commands to use for branching and working on branches. You will also explore how to develop your code on the main branch, switch branches, and then ultimately commit to a feature branch. Next, you will explore how you can stash changes to your project to work on them later. Finally, you will discover how to resolve divergences in the branches. You will try out both the merge and rebase methods and confirm that the branch commits are combined properly.
13 videos | 1h 34m has Assessment available Badge
Prompt Engineering for Data: Leveraging Prompts When Working with Data
Python is a powerful programming language for data science, and pandas is a popular open-source data manipulation and analysis library in Python. Combined with prompt engineering techniques, working with data in Python is easy and intuitive, which allows you to be more productive and efficient. You will start this course by leveraging prompt engineering to work with pandas. You will explore libraries such as Matplotlib, seaborn, and Plotly, which are used for visualization and charting. With ChatGPT's help you will read data from a CSV file and inspect the DataFrame. You'll delve into pandas Series objects and explore their creation and manipulation. You will leverage prompt engineering techniques to access elements in a Series using index labels through loc, iloc, at, and iat functions and perform operations like modification and visualization. Finally, you will explore how to use pandas DataFrame objects and create basic DataFrames using lists and dictionaries for data assignment and inspection. You will also generate code to perform basic operations on DataFrames using tools such as ChatGPT and Bard.
12 videos | 1h 37m has Assessment available Badge
Prompt Engineering for Data: Basic Data Manipulation Using Generative AI
With DataFrames in pandas you can filter, aggregate, join, pivot, and manipulate data efficiently. These operations enable data analysts and scientists to work with datasets for various data-driven tasks. Prompt engineering tools are adept at generating code to make these tasks simple. You will start this course by exploring the configurations you can apply to read in your data. You'll present your problem statement to ChatGPT and explore the use of arguments to configure various aspects of the file reading, such as defining column names, and specifying which columns to include in the DataFrame. Additionally, you will learn how to read data from different sources, including JSON, Excel, and the Clipboard and write files out to these different formats. Next, you'll delve into common DataFrame operations, examine statistics on your data, rename columns, iterate over, and sort your data. As you encounter issues, you will turn to prompt engineering to help debug them. Finally, you'll explore how you can enhance your data using computed columns. You'll harness the power of two essential functions, apply and map, to transform your records. You will also focus on utilizing generative AI for code generation and you will employ the chain-of-thought prompting method to guide the chatbot in generating code effectively.
12 videos | 1h 36m has Assessment available Badge
Leading Security Teams for GenAI Solutions: Enhancing Security Posture
Enhancing your security posture is an essential part of protecting yourself against generative artificial intelligence (AI) technologies. Monitoring and leveraging emerging generative AI technologies help organizations stay safe and secure while reducing manual work. In this course, discover why enhancing security posture is important, common security and cyber threats organizations are facing today, and the applications and advantages of using AI in cybersecurity. Next, examine the components of a successful cyberattack defense strategy and how to leverage machine learning (ML) in cybersecurity. Lastly, explore AI cybersecurity considerations and possible future trends of AI in cybersecurity. Upon course completion, you'll recognize how to monitor and utilize emerging generative AI technologies to help improve an organization's overall security posture.
13 videos | 2h 25m has Assessment available Badge
Leading Security Teams for GenAI Solutions: Protecting Intellectual Property
Protecting intellectual property (IP) is an important part of any business. In this course, you will learn the fundamentals of intellectual property including copyright, trademarks, patents, and trade secrets. Discover how to defend against intellectual property infringement in the realm of generative AI, how using generative AI can result in IP infringement, and considerations to have when using generative AI solutions for yourself or your business. Then you will learn how to detect when your intellectual property has been infringed upon, identify risk areas, and determine what detection tools are available. Finally, you will explore legal considerations surrounding intellectual property and AI and investigate future trends of intellectual property in the era of AI. After completing this course, you will be ready to the crucial steps to detect and protect the intellectual property within your organization.
15 videos | 1h 49m has Assessment available Badge
Leading Security Teams for GenAI Solutions: Generative AI Governance
Generative artificial intelligence (AI) creates a variety of concerns in a number of different ways, so we must create governance to regulate these concerns. In this course, you will discover why governance in the realm of generative AI is crucial, how and by whom governance can be achieved, as well as the challenges and opportunities that arise with the use of generative AI. In addition, you will also explore how governments and legal bodies can regulate generative AI, and what regulations are already in place in the public sector. Then you will examine AI governance best practices, including engaging stakeholders, managing AI models, and building internal governance structures. Next, you will investigate the benefits of AI auditing and monitoring. Finally, you will learn how to implement a governance approach that includes user education, data and AI risk management, and regulatory compliance.
