Post Graduate Program in Artificial Intelligence for Leaders Leverage the power of AI without coding and become an impactful business leader. 4 Month Online + Live Virtual Sessions Download Brochure Apply Now Application Closes Tomorrow Enquire: +1 512 883 2893 4.8/5 Trustpilot 97% Program Satisfaction 4.81/5 Course Report *Ratings given by our worldwide learners to Great Learning Top-Ranking AI Program, in Collaboration with: #3 MS - Business Analytics QS World University Rankings, 2022 #6 Executive Education - Custom Programs Financial Times, 2022 What Sets the PGP-AIFL Apart Comprehensive Curriculum Designed to cover business-relevant and strategic facets of AI Learn through a uniquely crafted no code approach towards leveraging Artificial Intelligence for business Read More Live Sessions with Industry Experts 50+ hours of live sessions with experts to build industry context 9+ case studies and multiple industry applications to form a strategic understanding of AI Read More Hands-on Training 5 projects and 1 capstone project, with personalised feedback from experts 8+ hands-on exercises to master practical foundations Read More Build sound foundations in AI for business: Machine Learning, Neural Networks, Deep Learning, and more 50+ hours of live sessions with experts to build industry context 5 projects, 1 capstone project, and 8+ hands-on exercises for practical training 9+ case studies and multiple industry applications to form a strategic understanding of AI What is the No Code Approach? There is significant interest in studying and utilizing machine learning and AI techniques across a variety of businesses. New no-code platforms are designed to enable various industries to create solutions, that would have previously required programming, using intuitive, interactive user interfaces allowing users to quickly classify information, perform data analysis, and create accurate data predictions with models. Business leaders can leverage the no-code tools to make better predictions and improve their decision-making. Here is the tool you will learn to use in this program: KNIME Software links dashboards with an easy-to-use analytics platform. The software is designed to give data experts the edge they need to drive business growth, using the latest AI and Machine Learning techniques. Strategic AI Program for Business Leaders & Senior Managers 4 months | Online Mentorship Download Brochure Certificate from The University of Texas at Austin All certificate images are for illustrative purposes only. The actual certificate may be subject to change at the discretion of the University. KUMAR MUTHURAMAN Faculty Director, PGP-AIFL H. Timothy (Tim) Harkins Centennial Professor Faculty Director, Center for Research and Analytics MS & PhD: Stanford University For any feedback & queries regarding the program, please reach out to us at MSB-AIML@mccombs.utexas.edu
Curriculum Curriculum designed by UT Austin-McCombs Covers the most business-relevant technologies and diverse industry applications to turn you into an AI-empowered leader. Our curriculum has been carefully-crafted to provide you with the breadth and depth you need to make smarter business decisions, lead AI teams, and devise an AI strategy for your organization. Accordingly, it covers the most business-relevant technologies and diverse industry applications to turn you into an AI-empowered leader. Download Curriculum Understanding AI through Data (1 Project, 1 Case Study) Think of this as a foundation course. We believe in building strong foundations and this module has been designed as a refresher to AI. We will be taking you through some of the jargons, terms and news around AI. The first module completely focuses on understanding AI through the given data. As data is the primary source of achieving AI, you will gain insights with regards to data and understand how to execute AI operations. Week 1 – Business of AI In the first week of the first module, you would learn about the business of Artificial Intelligence. By the end of the week, you would have gained various perspectives and warmed up gradually. Welcome to Career 2.0 Introduction to Artificial Intelligence Let’s go back to basics with this submodule,You would gain a basic understanding of AI and common keywords used in industries today. Explosion in AI This module will help you appreciate the magnitude of Artificial Intelligence. You would understand how AI is exploding in several industries like Financial Services, Pharmaceuticals, and Customer Experience. Business application and its limitations In this submodule, you would learn about the several business applications of AI & its limitations. Building AI project Let’s increase our pace, shall we? You would now learn the techniques and methodologies involved in building AI projects. ROI Calculation You will learn to appreciate AI from various angles like cost-benefit. As a leader in AI, you need to know the metrics involved. In this submodule, you would learn about the several methods and techniques involved in the calculation of return on investment. Case Study The most awaited part of this course is already here. Your first case study is already here. You would also partake in a case study session guided by experts. Congrats on completing your first week. Week 2 – Data Visualization using KNIME Data Visualisation is an important technique that needs to be pursued by AI project managers and AI leaders. Data