Explore Courses

In this introductory course, you'll embark on a journey into the world of generative AI and Microsoft Copilot. We'll demystify the concepts behind this transformative technology, exploring its potential and limitations. You'll gain a clear understanding of what generative AI is, how it works, and the diverse range of tasks it can perform. You'll be introduced to the specific capabilities of Copilot within the Microsoft 365 and Dynamics 365 ecosystems. We'll showcase how Copilot harnesses the power of AI and machine learning to revolutionize your productivity across various applications. From automating mundane tasks to generating creative content, you'll discover the myriad ways Copilot can enhance your workflow. By the end of this course, you'll be able to: • Recognize what Generative AI is and what it can and cannot do. • Define Copilot capabilities within the Microsoft 365 and Microsoft Dynamics 365 ecosystems. • Access and navigate the 365 Copilot platform effectively. • Recognize how to leverage Microsoft Copilot integration. • Demonstrate prompt engineering to get the most useful responses from Microsoft Copilot.

In this course, you'll model, transform, and serve data for both analytics and machine learning use cases. You'll explore various data modeling techniques for batch analytics, including normalization, star schema, data vault, and one big table, and you'll use dbt to transform a dataset based on a star schema and one big table. You'll also compare the Inmon vs Kimball data modeling approaches for data warehouses. You'll model and transform a tabular dataset for machine learning purposes. You'll also model and transform unstructured image and textual data. You'll explore distributed processing frameworks such as Hadoop MapReduce and Spark, and perform stream processing. You'll identify different ways of serving data for analytics and machine learning, including using views and materialized views, and you'll describe how a semantic layer built on top of your data model can support the business. In the last week of this course, you'll complete a capstone project where you'll build an end-to-end data pipeline that encompasses all of the stages of the data engineering lifecycle to serve data that provides business value.

The demand for technical gen AI skills is exploding. Businesses are hunting hard for AI engineers who can work with large language models (LLMs). This Generative AI Engineering and Fine-Tuning Transformers course builds job-ready skills that will power your AI career forward. During this course, you'll explore transformers, model frameworks, and platforms such as Hugging Face and PyTorch. You'll begin with a general framework for optimizing LLMs and quickly move on to fine-tuning generative AI models. Plus, you'll learn about parameter-efficient fine-tuning (PEFT), low-rank adaptation (LoRA), quantized low-rank adaptation (QLoRA), and prompting. Additionally, you'll get valuable hands-on experience in online labs that you can talk about in interviews, including loading, pretraining, and fine-tuning models with Hugging Face and PyTorch. If you're keen to take your AI career to the next level and boost your resume with in-demand gen AI competencies that catch the eye of an employer, ENROLL today and have job-ready skills you can use straight away within a week!

As Artificial Intelligence (AI) becomes integrated into high-risk domains like healthcare, finance, and criminal justice, it is critical that those responsible for building these systems think outside the black box and develop systems that are not only accurate, but also transparent and trustworthy. This course provides a comprehensive introduction to Explainable AI (XAI), empowering you to develop AI solutions that are aligned with responsible AI principles. Through discussions, case studies, and real-world examples, you will gain the following skills: 1. Define key XAI terminology and concepts, including interpretability, explainability, and transparency. 2. Evaluate different interpretable and explainable approaches, understanding their trade-offs and applications. 3. Integrate XAI explanations into decision-making processes for enhanced transparency and trust. 4. Assess XAI systems for robustness, privacy, and ethical considerations, ensuring responsible AI development. 5. Apply XAI techniques to cutting-edge areas like Generative AI, ahead of emerging trends. This course is ideal for AI professionals, data scientists, machine learning engineers, product managers, and anyone involved in developing or deploying AI systems. By mastering XAI, you'll be equipped to create AI solutions that are not only powerful but also interpretable, ethical, and trustworthy, solving critical challenges in domains like healthcare, finance, and criminal justice. To succeed in this course, you should have experience building AI products and a basic understanding of machine learning concepts like supervised learning and neural networks. The course will cover explainable AI techniques and applications without deep technical details.