14 videos | 1h 20m has Assessment available Badge
Leading Security Teams for GenAI Solutions: Use of Generative AI
When using generative artificial intelligence (AI) content within your business, you need to know how to leverage it safely and effectively. In this course, you will learn about techniques used to identify AI-generated content and how to avoid possible misinformation that it can produce. You will investigate deepfakes and learn how to detect them. You will discover how to provide proper attribution when using generative AI content and how to avoid having any copyright issues. Next, you will explore industry use cases for generative AI, and more specifically, discover common use cases for boosting cybersecurity using generative AI. Then you will examine stakeholder considerations and possible generative AI challenges. Finally, you will focus on security protections, security risks, and mitigating risks associated with generative AI. Upon course completion you will be able to confidently take steps to secure your organization when using generative AI solutions.
16 videos | 1h 47m has Assessment available Badge
Leading Security Teams for GenAI Solutions: Security Implications
The introduction of generative AI has created security, privacy, ethical, and moral implications for everyone involved. In this course, you will learn how and why generative AI is being used in security attacks, how to put countermeasures in place to defend against these attacks, and how to navigate and mitigate those attacks from happening. You will also explore the ethical and moral concerns that generative AI has created, how privacy plays a role in those concerns, and how to manage those risks. Then you will discover how generative AI can be leveraged by threat actors to perform data breaches, create malware threats, and coordinate social engineering attacks. Next, you will investigate how generative AI can be used maliciously to perform model manipulation and data poisoning. Finally, you will examine how to enhance protection controls using processes, governance, and ethics, and focus on common considerations for securing AI systems.
12 videos | 1h 19m has Assessment available Badge
Learning Django Using Generative AI Help
Django is a high-level, open-source web framework for building robust and scalable web applications using the Python programming language. Django comes equipped with a rich set of built-in features, including an object-relational mapping (ORM) system for database interactions, a powerful templating engine, and a secure authentication system. You will start this course by diving into Django and learning the model-template-view (MTV) architecture that Django uses. Next, you will install Django and create a basic app, seeking the help of generative AI tools such as ChatGPT and Google Bard to set up a Django project and explore its basic functionality. Then, you will create your own app within the project, focusing on the uses for and responsibilities of the automatically generated files. Finally, you will build a simple web app using Django, starting with a basic view that renders HTML templates that you can access at a URL path. You will learn to include static assets, such as stylesheets and images, and you will deal with misdirection from generative AI tools.
11 videos | 1h 36m has Assessment available Badge
Generative AI on GCP: An Introduction
Generative artificial intelligence (AI) has taken the world by storm. Chatbots, photo creation, document writing, and other practical applications are everywhere, and they're gaining in popularity and sophistication. Google Cloud Platform (GCP) has a broad range of powerful generative AI tools that can be used to leverage the power of modern artificial intelligence. In this course, you'll be introduced to generative AI, beginning with GCP and its generative AI offerings. You will discover the advantages and disadvantages of generative AI and features of GCP machine learning (ML). Then you'll learn about the generative AI life cycle, image generation, natural language processing (NLP), and best practices for developing generative AI. Finally, you'll explore GCP privacy, security, and compliance considerations, monitoring and logging with GCP, and some real-world generative AI use cases.
15 videos | 1h 56m has Assessment available Badge
Using OpenAI APIs: Exploring APIs with the OpenAI Playground
The OpenAI Playground is a dynamic and user-friendly platform that allows individuals to engage with OpenAI's cutting-edge language models, such as GPT-3.5 and GPT-4. The Playground enables users to experiment with natural language processing (NLP) capabilities and parameters to tweak the responses of models. You will start this course by exploring the fundamentals of OpenAI models. Next, you will log into the OpenAI Playground and input basic prompts, observing the responses. You will work with multiple application programming interfaces (APIs), including the recommended chat completions API and the legacy completions API, all of which are accessible via the playground. Finally, you will work with the Assistants API which has access to tools for data retrieval, code interpretation, and function calling and can leverage these to respond to user queries. You will utilize the code interpreter to read and visualize CSV data, generating Python code for charts using libraries like Matplotlib and Seaborn.
