PG Program in Artificial Intelligence & Machine Learning: Business Applications Learn from a top-ranking global school to build job-ready AI skills 6 Months Program Online Learning with Mentorship Download Brochure Apply Now Application Closes Tomorrow Enquire +1 512 861 6570 96% Program Satisfaction 4.8/5 Trustpilot 4.81/5 Course Report Enquire +1 512 861 6570 Thousands of Careers Transformed Gaston Alvarado Maza Global Category Manager The course content is of high-quality and the instructors are highly prepared in every topic. Joydeep Bhattacharjee Sr Advisor, Architecture Perfect for those who want to get started in this field with little or no prior knowledge. Samantha Fong Manager This program helped me re-enter the industry without having any relevant background. Sriram Subramaniam Senior Lead Engineer Right from their online portal to the training videos, everything is spot on with highly skilled professors to efficient mentor sessions. Gerald Zuniga Technical Safety Lead Concepts accessible for professionals without programming background and sufficiently challenging for those with advanced knowledge in related fields. Kingshuk Banerjee Software Engineering Director Recommend this course to anyone who is overwhelmed by the ML information on the web and wants a clear direction to navigate this exciting technical space. Sujoy Joy Module & Process Owner The course gave me a fair coverage in terms of both breadth and depth of AI ML in 6 months Adarsh Kumar Sr Project Manager Excellent course for students and professionals starting to develop skills needed in the field. Download Brochure Get Industry ready with Career Support 1:1 Industry Interactions Resume & Linkedin Profile Review Interview Preparations & Demos Online Portfolio Assessment Why Choose Our Post Graduate Program in AI & ML Global collaborations with peers Meet other artificial intelligence learners through micro classes and grow your professional network. VIEW BATCH PROFILE Weekend online mentorship by experts Get assistance on 8+ industry-relevant projects through weekend sessions with a certified industry professional. VIEW EXPERIENCE Program Support We help you stay motivated. Get personalised academic and non-academic support during the program. Industry-relevant Projects and Skills Learn in-depth Python, Machine Learning, and Deep Learning techniques. VIEW CURRICULUM Personalized Coding Assistance Build projects with the ease of supportive coding tools Transform your career with Artificial Intelligence & Machine Learning Download Brochure Certificate from the University of Texas at Austin Showcase your Certificate of completion from the University of Texas at Austin in your resume Post Graduate Program in Artificial Intelligence and Machine Learning from Texas McCombs #3 MS - Business Analytics, by QS World University rankings, 2022#6 Executive Education - Custom Programs, Financial Times, 2022 For any feedback & queries regarding the program, please reach out to us at MSB-AIML@mccombs.utexas.edu View Sample Certificate All certificate images are for illustrative purposes only. The actual certificate may be subject to change at the discretion of the University. #3 MS - Business Analytics QS World University Rankings, 2022 #6 Executive Education - Custom Programs Financial Times, 2022 Comprehensive Curriculum The curriculum has been designed by the faculty at McCombs School of Business at the University of Texas at Austin. Download Curriculum 6 months Online Learning 9+ Languages & Tools Download Curriculum Foundations The Foundations module comprises two courses where we get our hands dirty with Python programming language for Artificial Intelligence and Machine Learning and Statistical Learning, head-on. These two courses set our foundations for Artificial Intelligence and Machine Learning online course so that we sail through the rest of the journey with minimal hindrance. Welcome to the program. Module 1: Python Foundations 1 Project 7 Case Studies 1 Project 7 Case Studies Python is an essential programming language in the tool-kit of an AI & ML professional. In this course, you will learn the essentials of Python and its packages for data analysis and computing, including NumPy, SciPy, Pandas, Seaborn and Matplotlib. Python Programming Fundamentals Python is a widely used high-level, interpreted programming language, having a simple, easy-to-learn syntax that highlights code readability. This module will teach you how to work with Python syntax to executing your first code using essential Python fundamentals Python for Data Science - NumPy and Pandas NumPy is a Python package for scientific computing like working with arrays, such as multidimensional array objects, derived objects (like masked arrays and matrices), etc. Pandas is a fast, powerful, flexible, and simple-to-use open-source library in Python to analyse and manipulate data. This module will give you a deep understanding of exploring data sets using Pandas and NumPy. Data Visualization using Python Data visualization is an important skill and one can create compelling visual representations of data to enable effective analysis and communication of insights. Python provides libraries to do this in a simple and effective manner. Exploratory Data Analysis Exploratory Data Analysis, or EDA, is essentially a type of storytelling for statisticians. It allows us to uncover patterns and insights, often with visual methods, within data. This module will give you a deep insight into EDA in Python and visualization tools-Matplotlib and Seaborn. Data Pre-processing Data preprocessing is a crucial step in any machine learning project and involves cleaning, transforming, and organizing raw data to improve its quality and usability. The preprocessed data is used both analysis and modeling. Self-paced Module: Statistical Learning 3 Quizzes 1 Project 3 Quizzes 1 Project Statistical Learning is a branch of applied statistics that deals with Machine Learning, emphasizing statistical models and assessment of uncertainty. This course on statistics will work as a foundation for Artificial Intelligence and Machine Learning concepts learnt in this AI ML PG program. Descriptive Statistics The study of data analysis by describing and summarising numerous data sets is called Descriptive Analysis. It can either be