As Artificial Intelligence (AI) becomes integrated into high-risk domains like healthcare, finance, and criminal justice, it is critical that those responsible for building these systems think outside the black box and develop systems that are not only accurate, but also transparent and trustworthy. This course is a comprehensive, hands-on guide to Interpretable Machine Learning, empowering you to develop AI solutions that are aligned with responsible AI principles. You will also gain an understanding of the emerging field of Mechanistic Interpretability and its use in understanding large language models. Through discussions, case studies, programming labs, and real-world examples, you will gain the following skills: 1. Describe interpretable machine learning and differentiate between interpretability and explainability. 2. Explain and implement regression models in Python. 3. Demonstrate knowledge of generalized models in Python. 4. Explain and implement decision trees in Python. 5. Demonstrate knowledge of decision rules in Python. 6. Define and explain neural network interpretable model approaches, including prototype-based networks, monotonic networks, and Kolmogorov-Arnold networks. 7. Explain foundational Mechanistic Interpretability concepts, including features and circuits 8. Describe the Superposition Hypothesis 9. Define Representation Learning and be able to analyze current research on scaling Representation Learning to LLMs. This course is ideal for data scientists or machine learning engineers who have a firm grasp of machine learning but have had little exposure to interpretability concepts. By mastering Interpretable Machine Learning approaches, you'll be equipped to create AI solutions that are not only powerful but also ethical and trustworthy, solving critical challenges in domains like healthcare, finance, and criminal justice. To succeed in this course, you should have an intermediate understanding of machine learning concepts like supervised learning and neural networks.

As Artificial Intelligence (AI) becomes integrated into high-risk domains like healthcare, finance, and criminal justice, it is critical that those responsible for building these systems think outside the black box and develop systems that are not only accurate, but also transparent and trustworthy. This course is a comprehensive, hands-on guide to Explainable Machine Learning (XAI), empowering you to develop AI solutions that are aligned with responsible AI principles. Through discussions, case studies, programming labs, and real-world examples, you will gain the following skills: 1. Implement local explainable techniques like LIME, SHAP, and ICE plots using Python. 2. Implement global explainable techniques such as Partial Dependence Plots (PDP) and Accumulated Local Effects (ALE) plots in Python. 3. Apply example-based explanation techniques to explain machine learning models using Python. 4. Visualize and explain neural network models using SOTA techniques in Python. 5. Critically evaluate interpretable attention and saliency methods for transformer model explanations. 6. Explore emerging approaches to explainability for large language models (LLMs) and generative computer vision models. This course is ideal for data scientists or machine learning engineers who have a firm grasp of machine learning but have had little exposure to XAI concepts. By mastering XAI approaches, you'll be equipped to create AI solutions that are not only powerful but also interpretable, ethical, and trustworthy, solving critical challenges in domains like healthcare, finance, and criminal justice. To succeed in this course, you should have an intermediate understanding of machine learning concepts like supervised learning and neural networks.

Begin your journey into Natural Language Processing (NLP) with an introduction to text data and encoding techniques, delving into the intricacies of regular expressions through extensive practice and use cases. Progress to lexical processing, learning to handle stopwords, split words, and implement bag-of-words and Tf-IDF models, applying these techniques to tasks like spam detection through detailed case studies. Advance to sophisticated lexical processing topics like spelling correction models and the Soundex algorithm, exploring practical implementations via Levenshtein Distance and spell correctors, and tackling challenges such as handling combined words like "New Delhi." This section solidifies your ability to preprocess and clean text data effectively. Transition to syntactic processing, covering parsing and grammar for English sentences, intermediate topics like stochastic parsing, the Viterbi algorithm, and Hidden Markov Models, reinforced through case studies and practical applications. Finally, tackle advanced syntactic processing techniques, including CFG grammar, top-down and bottom-up parsing, and probabilistic approaches like PCFG, concluding with a real-world project on information extraction through a comprehensive case study on ATIS flight reservations. Designed for aspiring NLP practitioners, data scientists, and software engineers, this course enhances understanding of syntactic processing, with a basic knowledge of Python programming and familiarity with machine learning concepts recommended.