Visualisation refers to the tools and techniques employed to represent data in a graphical format. You would learn the data visualization techniques in this specific sub-module of The Artificial Intelligence for leaders program. By the end of this program, you will be able to understand the working of KNIME Studio and also will now be comfortable around various forms of data and representation of it visually. This module is highly interesting and you will enjoy learning the new tools. What is Data? Since data is the primary source of AI, you would learn about data Numerical and Textual data In this sub-module, you would learn about numerical and textual data. Graphs & Networks You would master the methodologies and techniques involved in graphs and networks Time series data Time series data is a collection of observations for a particular subject at varying time intervals Different types of data objects In this sub-module, you would learn about different types of data objects Understanding Visual metrics – Mean, Median & Mode Virtual metrics are incredibly used in the application of AI and they contribute so much to any business. Introduction to KNIME You would gain a basic understanding of cloud platforms using KNIME. Visualizing data using KNIME studio You would master KNIME to visualize the data. Data Manipulation using KNIME studio You would also learn Data manipulation using KNIME. In this module, you would work on a project and participate in case study analysis to enhance your understanding. Supervised Learning (1 Project, 3 Hands-on, 5 Case Studies) Supervised learning is a type of Machine Learning that maps an input to an output using a labeled training dataset. You would master the concepts of Supervised Learning in this module. Another week is done already! You will have now started understanding how truly transformative AI is. In case you are falling behind, or have been having a paucity of time, do contact your program advisors and mentors. Week 3 – Regression This week, you will meet some mathematical models like regression and learn more about statistics and numbers. Watch closely and ask questions, because this module is very important for what comes next. As an AIML Leader, you will be hearing these terms in data reports frequently. Regression employs mathematical methods that assist AI professionals to forecast accurate outcomes and make wise decisions considering the value of one or more predictor variables. You would master several regression techniques throughout this module of the Artificial Intelligence for leaders course. Below are the various concepts you would learn in this module. Introduction to Regression You would gain a basic understanding of Regression. Linear Regression Linear regression refers to the implementation of a linear function to a set of input-output pairs given a set of training examples where input and output features are numeric. Multivariate Linear regression Multivariate regression is a procedure that evaluates a single regression model with more than one outcome variable. Categorical Independent variable in regression A categorical variable is a statistical approach of AI. You would learn in this module. Root Mean square error and Mean Absolute error Decoding the root Mean square error and Mean Absolute error. Linear Regression – Pros & Cons Evaluate the pros and cons of Linear regression in AI. Hands-on using KNIME Let’s put those skills to action in the lab. You would participate in hands-on lab sessions using KNIME. Case study session with experts Each week closes with a case study to help you compile all your thoughts. You would partake in a case study session guided with experts. We suggest you ask as many questions as possible and leave no doubts unanswered. Week 4 – Classification You are now going to finish one-third of the program. By this time you have a stronghold on the basics and can understand what is happening. Brace yourself, we are going to power ahead now. Enjoy your hands-on lab sessions using KNIME. Classification in Machine Learning refers to the identification of the category of the given problem. You would master several techniques of classification in this specific sub-module of AI for business leaders course. As a leader in AI, you will be expected to understand and evaluate business models. We will be taking you through some of them to help you build your own! Introduction to classification You would gain a basic understanding of classification. Logistic regression Linear Regression is applied to predict the outcome that is continuous and has a constant slope within a continuous range. Setting up threshold In this module, you would learn to set up a threshold. Performance measures – Precision & Recall In this module, you would master several performance measures such as Precision and Recall. Evaluation of models One of the most important sessions. You would learn several techniques to calculate the accuracy of the machine learning model. Hands-on using KNIME Enjoy your hands-on lab sessions using KNIME. Case study session with experts By this time, you would be just as excited about the case studies as we are. These case studies are a unique opportunity to understand real life scenarios and the industry. Week 5 - Building POC for AI Projects One-third of the program is already done. Be proud of yourself. This week will be about leadership decisions, documentations, and managerial