a sample of a region’s population or the marks achieved by 50 students. This module will help you understand Descriptive Statistics in Python for AI ML. Inferential Statistics Inferential Statistics helps you how to use data for estimation and assess theories. You will know how to work with Inferential Statistics using Python. Probability & Conditional Probability Probability is a mathematical tool used to study randomness, like the possibility of an event occurring in a random experiment. Conditional Probability is the likelihood of an event occurring provided that several other events have also occurred. In this module, you will learn about Probability and Conditional Probability in Python for AI ML. Hypothesis Testing Hypothesis Testing is a necessary Statistical Learning procedure for doing experiments based on the observed/surveyed data. You will learn Hypothesis Testing used for AI and ML in this module. Chi-square & ANOVA Chi-Square is a Hypothesis testing method used in Statistics, where you can measure how a model compares to actual observed/surveyed data. Analysis of Variance, also known as ANOVA, is a statistical technique used in AI and ML. You can split observed variance data into numerous components for additional analysis and tests using ANOVA. This module will teach you how to identify the significant differences between the means of two or more groups. Machine Learning The next module is the Machine Learning online course, where you will learn Machine Learning techniques and all the algorithms popularly used in Classical ML that fall in each category. Module 2: Supervised Machine Learning 1 Project 10 Case Studies 1 Project 10 Case Studies Supervised Machine Learning aims to build a model that makes predictions based on evidence in the presence of uncertainty. In this course, you will learn about Supervised Learning algorithms of Linear Regression and Logistic Regression. Linear Regression Linear Regression is one of the most popular supervised ML algorithms used for predictive analysis, resulting in producing the best outcomes. You can use this technique to assume a linear relationship between the independent variable and the dependent variable. You will cover all the concepts of Linear Regression in this module. Logistic Regression Logistic Regression is also one of the most popular supervised ML algorithms, like Linear Regression. It is a simple classification algorithm where you can predict the categorical dependent variables with independent variables’ assistance. You will cover all the concepts of Logistic Regression in this module. Decision Trees A decision tree is a Supervised ML algorithm, which is used for both classification and regression problems. It is a hierarchical structure where internal nodes indicate the dataset features, branches represent the decision rules, and each leaf node indicates the result. Module 3: Ensemble Techniques and Model Tuning 1 Project 8 Case Studies 1 Project 8 Case Studies Ensemble methods help to improve the predictive performance of Machine Learning models. In this machine learning online course, you will learn about different Ensemble methods that combine several Machine Learning techniques into one predictive model in order to decrease variance, bias or improve predictions. Bagging and Random Forests In this module, you will learn Random Forest, a popular supervised ML algorithm that comprises several decision trees on the provided several subsets of datasets and calculates the average for enhancing the predictive accuracy of the dataset, and Bagging, an essential Ensemble Method. Boosting Boosting is an Ensemble Method which can enhance the stability and accuracy of machine learning algorithms, converting them into robust classification, etc. Cross Validation Cross-validation is a technique used to evaluate the performance of machine learning models, which helps in getting a clearer picture of an ML model's generalization ability. K-fold cross validation is one of the most common approaches. Class Imbalance Handling Class imbalance handling is a crucial task in machine learning when the distribution of classes in the dataset is uneven. There are different techniques like RandomUnderSampler, SMOTE, etc. used for this purpose. Hyperparameter Tuning Hyperparameter tuning is the process of finding the optimal values for hyperparameters in a machine learning algorithm. It involves exploring different combinations of hyperparameter values and evaluating their impact on the model's performance, often using techniques like grid search, random search, etc Module 4: Unsupervised Learning 1 Project 4 Case Studies 1 Project 4 Case Studies Unsupervised Learning finds hidden patterns or intrinsic structures in data. In this machine learning online course, you will learn about commonly-used clustering techniques like K-Means Clustering and Hierarchical Clustering along with Dimension Reduction techniques like Principal Component Analysis. K-Means Clustering K-means clustering is a popular unsupervised ML algorithm, which is used for resolving the clustering problems in Machine Learning. In this module, you will learn how the algorithm works and later implement it. This module will teach you the working of the algorithm and its implementation. Hierarchical Clustering Hierarchical Clustering is another popular unsupervised ML technique or algorithm, which is used for building a hierarchy or tree-like structure of clusters. For example, you can combine a list of unlabeled datasets into a cluster in the hierarchical structure. Principal Component Analysis (PCA) PCA is a dimensionality reduction technique used to transform a high-dimensional dataset into a lower-dimensional space. This can help in choosing and retaining only the variable which capture the highest amount of variablity in the data as they will be the most important ones. Self-paced Module: Model Deployment This last module of the machine learning online course will discuss the model deployment techniques and techniques around making your model scalable, robust, and reproducible. Packaging Models Model Packaging helps you package all the necessary assets to host a model as a web service. It also enables you to download either a fully built Docker image or the files required to make one. Rest APIs, Dockers RESTful