ChatGPT is a language generation model developed by OpenAI, with its latest version, Davinci, powering the ChatGPT program. This course aims to teach how to build applications and solutions using ChatGPT by understanding OpenAI and ChatGPT APIs. Students will learn to create serverless functions that handle ChatGPT prompts and responses, integrating these into various platforms. The course covers connecting to the OpenAI API, creating Azure functions, and developing applications such as CoverLetterCreator, StockPhotoPPT, PolitelySayNo, and TravelPlan. By the end, learners will be able to increase productivity and build real-life applications using ChatGPT and other OpenAI models like DALL-E. They will integrate the API into platforms like Outlook, Power Apps, Power Automate, Airtable, and Bubble. The course is ideal for managers, employees, and individuals looking to improve team efficiency or monetize applications using ChatGPT. Learners will integrate ChatGPT into business tools, create no-code apps, plan itineraries, generate cover letters, and produce stock photos for presentations.

Unlock the power of deep learning and elevate your machine learning skills with our comprehensive deep neural networks course. This hands-on program covers deep learning fundamentals, including artificial neural networks, activation functions, bias, data, and loss functions. Learn Python basics focused on data science, and master tools like Matplotlib, NumPy, and Pandas for data cleaning and visualization. Progress from the MP Neuron model to the Perceptron, Sigmoid Neuron, and Universal Approximation Theorem, exploring ReLU and SoftMax activation functions. Gain practical experience with TensorFlow 2.x, creating and training deep neural networks, evaluating their performance, and fine-tuning for optimal results. By the course's end, you'll be on your way to becoming a deep-learning expert. This beginner-friendly course is perfect for students and professionals aiming to stay updated on AI. A basic understanding of programming is recommended but not required, as fundamental Python skills are covered in the course.

In this course, you'll master Selenium, a powerful tool for automating web applications. Beginning with setting up your first web application, you'll dive into Selenium locators, learning to efficiently locate web elements using strategies like ID, name, and tag name. You'll explore TestNG for project setup, debugging, and test slowing for better visualization. The course covers handling form elements such as text fields, checkboxes, radio buttons, and select boxes, creating robust automation scripts that mimic real-world user interactions. Advanced topics include window navigation, alert handling, and WebDriver's manage window methods for effective automation. You'll gain hands-on experience with implicit and explicit waits, crucial for dynamic web applications. The course also covers cross-browser testing and data-driven testing, guiding you through setting up projects for different browsers, running headless tests, and implementing data-driven approaches using CSV and Excel files. By the end, you'll understand the Page Object Model, Selenium Grid, and standalone server, equipping you to scale your automation tests effectively. Designed for QA professionals and software testers with basic web technology knowledge, the course enhances skills in automated testing using Selenium, with a fundamental knowledge of Java recommended.

This course is your gateway to mastering Android game development with Unity 2022. You'll start by learning how to set up Unity and develop a basic understanding of the platform. From there, you will dive into building a simple yet engaging 3D endless runner game, learning how to create game levels, control player movement, and handle game mechanics such as platform spawning and object destruction. Next, the course takes you on an exciting journey to develop a 3D zigzag game, guiding you through advanced mechanics like player control via touch input, spawning platforms with random algorithms, and setting up dynamic in-game events such as camera movement and particle effects. You will also create a polished user interface, including score tracking, game over screens, and animations that give your game a professional touch. The final section will teach you how to build, optimize, and publish your games on the Android platform. In addition to coding and game mechanics, you’ll also learn to create your own game art assets from scratch using Inkscape. The course covers everything from sprite design for games to exporting assets for use in Unity. By the end, you’ll have hands-on experience building and publishing multiple Android games, and be equipped with the knowledge needed to start your own game development projects. This course is designed for beginners in game development who are familiar with basic programming concepts. No prior experience with Unity or game development is required, but basic knowledge of C# will be helpful.

This course starts with the theoretical concepts and fundamental knowledge of recommender systems, covering essential taxonomies. You'll learn to use Python to evaluate datasets based on user ratings, choices, genres, and release years. Practical approaches will help you build content-based and collaborative filtering techniques. As you progress, you'll cover necessary concepts for applied recommender systems and machine learning models, with projects included for hands-on experience. Key learnings include AI-integrated basics, taxonomy, overfitting, underfitting, bias, variance, and building content-based and item-based systems with ML and Python, including KNN-based engines. The course is suitable for experience for beginners and those with some programming, aiming to advance ML skills and build customized recommender systems. No prior knowledge of recommender systems, ML, data analysis, or math is needed, only basic Python. By the end, you'll relate theories to various domains, implement ML models for real-world recommendation systems, and evaluate them.