concepts. Time to awaken the business acumen. This module focuses on building the Proof of Concept ( PoC) for AI projects. Below are the various concepts you would learn in this module. Building POC – Outline This module focuses on building the proof of concept ( PoC) for AI projects. Solution at a glance In step 2, you would master the techniques employed to derive the solution at a glance. Market potential Understanding the Market potential is a vital step and we help you identify the various ways. Threats & Opportunities Learn to do a SWOT analysis of your projects in this module. Requirements – Data & People In this submodule, you would learn about evaluating the requirements of data and people to build an AI project. Product Development Roadmap A product development roadmap defines the summary of the proposed work and events included throughout the delivery of the output. Expansion plan After set-up comes expansion. With this module, you would master the techniques employed to expand the AI plan. AI techniques & their relevance to domains In this module, you would master the techniques of AI and their relevance to other domains. Identifying AI use cases In this submodule, you would master several techniques of Identifying AI use cases. Tips for building successful AI product In this bonus module, you would master some great techniques and tips employed for building a successful AI product. In this module, you would work on a project and participate in 3 hands-on lab sessions besides working on 5 case studies to enhance your understanding. Neural Networks & Ensemble Techniques (1 Project, 3 Hands-on) Neural Networks & Ensemble techniques assist you to employ the effective application of artificial intelligence in business management. Neural networks are often the core of applications and as a project manager or a leader you will need to know about some important concepts. Week 6 – Neural Networks Neural networks are a series of algorithms developed inspired by the biological neural networks designed to mimic the human brain in performing quick actions. Below are the various concepts you would learn in this module of the Artificial Intelligence Course. Introduction to Neural Networks In this submodule, you would gain a basic understanding of Neural Networks. Activation function Activation functions are applied to an artificial neuron to deliver outputs considering the given inputs. Feed forward neural network In this submodule, you would learn the techniques of feed-forward neural networks. The topology of a neural network In this submodule, you would learn about the topology of a neural network. Error & Loss function In this submodule, you would learn about the error and loss functions of a neural network. Training a neural network In this submodule, you would learn to train a neural network. Optimizing a neural network In this submodule, you would learn several techniques used to optimize a neural network. Hands-on using KNIME You would participate in hands-on lab sessions using KNIME and gain Hands-on expertise. Week 7 – Ensemble Techniques You are now almost halfway across the program and the pace has now increased. You will now be dealing with detailed concepts and more practical-based learning. Ensemble techniques are applied to obtain enhanced predictive performance than the models obtained by using any other learning algorithms. Below are the various concepts of Ensemble techniques you would learn in this module of AI for business leaders course. Introduction to Decision trees Decision trees are used to analyze the models as they facilitate effective decision-making. CART The CART algorithm is designed as a sequence of problems, the answers to which defines what would be the next question, if there exists any. Pruning Pruning is applied to checkout each note of a game tree which facilitates computing the correct minimax decision. Ensemble techniques You would learn several ensemble techniques in this submodule. Random Forest Random Forests are a group of decision trees used for classification, regression and more. Random Forests are ensemble learning methodologies. Hands-on using KNIME You would also gain Hands-on expertise working with KNIME. Case study session with experts You would also partake in an interesting case study session guided by experts. Throughout this module, you would work on one project and 3 hands-on lab sessions that help you gain hands-on expertise on the tools and techniques learned. Your mentors and program managers will be right next to you, ensuring you keep powering ahead. Unsupervised Learning (1 Project, 3 Hands-on, 1 Case Study) Unsupervised learning is applied to discover patterns or grouping data points without the intervention of humans. Unsupervised learning is used to analyze and cluster unlabeled datasets. Week 8 – Clustering & Dimensionality reduction Clustering is a technique applied to group a set of data points and classify them accordingly. Introduction to Clustering Clustering is a term you would have heard often in AI, let’s decode it. Types of clustering You would learn the different types of clustering techniques. K Means clustering K clustering is an unsupervised algorithm employed to assign each data point to any of the K groups considering the features specified. Importance of scaling Applications of clustering In this submodule, you would learn the several applications of clustering techniques. Advantages & Disadvantages of clustering In this submodule, you would learn the advantages & disadvantages of clustering. Visual analysis In this submodule, you would learn about the techniques involved in visual analysis. Hands-on using KNIME You would participate in hands-on lab sessions using KNIME. Week 9 – Building AI teams & driving data culture This module of the AI for managers program focuses on teaching the tools and techniques used to set up AI teams and drive AI culture. This module assists you to regulate the effective application of artificial intelligence in management. You will find this module extremely useful and interesting as it discusses managerial concepts and leadership. Below are the various concepts you would learn in this module - AI for management. Service Vs product companies In this submodule, you would learn about service and product companies, the differences between them and more. AI Team composition In this submodule, you would learn the specifications of building an AI team. Centralized vs Distributed AI teams You would learn the differences between centralized vs distributed AI teams. How to keep your team motivated? You would master the techniques to keep the team motivated. Handling resistance from senior management In this submodule, you would learn the techniques to handle resistance from senior management. Coaching others You would also gain knowledge used to coach others in an AI team. Managing portfolio of projects In this submodule, you would learn the techniques to manage a portfolio of various AI projects. Scaling AI teams In this submodule, you would learn the techniques employed in Scaling the AI teams. Week 10 – Recommendation systems As one of the most common applications of AI, we strongly recommend you take recommendation systems seriously. Most projects across all domains use recommendation systems to study the user's past actions and propose the most accurate recommendations considering their preferences. These recommendations enhance the user's experience. Below are the various concepts of recommendation systems you would learn in this module. Introduction to Recommendation systems You would gain a basic understanding of Recommendation Systems. Content based filtering You would learn the techniques of content-based filtering of the recommendations. Collaborative filtering You would learn the techniques of collaborative-based filtering of the recommendations. Similarity measures Similarity measures define the real-valued function that quantifies the similarity between two objects. Case study You would also partake in a case study session guided by experts. Hybrid Systems Hybrid systems combine neural networks and AI to detect the patterns in a given dataset. They are rapidly employed in solving the problems of deep learning. Hands-on using KNIME In this submodule, you would gain hands on expertise on KNIME. Case study session with experts You would also partake in a case study session guided with experts on subjects like Netflix and Amazon which use recommendation systems to produce customer satisfaction. Throughout this module, you would work on a project and 3 hands on labs besides working on a case study. Deep Learning – CV & NLP (1 Project, 3 Hands-on, 9 Case studies) As we are almost towards the end of our program, we take you towards one of the most important subsets of Machine Learning- Deep Learning. Deep learning systems imitates the acts of the human brain. The structure of neurons is inspired by the neurons of the human brain. Week 11 - Natural Language Processing (NLP) Natural language processing employs computational linguistics to develop real-world applications that run with languages of fluctuating structures. NLP deals with training a system to learn a language and understand it. Below are the various concepts of NLP you would learn in this module. Introduction to NLP You would gain a basic understanding of Natural Language Processing. Different tasks in NLP In this submodule, you would learn about the different tasks executed in NLP. How are NLP problems solved? In this submodule, you would learn how to solve the various NLP problems. Text extraction/ web scraping You would learn the techniques of text extraction and web scraping. Building a model You would learn to build an NLP model. Case study – Sentiment analysis You would also partake in a case study performed on Sentiment analysis. NLP Demonstration on Sentiment analysis Hands-on using AWS You would participate in hands-on lab sessions using AWS. Case study session with experts You would also partake in a case study session guided by experts. Week 12 – Computer Vision (CV) Computer Vision is a trending application of Artificial Intelligence. Computer Vision is the technique of training the systems to process data from the images. In this submodule, you will learn to process the images for Image classification employing Neural Networks. Introduction to Computer Vision You would gain a basic understanding of Computer Vision. Types of CV problems You will learn about several types of CV problems and the techniques to solve them. Pixel A pixel in computer vision refers to the numerical value assigned to one specific point of the picture. How does the computer see an image? You would learn how the computer sees an image with regard to computer vision. 