API, also known as Representational State Transfer, is an API that uses HTTP requests like GET, PUT, POST, and DELETE to communicate with web services. Docker is one of the most popular tools, which is used to create, deploy, and run applications with the help of containers. This module will teach how to package up an application using containers. ML Pipeline and Model Scalability In this module, you will learn everything you need to know about ML Pipeline and Model Scalability used for ML models. Artificial Intelligence & Deep Learning The AI and Deep Learning course will take us beyond the traditional ML into the realm of Neural Networks. From the regular tabular data, we move on to training our models with unstructured data like Text and Images. Module 5: Introduction to Neural Networks 1 Project 4 Case Studies 1 Project 4 Case Studies In this artificial intelligence online course, you will learn about the basic building blocks of Artificial Neural Networks. In this deep learning online course, you’ll learn how Deep Learning Networks can be successfully applied to data for knowledge discovery, knowledge application, and knowledge-based prediction. Deep Learning and its history Deep Learning carries out the Machine Learning process using an ‘Artificial Neural Net’, which is composed of several levels arranged in a hierarchy. It has a rich history that can be traced back to the 1940s, but significant advancements occurred in the 2000s with the introduction of deep neural networks and the availability of large datasets and computational power. Multi-layer Perceptron The multilayer perceptron (MLP) is a type of artificial neural network with multiple layers of interconnected neurons, including an input layer, one or more hidden layers, and an output layer. It is a versatile architecture capable of learning complex patterns from data. Activation functions Activation Function is used for defining the output of a neural network from numerous inputs. Backpropagation Backpropagation is a key algorithm used in training artificial neural networks, enabling the calculation of gradients and the adjustment of weights and biases to iteratively improve the performance of a neural network. Optimizers and its types Optimizers are algorithms used to adjust the parameters of a neural network model during training to minimize the loss function. Different types of optimizers are Gradient Descent, RMSProp, Adam, etc. Weight Initialization and Regularization Weight initialization is the process of setting initial values for the weights of a neural network, which can significantly impact the model's training and convergence. Regularization is a technique used in machine learning/ neural networks to prevent the model from overfitting, which helps improve the model's generalization ability. Module 6: Introduction to Computer Vision 1 Project 4 Case Studies 1 Project 4 Case Studies The next module is the Computer Vision course. This module will reflect on the ability of a computer system to see and make sense of visuals using CNN (Convolutional Neural Network). It will enable you to efficiently handle image data for the purpose of feeding into CNNs. Overview of Computer Vision (CV) In this module, you will drive through all the business applications of computer vision and learn how it impacted several business industries. Understanding images (Color Pixel Theory and Image Representation) This module will teach you how to process the image and extract all the data from it, where you can use the data for image recognition in deep learning. Convolution Operation and Convolutional Neural Networks (CNNs) Convolutional Neural Networks (CNN) are used for image processing, classification, segmentation, and numerous other applications. This module will give you a deep understanding of CNNs from scratch. CNN Architectures CNN architectures are specialized deep learning models designed for processing grid-like data, such as images or audio. Common architectures include VGG16, ResNet, InceptionNet, etc. Transfer Learning Transfer learning is a technique in neural networks where a pre-trained model, usually trained on a large dataset, is used as a starting point for solving a related task. It allows leveraging the knowledge and learned features from the pre-trained model, reducing the need for extensive training on limited data, and often leads to improved performance and faster convergence. Module 7: Natural Language Processing 1 Project 3 Case Studies 1 Project 3 Case Studies This module of the NLP online course talks about yet another interesting implementation of Neural Networks that revolves around equipping computers to understand human language. You will learn to understand sentiments from the texts. Overview of Natural Language Processing (NLP) This module will get you comfortable with the introduction to NLP and, later, teach you all the essential business applications you need to know about NLP. Natural Language Processing (NLP) applies computational linguistics to build real-world applications, which work with languages consisting of varying structures. First, we teach the computer to learn languages and then expect it to understand them with relevant, efficient algorithms. Text Preprocessing Text preprocessing is a technique for cleaning and preparing text data. In this module, you learn how to work with text data using all the steps involved in preprocessing a text, such as Text Cleansing, Tokenization, Stemming, etc. Word Representation and Vectorization Techniques Word representation and vectorization techniques aim to convert words or text into numerical representations that can be processed by machine learning models. Methods like one-hot encoding and word embeddings (such as Word2Vec or GloVe) provide effective ways to capture semantic and contextual information about words. Recurrent Neural Networks (RNNs) Recurrent Neural Networks (RNNs) are a type of neural network architecture specifically designed to process sequential data, such as time series or natural language. They introduce feedback connections and excel in capturing temporal dependencies in data. Long Short Term Memory Networks (LSTMs) Long Short-Term Memory (LSTM) networks are a specialized type of recurrent neural network (RNN) designed to overcome the limitations of standard RNNs. They utilize memory cells and a gating mechanism which helps in capturing long-term dependencies. Sentimental Analysis Sentiment Analysis is an NLP technique to check whether the data is positive, negative, or neutral. Twitter is the most commonly used example of Sentiment Analysis. Self-paced Module: Demystifying ChatGPT and Applications 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. 