Data generation prompts instruct language models to generate synthetic data, useful for creating datasets. Code generation prompts are used to produce code snippets or entire programs, aiding developers in coding tasks. Zero-shot prompts challenge models to respond to unfamiliar tasks, relying on their general knowledge. Few-shot prompts provide limited context to guide models in addressing specific tasks, enhancing their adaptability. You will start this course by working with data generation and code generation prompts. You will explore how to use starter code prompts, convert code from one language to another, and prompt models to explain a piece of code. Next, you will see how to leverage generative AI to debug your code and generate complex bits of code with step-by-step instructions. Finally, you will explore techniques to improve prompt performance.

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.

Combining data is a key data manipulation technique and is well supported in the pandas library. Exploratory data analysis involves data visualization to understand the relationships that exist in your data. Prompt engineering can help you pick the right visualization for viewing and understanding relationships between variables and can also generate code for these visuals. You will start this course by combining data in DataFrames learning techniques to join DataFrames using different constructs such as the inner join, left and right joins, and the full outer join. Next, you will delve into time-series analysis and visualization. You will use prompt engineering help to visualize your time series data to identify trends and patterns. Finally, you will explore data visualization in Python. You will begin by crafting univariate visualizations that display information about a single variable. You'll see that tools such as ChatGPT and Bard can help you pick the right visualizations for different use cases. You will explore bivariate visuals and use Plotly to generate interactive visualizations which are more user-friendly and intuitive.

Data manipulation involves getting your data in the right format to generate further insights. Prompt engineering allows you to specify your problem statements in natural language and generate code to meet your needs. You will begin this course by applying filters to your DataFrames in pandas. You will use logical and comparison operators to specify filter predicates and filter based on datetime data. Next, you will group and aggregate your DataFrames. You will use prompt engineering to explain your grouping and aggregation requirements and tweak generated code to tailor your solutions. Additionally, you will learn about the split-apply-combine method, a step-by-step technique for grouping and aggregation. You will then tackle data cleaning. You will remove rows with duplicate records and deal with missing values and other inaccuracies in your data. Finally, you will explore the use of pivot tables, which help rearrange and reshape data into a format more suitable for analysis.

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.

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.

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.

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.

Loops and functions are two of the most important concepts in Python programming. Loops allow you to repeat a block of code a certain number of times, or until a condition is met. Functions allow you to group together related code and reuse it throughout your program. Begin this course by exploring shallow and deep copies of data structures and performing string splicing operations in Python. Then you will use comparison and logical operators to check whether a condition is satisfied. You will evaluate conditions using prompt engineering to find solutions to your problem scenarios. Next, you will iterate over sequences using for and while loops and use the break and continue keywords. You will create and use functions in Python, discovering how to prompt generative AI tools to write functions for you in response to stubs that you provide. Finally, you will focus on first class functions and lambdas.

As the digital revolution marches forward, cloud computing and the Internet of Things (IoT) have taken center stage in transforming our world into a seamlessly interconnected ecosystem. This course dives deep into the interplay between Cloud computing, IoT, and generative artificial intelligence (AI), shedding light on their collective vulnerabilities and potential and demonstrating how generative AI can be both a formidable adversary and a powerful ally. You will begin by exploring common cloud service and IoT device vulnerabilities and how generative AI can help in either exploiting their respective vulnerabilities or protecting against such weaknesses. Then, you will then conduct a simulated hack against a cloud-based service with the assistance of generative AI. Next, you'll identify security best practices for cloud and IoT devices and evaluate security measures for cloud services and IoT devices. You'll employ generative AI to identify and mitigate IoT device vulnerabilities and you'll discover trends and advancements in cloud and IoT security and the role of generative AI. Finally, you will explore real-world case studies involving cloud and IoT hacking focusing on the potential impact of generative AI on these attacks.

In our increasingly digital world, the convergence of ethical hacking and generative AI technologies has become a crucial frontier in cybersecurity. As technology advances, so do the methods employed by hackers, making it essential for ethical hackers, or white hat hackers, to stay one step ahead. This course introduces you to the exciting world of ethical hacking and generative AI technologies. You will gain insights into the evolving cybersecurity landscape, learn about the techniques used by both malicious and ethical hackers, and explore how generative AI can be leveraged for both offensive and defensive purposes. By the end of this course, you will be equipped with a solid foundation in ethical hacking and generative AI, enabling you to understand the complex dynamics between security and innovation in the digital age.