3D images You would gain an in-depth understanding of the CV of 3D images. Resolution Resolution in computer vision refers to the number of pixels that exist in a display monitor. Resolution refers to the sharpness of the image in a display. Convolution & Pooling Convolutional layers in a convolutional neural network compile the behavior of features in an input image and Pooling layers provide access to downsample the future maps. Convolutional Neural Networks Convolutional neural networks deep learning algorithms, made of neurons that receive inputs that assign importance to each of the objects of the given image and differentiate from one another. Hands on using AWS You would participate in hands-on lab sessions using AWS. Case study session with experts You would also partake in a case study session guided by experts. Throughout this module, you would work on a project and 3 hands on labs besides working on 9 case studies spanning various industries and domains AI in Practice (1 Project) By this time, you would be proficient in Artificial Intelligence and Machine learning.You will be able to appreciate it from various angles and hence ready to take on a role as an AI Practitioner, project manager, or leader. This module will now summarise all your project setup and installation. You are at the final rung of the race. Week 13 – Jumpstarting AI Transfer Learning Transfer learning is a machine learning research application that concentrates on storing the insights obtained while solving the given problem. How it works You would gain an in-depth understanding of the functionality of Transfer Learning. Applications of Transfer Learning – Advantages vs Disadvantages In this submodule, you would learn about the various applications of Transfer Learning, its advantages, and disadvantages. Dealing with Imbalanced data - Data Augmentation AI and ML require to deal with a lot of imbalanced data. In this submodule,, you would learn several techniques applied in dealing with imbalanced data. You would learn the process of Data Augmentation. Data Augmentation types Data augmentation is a strategy that empowers AI professionals to enhance the variety of existing or available data for training models, without collecting new data. In this submodule, you would learn different types of Data Augmentation types. Model Deployment In this submodule, you would learn the techniques and methodologies applied for the deployment of a Machine Learning model. Modes of training You would master the several modes of training techniques and methodologies in this submodule. Serialization Serialization is a technique applied in Artificial Intelligence for transforming objects into a structured format that can be stored in a file. Model Monitoring & Recalibration In this submodule, you would master several techniques of model monitoring & recalibration. Throughout this module, you would work on a project that helps enhance your understanding of the concepts learned. This submodule was designed to help you deploy your AI base projects and oversee a successful implementation.You would now have a proper overview of AI projects and their stages from the beginning to deployment Week 14 – Break Self-paced Module Gain an understanding of what ChatGPT is and how it works, as well as delve into the implications of ChatGPT for work, business, and education. Additionally, learn about prompt engineering and how it can be used to fine-tune outputs for specific use cases. Demystifying ChatGPT and Applications Overview of ChatGPT and OpenAI Timeline of NLP and Generative AI Frameworks for understanding ChatGPT and Generative AI Implications for work, business, and education Output modalities and limitations Business roles to leverage ChatGPT Prompt engineering for fine-tuning outputs Practical demonstration and bonus section on RLHF Capstone Project Candidates work on a capstone project which is considered as the cornerstone of their learning journey. The capstone project encompasses all tools and techniques of AI that are mastered throughout the course. The capstone project demands the rigorous application of all tools of AI. Certificate of Completion from The University of Texas at Austin Upon the successful completion of the course, candidates gain a certificate from the prestigious University of Texas at Austin. Languages and Tools covered Hands-on Projects Find below an indicative list of Hands-on projects our learners go through the course of the program. 1000+ Projects completed 22+ Domains EDA, Visualization, Transport Services Predicting the fluctuations in demand patterns for a Bike Sharing company The project involves using visualization techniques to derive valuable insights about the data to help predict and cater to the fluctuating bike sharing demand patterns. Learn more Applications of AI, POC, & Market Analysis Ideating steps involved in conceptualizing an AI product The project involves coming up with an AI project and building its POC, including the problem statement, proposed solution, validation metrics, market analysis, and timelines for implementing the project. Learn more Classification, Regression, BFSI Define a loan approval and loan pricing strategy to minimize loss The project involves building classification and regression models to redefine the loan approval and loan pricing strategy to minimize loss due to loan default for an auto finance company. Learn more Building AI teams Ideating steps to build an AI team The project involves making a plan and building a team that can implement the