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 Self-paced Module: ChatGPT: The Development Stack Dive into the development stack of ChatGPT by learning the mathematical fundamentals that underlie generative AI. Further, learn about transformer models and how they are used in generative AI for natural language. Mathematical Fundamentals for Generative AI VAEs: First Generative Neural Networks GANs: Photorealistic Image Generation Conditional GANs and Stable Diffusion: Control & Improvement in Image Generation Transformer Models: Generative AI for Natural Language ChatGPT: Conversational Generative AI Hands-on ChatGPT Prototype Creation Next Steps for Further Learning and understanding Self-paced Module: Recommendation Systems 1 Project 1 Project The last module in this Artificial Intelligence and Machine Learning online course is Recommendation Systems. A large number of companies use recommender systems, which are software that select products to recommend to individual customers. In this course, you will learn how to produce successful recommender systems that use past product purchase and satisfaction data to make high-quality personalized recommendations. Popularity-based Model A popularity-based model is a recommendation system, which operates based on popularity or any currently trending models. Market Basket Analysis Market Basket Analysis, also called Affinity Analysis, is a modeling technique based on the theory that if you purchase a specific group of items, then you are more probable to buy another group of items. Content-based Model First, we accumulate the data explicitly or implicitly from the user. Next, we create a user profile dependent on this data, which is later used for user suggestions. The user gives us more information or takes more recommendation-based actions, which subsequently enhances the accuracy of the system. This technique is called a Content-based Recommendation System. Collaborative Filtering Collaborative Filtering is a collective usage of algorithms where there are numerous strategies for identifying similar users or items to suggest the best recommendations. Hybrid Recommendation Systems A Hybrid Recommendation system is a combination of numerous classification models and clustering techniques. This module will lecture you on how to work with a Hybrid Recommendation system. Career Assistance: Resume and LinkedIn profile review, interview preparation, 1:1 career coaching This post-graduate certification program on artificial intelligence and machine learning will assist you through your career path to building your professional resume and reviewing your Linkedin profile. The program will also conduct mock interviews to boost your confidence and nurture you nailing your professional interviews. The program will also assist you with one-on-one career coaching with industry experts and guide you through a career fair. Post Graduate Certificate from The University of Texas at Austin Earn a Postgraduate Certificate in the top-rated Artificial Intelligence and Machine Learning online course from the University of Texas, Austin. The course’s comprehensive Curriculum will foster you into a highly-skilled professional in Artificial Intelligence and Machine Learning. It will help you land a job at the world’s leading corporation and power ahead your career transition. Languages and Tools covered Hands-on Projects Data sets from the industry 1000+ Projects completed 22+ Domains Supervised Learning A campaign to sell personal loans Build a model that helps to identify potential customers of a bank who have a higher probability of purchasing a loan. LEARN MORE Ensemble Techniques Predict Potential Customers Build a model that will help the marketing team of a company to identify potential customers for a term deposit subscription. LEARN MORE Feature Engineering & Model Tuning Construction Material Strength Perform Feature Engineering and Model Tuning on a model designed to predict the strength of construction material to enhance accuracy. LEARN MORE Unsupervised Learning Bank Customer Segmentation Identify different segments from a bank’s existing customer pool based on their spending patterns as well as past interactions with the bank. LEARN MORE Neural Networks Identify Street View House Numbers Build an Image Classification model to classify street view house numbers using Neural Networks. LEARN MORE Natural Language Processing Sarcastic News Detection Detect sarcastic news headlines using a Recurrent Neural Network architecture on different news headlines. LEARN MORE Recommendation Systems e-Commerce Recommendation System Build your own recommendation system for products on an e-commerce website. LEARN MORE View More (4) Our Faculty and Mentors Learn from leading academicians in the field of Data Science and Engineering and several experienced industry practitioners from top organisations. 20+ Professors 2500+ Industry Mentors Dr. Kumar Muthuraman Faculty Director, Centre for Research and Analytics Dr. Abhinanda Sarkar Faculty Director, Great Learning Prof. Mukesh Rao Director- Data Science Dr. Sunil Kumar GM - Engineering Innovation Industry Mentors from Top Organisations Idris Malik Software Engineer, Machine Learning Nimish Srivastava Senior Machine Learning Engineer Franck Tchuente Senior Data Scientist Vybhav Reddy K C Senior Data Scientist Dipjyoti Das Staff Data Scientist Omid Badretale Senior Research Data Scientist | Alternative Data Asghar Mohammadi Senior Data Scientist Rafat Mohammed Senior Data Scientist, Advanced Analytics Mustakim Helal Senior Data Engineer Alisher Mansurov Assistant Professor Shahzeb Shahid Senior Data Scientist Yusuf Baktir Senior Data Scientist Shekhar Tanwar Machine Learning Engineer Mahmudul Hasan Lead Data Scientist Olha Kuzaka Senior Software Engineer 1 - Data, Tech Lead Karlos Muradyan Data Scientist Marcelo Guarido de Andrade Senior Data Scientist and Head of the CREWES