In the digital era, boundaries are constantly being redefined. Our networks, once clear and tangible, have been blurred by the advent of cloud technology, Internet of Things (IoT), and a myriad of interconnected systems. As these boundaries shift, the methods employed to breach them also evolve. To fortify our defenses and safeguard ever-expanding perimeters, it becomes critical for organizations to harness cutting-edge advancements, such as generative artificial intelligence (AI), to envision, predict, and counteract these emerging threats. Begin by exploring the fundamentals of network and perimeter hacking, typical network attack techniques, and how these attacks can be enhanced using generative AI. You will investigate AI-assisted countermeasures, examine how AI impacts both attack and defensive posture, and assess the effectiveness of network security hardware and software. You will use generative AI for enhanced network vulnerability discovery of wired and wireless networks and conduct a simulated AI-driven attack on a wired network. Finally, you will discover why network and perimeter security is so important and assess real-world case studies involving network and perimeter hacking.

In the dynamic digital landscape, cybersecurity is more important than ever. Malicious actors are well aware that people are often the weakest link in an organization's cybersecurity posture. Ethical hackers commit significant effort into penetration testing and strategizing to combat malware and social engineering efforts executed by threat actors. This course journeys deep into the techniques and methodologies employed by ethical hackers to uncover malware and educate users in the ongoing battle against social engineering and how generative artificial intelligence (AI) can assist them in this pursuit. You'll start by exploring the types of malware and social engineering principles and techniques. With this foundation, you'll explore how generative AI can be used to help detect malware and social engineering attacks. You'll discover potential damage that can result from such attacks along with countermeasures that can be taken and then you'll explore proactive efforts to train users about social engineering attacks. You'll investigate how to use generative AI and prompt engineering to counter malware and social engineering, and you'll proceed to explore how malware and social engineering evolve over time. Lastly, you'll examine some real-world case studies that feature malware and social engineering attacks.

In today's rapidly evolving digital landscape, the convergence of ethical hacking and generative AI has emerged as a powerful force in countering cybersecurity threats. As malicious hackers adapt and exploit advanced technologies, the need for innovative defenses becomes paramount. This course explores the cutting-edge domain of generative artificial intelligence (AI) and reconnaissance techniques. In this course, you will explore reconnaissance techniques leveraging AI's potential and apply passive and active reconnaissance techniques. Next, you'll explore the challenges and solutions associated with reconnaissance and generative AI and consider the methods used to protect against it. Finally, you'll explore how ethical hackers can use the information obtained during reconnaissance.

In today's dynamic and evolving digital terrain, the need for robust cybersecurity measures has never been more critical. As technology continues to advance, hackers are constantly adapting their methods, making it imperative for ethical hackers to stay one step ahead. This course explores the convergence of ethical hacking and generative AI, specifically focusing on the scanning and enumeration phase of the ethical hacking process. You will begin by examining the network scanning phase of the ethical hacking process and how generative AI and prompt engineering enhance this process. With a solid foundation, you will then discover ways that prompt engineering can optimize scanning, the risks and benefits of using AI-assisted scanning, and how generative AI can boost various scanning techniques. Finally, you will take a look at realistic examples involving scanning and enumeration techniques, focusing on the role that generative AI plays in these scenarios.

In a world increasingly reliant on interconnected systems, safeguarding them from malicious actors is critical. Ethical hackers are key to maintaining a robust cybersecurity posture, and must commit to continuous improvement to stay ahead of malicious actors. This course provides an immersive journey into the techniques and methodologies employed by ethical hackers to protect and secure systems while focusing on generative artificial intelligence (AI) as a transformative resource in the hacking toolchain. You will start by exploring the fundamental concepts and methodologies that underpin system hacking and how generative AI can enhance hacking techniques and countermeasures. With this foundation in place, you'll investigate how to enhance attacks with generative AI, protect against system hacking by detecting vulnerabilities, use AI to drive penetration tests, and recognize the impact generative AI has on system hacking. Finally, you'll explore real-world system hacking scenarios and reflect on the transformative potential of generative AI.

This course is a comprehensive journey into the tools and techniques available to ethical hackers to discover vulnerabilities and weaknesses in web applications and databases. Particular attention will be paid to how generative artificial intelligence (AI) can help in the discovery and mitigation of attacks on these technologies, including those enhanced by integrating AI in the attack. You'll start by exploring common web application and database vulnerabilities and how they are exploited and mitigated. You'll also examine common database hacking techniques. Next, you will explore how generative AI can be applied to enhance the security of web applications and databases and you will conduct a generative AI-assisted SQL injection attack in a simulated environment. Then, you'll explore strategies to secure Web applications and databases and use generative AI and prompt engineering to detect and address vulnerabilities in a web application. You'll explore the implications of AI-enhanced Web and database hacking and consider the importance of regular updates and patches. Lastly, you'll review and assess real-world case studies of web application and database hacking and focus on the potential impact of generative AI on such attacks.