AI project in the organization, including product details, organization type, data science ecosystem, execution, and operations. Learn more Ransomware attacks Ransomware Threat Reports Learners will perform threat research. Learn more Faculty and Industry Experts 20+ Professors 2500+ Industry Mentors 2 Award winning faculties Learn from leading academicians in the field of Artificial Intelligence and Machine Learning and several experienced industry practitioners from top organizations. Dr. Kumar Muthuraman Faculty Director, Centre for Research and Analytics Randhir Agarwal Director, Data Science Sunil Kumar Vuppala Director-Data Science Karthikeyan Sankaran Chief Technology Officer Balaji Chandrasekaran Senior Data Scientist Srinivas Atreya Chief Data Scientist The Great Learning Advantage Learn the business implications of AI through a program that combines recorded lectures from distinguished academicians and live sessions with industry experts to build a practical grounding in AI. Benefit from hands-on training, personalised support, and networking opportunities to prepare for success. The program is distinguished by its unique combination of recorded videos, live learning sessions, hands-on training, and extensive program support. BUILD YOUR AI EXPERTISE Learn AI from Industry Leaders Recorded content from distinguished academicians, combining conceptual understanding with use cases and demos Mentored learning sessions with AI practitioners, focusing on doubt-resolution and case-study based practice Industry case sessions by experts at partner companies to understand applications to diverse industries GAIN REAL-WORLD INSIGHTS Hands-on, Practical Training 5 case-based projects that don’t require programming experience 9+ case studies and 8+ hands-on exercises to master practical foundations 1 team-based capstone project to conceptualize an AI solution for a stated business need TAILORED LEARNING EXPERIENCE Personalized Support and Guidance Dedicated Program Manager to track your learning progress Constant feedback, nudges, and motivation to ensure your success Quick and personalized resolution of all academic and non-academic queries Who is the program For? AI for leaders is an extensive online program for Business Leaders To leverage the power of AI in building products and services Operations, Delivery and Product Managers To successfully and swiftly launch a product Entrepreneurs and Consultants To build working prototypes without needing large data teams Working Professionals and Marketers To lead AI and data-driven teams and build innovation initiatives using AI technologies View Cohort Profile Hear what our learners have to say I can already envision specific projects that I'm starting to deploy at work as a result of enrolling in this program and learning much more about how AI functions and how to apply it effectively. Gregory Thompson Vice President Supply Chain, Carnival Cruise Line This program's curriculum and content are really well organized throughout. I am very impressed by all of the bite-sized modules and video lectures that made it easier to understand each technology's underlying principles. Tauhid Abddul Jalil Principal Solutions Consultant, Laiye Career Success Stories from our Empowered Learners Hugh Hanlon Defense Research Analyst-Associate The course was flexible and increased my understanding of AI and the processes for integrating it into business. I would highly recommend this course to business professionals Revathi Thiruvengadam Product Manager This incredible course has helped me gain more knowledge and employ new techniques in my current job and future jobs to come. The course was well structured and planned. Sreeram Bonde Director AIFL program is conducted very methodically and has relevant content. The content is organized really well and the learning gets evolved with every course. Nancy P Jani Sr. Vice President The program manager had been phenomenal during the entire learning journey, always proactively reminding us of deadlines. I highly recommend Great Learning to friends and colleagues. Sandeep Bisawa Executive Director, Client Coverage (Performance Management & Analytics) This is one of the best courses I have gone through since I graduated. I can see a big difference in my knowledge of AI. NC Nagarajan Senior Director of IT Strategy Overall a well-executed program from Great Learning .The knowledge and the command on the subject were self-evident. The mentors also checked periodically for feedback which was good too. Dipesh Naigaonkar Principal Consultant (Finacle) I joined the program to gain a structured academic perspective of the domain. It provides students the ability to ask the right questions and discern business relevance. Ramakrishnan Srinivasanr Vice President (Head of Regulatory Projects) The program has given me a very good insight and confidence on AI and its practical application in solving problems. It provides a good platform to clarify doubts with practical examples. Raghavendra P Setty Vice President The program has been a great learning experience with right levels of depth and breadth across the subject. The mentor sessions provided a look into real-world applications. Ashish Roy Barman Delivery Partner The program remarkably provied the required depth and understanding of AI/ML concepts from a strategic management perspective. It was very well structured with training videos from esteemed faculty