Data Science Initiative Kandarp Patel Staff Data Scientist, AI/ML Ben Brock Teaching Assistant to Professor Stuart Urban for Quantitative Financial Analysis course. Learner Testimonials "The faculty and videos have been fantastic. At the end of each and every session, there were practice modules that were provided to us. We also had a project discussion forum where anybody in the team who was working on the project could raise a question and the team would answer it. Gaurang Laxmanbhai Patel IT Project Manager, L&T Infotech (United States) "The way it was structured, the timings, and how it was broken down were really good. I started noticing that I had pretty much touched all the important areas or fundamental areas that would actually help me take this subject or my learning to the next level. Shadab Syed Specialist - Information Security, QIB (Qatar) "It has exceeded my expectations. I literally walked away feeling great and confident. I was intimidated by artificial intelligence. Now I'm not. That’s where I see the impact. Alston Noah CEO, Vincari (United States) "The fact that each video can be watched during a lunch break or during downtime at work in a way that you can understand makes the learning journey more rewarding, satisfying, and manageable. William Matthew Tyler Sr. Associate Consultant, Infosys (United States) "The support system was key, like having a mentor, coordination manager, those sorts of concepts, and I didn't find that in many of the other ones. If you're balancing your work, your family, and studying, then this sort of thing really helps you. Tandeep Sandhu Solutions Director, HCL (United States) "The program is perfect for someone who has little to no experience in the field of Data Science. For me, the brochure and the information provided syllabus, requirements, and delivery schedule were the main selling points. I would wholeheartedly recommend this program to anyone who wants to jumpstart a career in Data science. David Hickman Director-Data Science & Analytics, PE Impact (United States) "I liked the concept of learn and apply at Great Learning. The program gave me the confidence to be able to solve complex problems and figure out the tools that can help me do that. The mentor sessions were incredible, with all mentors always going above and beyond when it came to imparting knowledge. Stephanie Nicole Baker Research Associate, TACC-UT Austin (United States) "I believe the course content is of high quality, and all the instructors are highly prepared in every topic. The mentorship sessions are great with their insights and additions to the materials being very valuable. The support from the Program Managers has also been outstanding. I believe the program is outstanding. Gaston Alvarado Maza Global Category Manager, Materion (United States) "The program helped me upgrade my skillsets to understand the concepts that emerging technologies are bringing . It helped me upskill exactly in the same technologies that my company was working into, and gave me the ability to work efficiently in this field. Ana Alfaro Senior Demand Management Systems Analyst, NXP (United States) "The Mentor Learning sessions and the ability to network with a diverse cohort were the two things that made me take the course. The case studies in the program help us solve real world problems with much ease! Dustin Lee Junior Technical Consultant, ProLytX (United States) "The content has been well thought out and the team has been very responsive. It has been a great experience for me, and I would recommend this program to my colleagues. Deepa Chandrasekaran Director-Strategic Development, IMI (UK) "My experience with my program advisor has been great. He is very receptive and solves all doubts I have. The program advisor pushes us to achieve our goals consistently, which makes this program better than others. Everth Hernandez Sales Director, Aruba -HPE (Mexico) "I speak the language now when I get talking to my clients or when I go for business development activities. The course has offered me that edge and confidence to understand the field of AI and ML better. Kokila Narayanan Senior Consultant, CGI (United States) "The program is helping me in my current job, where I am going to incorporate my learnings. With the transition happening in the industry, this program is a great stepping stone. Afshan Parkar Instructor, Zayedh University (UAE) "I am very greatful to the program office, as they helped me throughout the learning journey. Anytime I had a request, the program advisor would respond very quickly and effectively. Dimitrios Zografos Director-Asset Management, IPTO (Greece) "My learnings through projects allowed me to solve problems at my job, especially problems related to computer vision and robotic processes. I am also greatful to the program advisor, who helped us every time we faced a problem. Endri Hoxha Automation Engineer, Alten (Switzerland) Learner Feedback on Mentorship and PM Support PG Program in Artificial Intelligence and Machine Learning Learner Satisfaction Rating 5,463Ratings All Reviews Beatriz Krivda Batch of January 2023 | United States Excellent from first moment of Learning session to very last second, mentor was very enthusiastic about the concepts, very focused on the questions the learners asked, answered all questions and engaged the learners in the subject matter. Provided insights, tips, and recommendations for the data science field, which will help the learners begin to adapt to think like a data scientist. the mentor was very encouraging as well. READ MORE 01 Apr 2023 José Miguel Ruiz Guerra Batch of July 2022 | Modeling Analyst at Nielsen | Mexico The session was very good and helpful in improving my understanding of the topic and how to address computer vision classification. I found it somewhat difficult to improve the performance of the model on my own but this session will definitely help me in the future. READ MORE 04 Mar 2023 Beatriz Krivda Batch of January 2023 | United States The instructor was engaged in the concepts, the audience, questions from the audience in the chat box as well as questions and concerns voiced, the instructor offered many explanations and clarifications, all of which help the learners learn. Overall an excellent learning session. READ MORE 21 Mar 2023 Ivan