12 videos | 1h 45m has Assessment available Badge
Using OpenAI APIs: Accessing OpenAI APIs from Python
OpenAI application programming interfaces (APIs) represent a groundbreaking leap in the accessibility of state-of-the-art natural language processing (NLP) capabilities. These APIs provide developers with a powerful toolset to integrate advanced language models seamlessly into their applications, products, and services. You will start this course by engaging with OpenAI through the command-line, utilizing the OpenAI APIs. You will learn how to authenticate yourself using API keys when programmatically accessing API endpoints using cURL commands. You will explore how to configure context for past interactions with the model and access both chat completions and legacy completions APIs via their respective endpoints. Moving onto Python, you will install the OpenAI library to create a client object for endpoint access. You will configure the API key and send requests to the chat completions endpoint with prompts in the JSON format. You will also explore the legacy completions API using the same client object. You will be introduced to the diverse range of model offerings from OpenAI and learn how to use those models. Finally, you will configure model parameters to adjust the response from the model. You will learn about the seed parameter to receive deterministic responses and how the system fingerprint helps track infrastructure changes on the server. You will explore various parameters, including Top P and Temperature for controlling creativity, max length, and stop sequences for response length, and frequency and presence penalty for word and topic repetition.
12 videos | 1h 53m has Assessment available Badge
Computing Descriptive Statistics Using Prompt Engineering
Statistics is a branch of mathematics that involves the collection, analysis, interpretation, presentation, and organization of data. It provides a framework for making inferences and drawing generalizable conclusions from observed information and it offers great tools to uncover patterns, trends, and relationships within datasets. Begin this course by exploring two important types of statistics - descriptive and inferential statistics. Next, learn how to compute and interpret descriptive statistics in code, including measures of central tendency and dispersion, mean and median, and range. Then use generative artificial intelligence (AI) tools to help interpret visualizations and understand the nuance between the different statistical measures and when you would choose to use them. After completing this course, you will have a solid understanding of how to calculate, interpret, and visualize descriptive statistics using Python and be able to leverage prompt engineering to help with implementation and interpretation.
9 videos | 1h 24m has Assessment available Badge
Running Statistical Tests Using Generative AI Tools
Hypothesis testing is an important part of inferential statistics that involves assessing sample data to draw conclusions about a population parameter. Begin this course by exploring how hypothesis tests work, the results they generate, and how you interpret those results. You will learn how you set up the null and alternative hypotheses for tests and how to interpret the results which includes the test statistic and the p-value. Then you will discover the different types of t-tests, such as one-sample, two-sample, and paired samples. Finally, you will investigate the use of generative artificial intelligence (AI) tools to implement one-sample t-tests and interpret the results. At course completion, you will have a solid understanding of the basics of hypothesis testing and how prompt engineering can help you implement and interpret these statistical tests.
11 videos | 1h 27m has Assessment available Badge
Prompt Engineering for Hypothesis Testing
T-tests and analysis of variance (ANOVA) are statistical methods used to compare means between groups and assess whether observed differences are statistically significant. In this course, you will perform two-sample t-tests, comparing two independent groups to determine if the difference between their means is statistically significant. You will use ChatGPT and Google Bard to help ensure that your samples meet the assumptions of the t-test. Then you will visualize and interpret the characteristics of your data and run the right variation of the t-test based on your data. Next, you will run a paired sample t-test with help from generative artificial intelligence (AI) tools. Finally, you will use ANOVA to compare multiple samples simultaneously, use prompt engineering to determine when to use ANOVA, and use post-hoc analysis after running ANOVA to identify which groups or categories are significantly different. After completing this course, you will have a solid understanding of t-tests and ANOVA, and be able to leverage Generative AI tools to help you with your analysis.
13 videos | 1h 58m available Badge
Prompt Engineering for Machine Learning
Machine learning involves creating models that dynamically change based on the data from which they are created. Within machine learning, three fundamental problems-regression, classification, and clustering-are the focus of a variety of solution techniques. Begin this course by conducting regression analysis. You will analyze and visualize data to get a sense of the variables with predictive power, split data into training and test sets, and train a model. Then you will interpret the R-squared metric to evaluate how well the regression model has performed. Next, you will create a classification model for predicting categorical targets and split your data into test and training data to train a logistic regression model. You will also explore the impact of training a model on imbalanced data, and with generative artificial intelligence (AI) assistance, see how you can mitigate this by leveraging oversampling and undersampling techniques. Finally, you will perform clustering, train a k-means clustering model, and evaluate it using the silhouette and Davies-Bouldin scores. At course completion, you will have a good understanding of key concepts of machine learning and how to perform regression analysis, classification of data, and clustering
13 videos | 1h 43m 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 4m 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 | 47m has Assessment available 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 has Assessment available Badge

COURSES INCLUDED

AI Fundamentals
Discover the fundamental concepts of the technologies driving artificial Intelligence (AI).
10 videos | 1h 3m 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 26m 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 47m 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 | 56m 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 | 47m 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 20m 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 8m 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 | 55m 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 | 56m 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 | 38m 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 | 43m 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 | 36m 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 | 43m 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 | 26m 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 7m 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 | 41m 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 | 39m