members. Chris Hegeman Director of Marketing Analytics & Business Intelligence (Embedded Technology/ IoT) The program was thoughtfully designed to include a high level of exposure to experts practicing in the field. The pace of the class was fast enough to cover primary categories of AI and ML. Carlos Henrique Ribeiro Fernandes Software Developer and Solution Modeler The course is very well organized and adapted to students' needs. The teachers were very professional and committed. It was a good decision to choose this institution. Sridhar Santhanagopalan Head Of Central Procurement Center (Global Business Service) I would recommend this course to any leader who wants to understand the concepts of AI and see how to drive value out of AI project implementation. Ken Mugo Regional IT Manager, Africa I genuinely appreciate the mentorship team of Great Learning. The presentation was great and focused on real-world problems while covering them in-depth too. Download Brochure Learner Feedback on Mentorship and Program Support All Reviews Alexander Peralta 23 Jan 2023 Batch of July 2022 | Director, Software Engineering at Salesforce | United States Good information and clarity on the subject. The instructor had a mastery of the subject and was able to explain it with good and clear examples. Travis David Luttrell 23 Jan 2023 Batch of July 2022 | United States Extremely good session for AI Leaders! While I appreciate the "how to" for KNIME, it is sessions like this that I was expecting when I enrolled. Ricardo Artur Ribeiro 23 Jan 2023 Batch of March 2022 | Chief Transformation Officer at Montepio Crédito | Portugal A valuable session that was able to structure the subject quite well in my mind. Moreover, the examples, based on problems to address, for each machine learning category helped me to understand better the applications in the real world. Really very good session. Ricaurte Santamaria 23 Jan 2023 Batch of September 2021 | Senior Manager Reliability at Dp World | United Arab Emirates The computer vision concept normally is a difficult one to send across, but I'm sure that my colleagues will agree that the session today was fantastic with a detailed explanation about how it works, and with practical applications. Well done! Read all reviews Program Fees Post Graduate Program in Artificial Intelligence for Leaders 2,950 USD Payable in installments. Pay upfront and get a 150 USD discount. Certificate from High-quality learning content from UT Austin & Global Faculty 5 Hands-on Projects 1 Comprehensive Capstone Project Weekly Mentored Learning Sessions with Industry Experts 5+ Industry Case Sessions by Industry Practitioners Personalized Academic & Non-Academic Support Apply Now Apply Now Admission Process 1 Complete your Application Register by filling up the online application form. 2 Application Screening A panel will review your application to determine your fit with the program. 3 Join the Program If selected, you will receive an admission offer for the upcoming cohort. Secure your seat by paying the fee. Upcoming Application Deadline Admissions are closed once the requisite number of participants enroll for the upcoming cohort . Apply early to secure your seat. Deadline: Tomorrow Apply Now Reach out to us We hope you had a good experience with us. If you haven’t received a satisfactory response to your queries or have any other issue to address, please email us at help@mygreatlearning.com Cohort Start Date Online 18th Nov 2023 Frequently Asked Questions Program Details What is meant by “mentored learning sessions”? Mentored learning connects you directly with industry experts and practitioners of AI, Machine Learning, and Deep Learning through live virtual sessions. These sessions happen on weekends in small groups of learners and help you understand the practical side of concepts, gain industry context, and make sense of difficult concepts. What is the role of The University of Texas at Austin in PGP – Artificial Intelligence for Leaders Program? The PGP-Artificial Intelligence for Leaders curriculum has been designed by McCombs School of Business at The University of Texas at Austin with the learning content and assessments created by faculty of The University of Texas at Austin and other practising Artificial Intelligence experts. Upon completion, all successful participants get a certificate from The University of Texas at Austin. Will the content be available after the program is completed? We believe that learning is continuous and hence all learning material – lecture notes, online content and supporting material – will be available through the online platform for 1 years after completion of the program. How will I be evaluated during the program? In this holistic and rigorous program, you will be evaluated continuously. All quizzes, assignments, attendance and projects are used to evaluate and monitor your progress towards the desired learning outcomes. What are “Industry Case Sessions”? Industry case sessions are led by AI practitioners working at a variety of partner companies. Case studies include the application of Al techniques to multiple industries, including financial services, healthcare, retail, e-commerce, among others. A typical industry case session walks learners through a real-life use case, and explores the choice of models and concomitant trade-offs. Will I receive a transcript or grade sheet after completion of the program? No, PGP-AIFL is