Lozano Batch of February 2023 | United States As always the mentor does a great job of explaining and answering everyone's questions. He has a great deal of patience and I get the sense that he enjoys helping others. I am very happy so far with this course. Thank you. READ MORE 14 Mar 2023 Yonas Keflemariam Batch of July 2023 | Ceo / Lead Consultant at Sphynx Software Solutions, Llc | United States Very solid discussion with clear real-world examples. Sometimes making the connection from learning these new concepts to how they could be applied in business is hard. This lecture made that connection really well! READ MORE 06 Mar 2023 Read all reviews Program Fees Program Fees: 3,800 USD Apply Now Upfront Payment & Referral Upfront Discount: 3,800 USD3,600 USD Referral Discount: 3,800 USD3,650 USD Benefits of learning from us High-quality content 9+ hands-on projects Live mentored learning in micro classes Doubt solving by industry experts Live webinars by UT Austin faculty Career support services Additional Certificate in Python Foundations This program helped me gain hands-on skills with guidance from industry practitioners. And this is just what employers require. Bernard Tumanjong Information Systems Engineer U.S. Army × InstallmentPlans Full fee payment plan Monthly Installment Installments Starts at 6 months 634 USD/month 12 months 317 USD/month Total Fee Payment 3800 USD Application Process 1 Fill the application form Apply by filling a simple online application form. 2 Interview Process Go through a screening call with the Admission Director’s office. 3 Join program An offer letter will be rolled out to the select few candidates. Secure your seat by paying the admission 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 Batch Start Dates Online 14th Oct 2023 Frequently Asked Questions Program Details Who are the mentors as part of this program? You can find the details of the mentors in the program page. Suffice to say, the mentors are industry practitioners with leading organizations and come with extensive experience in their fields. 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 3 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 kind of career support can I expect from this program? The PG Program in Artificial Intelligence & Machine Learning: Business Applications offers career support to ensure you derive not just positive learning outcomes, but also the career outcomes you desire for your professional journey. You can expect career guidance through 1:1 career coaching sessions with industry practitioners, resume and LinkedIn profile review, interview preparation sessions, and webinars with UT Austin faculty. Do candidates need to bring their own laptops? The candidates need to bring their own laptops; the technology requirement shall be shared at the time of enrolment. Will I receive a transcript or grade sheet after completion of the program? The Post Graduate Program in Data Science and Business Analytics is an online professional certificate program offered by the McCombs School of Business in collaboration with Great Learning. You will receive the grade sheet post-completion; however, the program does not carry any credits. Also, your performance will be assessed through individual assessments and module completion to determine your eligibility for the certificate. Upon completing all the modules in accordance with the qualifying requirements for the program, you'll receive a certificate from 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 mentored learning 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. Why Artificial Intelligence and Machine Learning What is Artificial Intelligence? Artificial Intelligence is the technology used to build intelligent machines that act as humans do. The AI enabled systems to mimic human behavior and perform tasks as we do. This intelligence is built using complex algorithms and mathematical functions. Artificial Intelligence is the technology that is being applied in almost every industry and business. AI is literally everywhere. We are witnessing the presence of Artificial Intelligence every single day of our lives. Artificial Intelligence is applied in smartphones, smart window treatments, banking, self-driving cars, healthcare, social media, video games, surveillance, and many other aspects of our daily life. What is Machine Learning? Machine Learning is an important subset of Artificial Intelligence. Machine learning is one of the most interesting careers that you could choose. Machine learning is perceived as one of the fastest-growing technologies. Machine learning is a subset of artificial intelligence that provides systems the ability to automatically learn and progress from experience without being specifically instructed. By employing Machine Learning techniques, businesses can automate routine tasks and maximize profits. Hence, pursuing a PG in achine learning and artificial intelligence would fetch you the best career opportunities. Is learning Artificial Intelligence worth it? Artificial Intelligence is one of the most latest trending technologies. Artificial Intelligence is not just about creating robots or building computer systems that can think as humans do. Artificial Intelligence is a technology that understands humans and makes their lives easy. From Apple's Siri to Google's voice assistant, from facebook friend recommendations to Netflix's movie recommendations, Artificial Intelligence is playing the most pivotal role in making our lives easy. AI in simple words can be defined as an interface to us and the computer devices, it is the technology that makes the systems understand humans so well. The technology of AI is just growing at a rapid pace and the number of industries and businesses adapting this technology is reaching the skies. There is a huge demand for AI professionals across the globe. Hence, taking up the best Artificial Intelligence course and pursuing a career in this domain stands as the best choice you could make for yourself. What does an AI do? AI has been around for years. In a world full of data, Artificial Intelligence is already making our lives easier in a lot of days today. Experts predict that the world would be changed forever by the revolution of AI by the year 2050. But let us understand what AI does. Artificial Intelligence is nothing but software that automatically gathers and analyses data to derive meaningful insights from that. The Facebook newsfeed would automatically adjust according to our interests. The AI algorithms intake flood of data, filter them out, and display them for us at the top of the page. Artificial Intelligence does mimic the human brain. In other words, AI enables machines to think and act as humans do. Artificial Intelligence provides the machines the ability to adapt, reason, and provide solutions. AI in simple terms can be explained as the technology to make the machines artificially intelligent. This requires huge data and immense computer capacity to process the applications. Even as more powerful technologies such as Blockchain, the Internet of Things, and more are being developed, AI is constantly causing a revolution in every domain.This is why the demand for artificial Intelligence is growing day by day. Therefore, many are seeking for ai course to encounter a career transition into the job roles of AI. What is the pay scale of Artificial Intelligence and Machine Learning professionals across the world? The pay scale offered in the domain of Artificial Intelligence is one of the major factors that is motivating many to pursue a career in this domain. The job roles offered in this domain are considered to be one of the highest-paid across the globe. In the United States, the pay scale of Artificial Intelligence and Machine Learning professionals ranges from $90k to $305k per annum. The average pay scale is expected to be $164,769 per annum. While in India it ranges from 6 to 35 lakh per annum and the average pay scale is estimated as 21,86,857 per annum. Hence, the demand for Artificial Intelligence and Machine Learning courses is at its peak across the world. What are the prerequisites for pursuing AI and machine learning courses? If you are interested in taking up an Artificial Intelligence training, choose to learn the prerequisites and fundamentals of artificial intelligence. Here are a few obvious prerequisites that assist you in mastering the techniques of Artificial Intelligence and Machine Learning thoroughly. 1. Programming languages As AI and ML are all about training computer systems, it is essential to learn programming languages. Programming languages are the basic source to interact with the machines. Java, C++, R, Python, etc are a few languages that are extensively used in machine learning. Python specifically is the most popular programming language used by most AI and Machine Learning professionals. 2. Mathematics and Statistics You need not be an expert in mathematics to learn Artificial Intelligence and Machine Learning technologies. Nevertheless, you shouldn't be a novice to several mathematical concepts that are applied in Artificial Intelligence and Machine Learning. A fundamental understanding of calculus, probability, statistics is crucial to understand the concepts of AIML. 3.Data Visualisation Tools Data Visualisation is one of the primary job roles of AI professionals performed on a daily basis. Hence, being good at several data visualization tools such as Tableau, Microsoft Power BI and more would assist you to learn the techniques of AI at a faster pace. Besides the above-mentioned prerequisites, having a concise understanding of the fundamentals of Artificial Intelligence would also promote a great understanding of the several concepts of Artificial Intelligence. Who can pursue an Artificial Intelligence Course? The Artificial Intelligence Courses designed by Great Learning are suitable for someone who is: As computer science with artificial intelligence is an exciting combination, a developer who wants to become a Machine Learning Engineer or Artificial Intelligence Scientist would take up an AI learning course. Analytics Managers that drive a team composed of Analysts could learn AI. Analytics professionals that desire to work in AI or Machine Learning Fresh graduates who want to secure a career in Machine Learning or AI could take up the pg in artificial intelligence courses. Managers or Business owners who desire to become AI-enabled professionals can opt for the AI for leaders course. Experienced working professionals that want to employ AI in their existing work field. What are the various benefits of Artificial Intelligence and Machine Learning? The technology of Artificial Intelligence has a lot more to contribute to any industry than individuals do. Hence many businesses are applying advanced artificial intelligence to draw the best outcomes. Let us understand a few of the benefits. Building better business strategy: By employing Artificial Intelligence, organizations can develop the best business plan. Artificial Intelligence renders solutions to come up with the best business plan that supports companies' flourish. Today, most of the top-notch companies are applying Artificial Intelligence in project and operation management to obtain better outcomes. Better Research and Inventions: Organizations must be conscious of the latest trends in their market. An AI-enabled business team would shape their business in the best way that suits the requirements of end customers. An AI-enabled organization would learn current technological trends, plan a business strategy that delivers the best services. Businesses with a good vision and well versed with AI can compose a groundbreaking solution. AI assists businesses to add value to their products by adapting themselves to the latest trends in the market, technology. Cost Reduction: Cost reduction is one of the major benefits that AI contributes to any business. Small and medium scale certainly strive for their endurance considering their limited budget and resources. With a substantial demand for AI professionals, these companies may not be able to afford such resources to meet their needs. Hence, businesses need to adopt AI so that they can reduce costs to the company. AI in business draws more customers that explore solutions for their problems. Therefore, taking up an AI certification course would fetch you with the best career opportunities in several industries in the market. What are the Applications of AI in different industries? Many believe that Artificial Intelligence and Machine Learning are limited to