an online professional certificate program offered by the McCombs School of Business in collaboration with Great Learning. Since it is not a degree/full-time program offered by the university, therefore, there are no grade sheets or transcripts for this program by the university. You will receive marks on each assessment to test your understanding and marks on each module to determine your eligibility for the certificate. Upon successful completion of the program, i.e. after completing all the modules as per the eligibility of the certificate, you are issued a certificate from the McCombs School of Business at the University of Texas at Austin. What is the required weekly time commitment? Each week involves around 2-3 hours of recorded lectures and an additional 2-hour live virtual class/industry mentor session each weekend, which includes hands-on practical applications and problem-solving. The program also involves around an hour of practice exercises or assessments each week. Additionally, based on your background, you should expect to invest 2 to 4 hours every week in self-study and practice. So, that amounts to a time commitment of 8-10 hours per week. Admissions & Eligibility What is the eligibility for the program? The Post Graduate Program in Artificial Intelligence for Leaders is a hands-on program designed for senior professionals and thus a minimum of 8 years of work experience is required to join this course. You should also be sufficiently able to understand and express yourself in written and spoken English to derive full benefit from the course. What is the admission process? You are invited to apply using our online application form. Our admissions panel evaluates all applications and if shortlisted, you will be extended an offer of admission. How can I apply for this program? If you are interested in the program, you can apply through the online application form. If you need assistance, please write to us at aifl.utaustin@mygreatlearning.com Fee & Payment What are my payment options? Candidates can pay the course fee through Bank Transfer and Credit/Debit Cards. There are installment plans also available. For further details, please get in touch with us at aifl.utaustin@mygreatlearning.com What is the refund policy? Please note that submitting the admission fee does constitute enrolling in the program and the below cancellation penalties will be applied: 1) Full refund can only be issued within 48 hours of enrollment 2) Admission Fee - If cancellation is requested after 48 hours of enrollment, the admission fee will not be refunded. 3) Fee paid in excess of the admission fee: 1. Refund or dropout requests requested more than 4 weeks before the Commencement Date are eligible for a full refund of the amount paid in excess of the admission fee 2. Refund or dropout requests requested more than 2 weeks before the Commencement Date are eligible for a 75% refund of the amount paid in excess of the admission fee 3. Refund or dropout requests requested more than 24 hours before the Commencement Date are eligible for a 50% refund of the amount paid in excess of the admission fee 4. Requests received after the Commencement Date are not eligible for a refund. Cancellation must be requested in writing to the program office. Still have queries? Contact Us Please fill in the form and a Program Advisor will reach out to you. You can also reach out to us at aifl.utaustin@mygreatlearning.com or +1 512 883 2893 Application Closes Tomorrow Download Brochure Check out the program and fee details in our brochure Oops!! Something went wrong, Please try again. Name Email Mobile Number Work experience in years Work Experience in years Currently a college student 0 Years Less than 1 Year 1-2 Years 2-3 Years 3-5 Years 5-8 Years 8-12 Years 12-15 Years More than 15 Years By submitting the form, you agree to our Terms and Conditions and our Privacy Policy. Submit Form submitted successfully Thank you for reaching out to us. You can expect to hear from us in 1 working day. Not able to view the brochure? 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Learner Feedback on Mentorship and Program Support All Reviews Alexander Peralta 23 Jan 2023 Batch of July 2022 | Director, Software Engineering at Salesforce | United States Good information and clarity on the subject. The instructor had a mastery of the subject and was able to explain it with good and clear examples. Travis David Luttrell 23 Jan 2023 Batch of July 2022 | United States Extremely good session for AI Leaders! While I appreciate the "how to" for KNIME, it is sessions like this that I was expecting when I enrolled. Ricardo Artur Ribeiro 23 Jan 2023 Batch of March 2022 | Chief Transformation Officer at Montepio Crédito | Portugal A valuable session that was able to structure the subject quite well in my mind. Moreover, the examples, based on problems to address, for each machine learning category helped me to understand better the applications in the real world. Really very good session. Ricaurte Santamaria 23 Jan 2023 Batch of September 2021 | Senior Manager Reliability at Dp World | United Arab Emirates The computer vision concept normally is a difficult one to send across, but I'm sure that my colleagues will agree that the session today was fantastic with a detailed explanation about how it works, and with practical applications. Well done! Read all reviews
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