the IT industry. AI is being applied everywhere in every industry across the world. Let us understand how AI is being employed in several industries today. Customer Support: The domain of AI is observed to replace many customer support job roles. Today, most websites are using chatbots to assist customers. The AI-enabled chatbot systems are capable of addressing customer's problems and provide the user with the most meaningful product recommendations at a faster pace. E-commerce: With the employment of an AI recommendation system, E-commerce websites are offering personalized shopping experiences to their users. The systems study the user's past purchase records and recommend the most suitable products. The system learns the customer's choice and presents the most meaningful recommendations. This makes the user experience a personalized shopping experience. In this way, AI is benefitting the E-commerce industry by enhancing the customer experience. Today, a lot of e commerce giants such as Amazon employ AI to drive their businesses. Artificial Intelligence in Social Media Social Media has become an indispensable part of our daily lives. We spend most of our time on Social media platforms such as Facebook, Twitter, Instagram, and more. There is a huge amount of data being generated through social media websites in the form of messages, tweets, posts, and more. In social media platforms like Facebook, Artificial Intelligence is used for face recognition while Machine Learning and Deep Learning concepts are used to recognize the facial features of people and automatically suggest you tag them. Twitter's AI is being used to identify hate speech and terroristic language in tweets by employing Natural Language Processing. Hence, check out the best courses in Artificial Intelligence, learn AI today, and get into the most in-demand job roles of the 21st century. Fee & Payment 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. Related Programs for you Accelerate your career with a program in Data Science Let us help you find the best program 5 Programs 48% avg. salary hike Explore All Programs Applied Data Science Program 12 Weeks Live Virtual Weekdays & Weekend PG Program in Data Science and Business Analytics 6 Months Online Weekend PG Program in Artificial Intelligence for Leaders 4 Months Online Weekend PG Program in Cloud Computing 6 months Online Weekend Design Thinking: From Insights to Viability 14 Weeks Online Weekend Accelerate your career with a program in Data Science Let us help you find the best program 5 Programs 48% avg. salary hike Explore All Programs Still have queries? Contact Us Please fill in the form and an expert from the admissions office will call you in the next 4 working hours. You can also reach out to us at aiml.utaustin@mygreatlearning.com or +1 512 861 6570 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? View Brochure Browse Related Blogs 20 Deep Learning Applications in 2021 Across Industries Learn More > What is Artificial Intelligence? How Does AI Work, Applications and Future? 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Learner Feedback on Mentorship and PM Support PG Program in Artificial Intelligence and Machine Learning Learner Satisfaction Rating 5,463Ratings All Reviews Beatriz Krivda Batch of January 2023 | United States Excellent from first moment of Learning session to very last second, mentor was very enthusiastic about the concepts, very focused on the questions the learners asked, answered all questions and engaged the learners in the subject matter. Provided insights, tips, and recommendations for the data science field, which will help the learners begin to adapt to think like a data scientist. the mentor was very encouraging as well. READ MORE 01 Apr 2023 José Miguel Ruiz Guerra Batch of July 2022 | Modeling Analyst at Nielsen | Mexico The session was very good and helpful in improving my understanding of the topic and how to address computer vision classification. I found it somewhat difficult to improve the performance of the model on my own but this session will definitely help me in the future. READ MORE 04 Mar 2023 Beatriz Krivda Batch of January 2023 | United States The instructor was engaged in the concepts, the audience, questions from the audience in the chat box as well as questions and concerns voiced, the instructor offered many explanations and clarifications, all of which help the learners learn. Overall an excellent learning session. READ MORE 21 Mar 2023 Ivan Lozano Batch of February 2023 | United States As always the mentor does a great job of explaining and answering everyone's questions. He has a great deal of patience and I get the sense that he enjoys helping others. I am very happy so far with this course. Thank you. READ MORE 14 Mar 2023 Yonas Keflemariam Batch of July 2023 | Ceo / Lead Consultant at Sphynx Software Solutions, Llc | United States Very solid discussion with clear real-world examples. Sometimes making the connection from learning these new concepts to how they could be applied in business is hard. This lecture made that connection really well! READ MORE 06 Mar 2023 Read all reviews
Related Programs for you Accelerate your career with a program in Data Science Let us help you find the best program 5 Programs 48% avg. salary hike Explore All Programs Applied Data Science Program 12 Weeks Live Virtual Weekdays & Weekend PG Program in Data Science and Business Analytics 6 Months Online Weekend PG Program in Artificial Intelligence for Leaders 4 Months Online Weekend PG Program in Cloud Computing 6 months Online Weekend Design Thinking: From Insights to Viability 14 Weeks Online Weekend Accelerate your career with a program in Data Science Let us help you find the best program 5 Programs 48% avg. salary hike Explore All Programs
Browse Related Blogs 20 Deep Learning Applications in 2021 Across Industries Learn More > What is Artificial Intelligence? How Does AI Work, Applications and Future? Learn More > What is Artificial Intelligence Learn More > Artificial Intelligence Books Learn More > Machine Learning Interview Questions Learn More > What is Machine Learning Learn More > Prerequisites for Machine Learning Learn More > Deep Learning vs Machine Learning Learn More > Deep Learning Applications Learn More > AI Technology Learn More >