PG Program in Artificial Intelligence & Machine Learning: Business Applications
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Thousands of Careers Transformed
Michael Wang
Director of Business Development, ISSI
By breaking down advanced concepts into understandable terms, the course gave me the confidence and skills to advance my career.
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.
Travis L Stoner
Principal Product Owner, Hexagon
The program allowed me to introduce to my workplace the ways in which we can take advantage of AI concepts and technologies.
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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.
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
Interactive Mentor-led Sessions by Industry Experts
Augment weekly learning experience and gain industry insights in in live and interactive mentor-led sessions.
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Global Learning Experience
Upskill with a diverse cohort of professionals from all over the world and grow your professional network.
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Learn Fundamentals of Python Programming
Learn coding without prior experience .Earn a Certificate in Python Foundations.
Real-world Business Projects
Build industry-relevant AI and machine learning skills with 8 hands-on projects under the guidance of experts.
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Certificate from the University of Texas at Austin
Showcase your Certificate of completion from the University of Texas at Austin in your resume
#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
#3
MS - Business Analytics
QS World University Rankings, 2022
#6
Executive Education - Custom Programs
Financial Times,
2022
Elevate Your Skills with On-Campus Immersion (Optional Paid Program)
Decision Science and AI Program
In the 3-day immersive on-campus program you can:
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Connect with like-minded AI professionals.
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Immerse in On-Campus Learning for 3 Days
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Learn Leadership Skills
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Create Intelligent Decision Science Systems
Comprehensive Curriculum
The curriculum has been designed by the faculty at McCombs School of Business at the University of Texas at Austin.
7 months
Online Learning
9+
Languages & Tools
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.
- The fascinating history of Data Science and AI
- Transforming Industries through Data Science and AI
- The Math and Stats underlying the technology
- Navigating the Data Science and AI Lifecycle
Gain an understanding of the evolution of AI and Data Science over time, their application in industries, the mathematics and statistics behind them, and an overview of the life cycle of building data driven solution.
Gain a fundamental understanding of the basics of Python programming and build a strong foundation of coding to build AI applications.
1 Project
10 Case Studies
- Python Programming Fundamentals
- Python for Data Science - NumPy and Pandas
- Data Visualization using Python
- Exploratory Data Analysis
- Data Pre-processing
- AI Application Case Study
1 Project
10 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 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
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 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, 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 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.
AI is used across a variety of businesses to enhance operational efficiency, streamline decision-making processes, gain actionable insights from data, and foster innovation, This module will cover an end-to-end case study to illustrate the different stages of implementing an AI solution for a business problem.
3 Quizzes
1 Project
7 Case Studies
- 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.
3 Quizzes
1 Project
7 Case Studies
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.
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.
1 Project
10 Case Studies
- Linear Regression
- Logistic Regression
- Decision Trees
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
1 Project
10 Case Studies
In this module, understand the concept of learning from data, build linear and non-linear models to capture the relationships between attributes and a known outcome, and discover patterns and segment data with no labels.
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 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 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.
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.
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 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 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.
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.
1 Project
6 Case Studies
- Bagging and Random Forests
- Boosting
- Cross Validation
- Class Imbalance Handling
- Hyperparameter Tuning
1 Project
6 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.
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 is an Ensemble Method which can enhance the stability and accuracy of machine learning algorithms, converting them into robust classification, etc.
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 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 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
- 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.
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.
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.
1 Project
5 Case Studies
- Deep Learning and its history
- Multi-layer Perceptron
- Activation functions
- Backpropagation
- Optimizers and its types
- Weight Initialization and Regularization
1 Project
5 Case Studies
In this module, implement neural networks to synthesize knowledge from data, demonstrate an understanding of different optimization algorithms and regularization techniques, and evaluate the factors that contribute to improving performance to build generalized and robust neural network models to solve business problems.
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.
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 Function is used for defining the output of a neural network from numerous inputs.
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 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 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.
1 Project
4 Case Studies
- Overview of Computer Vision (CV)
- Understanding images (Color Pixel Theory and Image Representation)
- Convolution Operation and Convolutional Neural Networks (CNNs)
- CNN Architectures
- Transfer Learning
1 Project
4 Case Studies
In this module, you will drive through all the business applications of computer vision and learn how it impacted several business industries.
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.
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 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 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.
1 Project
6 Case Studies
- Overview of Natural Language Processing (NLP)
- Attention Mechanism and Transformer Models
- Word Embeddings
- Large Language Models and Prompt Engineering
1 Project
6 Case Studies
Get introduced to the world of natural language processing, gain a practical understanding of text processing and vectorization methods, gain a practical understanding of the working of different transformer architectures that lie at the core of large language models (LLMs), and design and implement robust NLP solutions using open-source LLMs combined with prompt engineering techniques.
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.
Transformers are neural network architectures that develop a context-aware understanding of data and have revolutionized the field of NLP by exhibiting exceptional performance across a wide variety of tasks. This module dives into the underlying working of transformer models and how to use them to solve complex NLP tasks.
Word embeddings allow us to numerically represent complex textual data, thereby enabling us to perform a variety of operations on them. This module will cover different word embedding techniques and the steps involved in designing and implementing hands-on solutions combining word embedding methods with machine learning techniques for solving NLP problems.
Large Language Models (LLMs) are ML models that are pre-trained on large corpora of data and possess the ability to generate coherent and contextually relevant content. Prompt engineering is a process of iteratively deriving a specific set of instructions to help an LLM accomplish a specific task. This module introduces LLMs, explains their working, and covers practices to effectively devise prompts to solve problems using LLMs.
- ChatGPT and Generative AI - Overview
- ChatGPT - Applications and Business
- Breaking Down ChatGPT
- Limitations and Beyond ChatGPT
- Generative AI Demonstrations
Get an overview of Generative AI, what ChatGPT is and how it works. delve into the business applications of ChatGPT, and an overview of other generative AI models/tools via demonstrations.
1 Project
2 Case Studies
- 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.
1 Project
2 Case Studies
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.
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.
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.
The Decision Science and AI is a 3-day on-campus Program that presents a valuable opportunity to explore AI use cases and become a driving force behind AI-driven initiatives within your organization. It comprises of dynamic discussions, collaboration with like-minded professionals, and engaging networking sessions hosted at the prestigious University of Texas at Austin.
- Welcome & Program Orientation
- Introduction to Decision Sciences & AI
- Campus Tour & Group Photo
- Introduction to Dynamic Programming
- Programming an AI agent to Play a Variant of Blackjack
- Introduction to Reinforcement Learning
- Programming an AI Agent that learns by itself to play computer games
- Session with Industry Mentor
- The Art and Science of Negotiations
- Project Brief and Active group work
- Group work on Project
- Certifications and Photo Ops
Languages and Tools covered
Hands-on Projects
1000+
Projects completed
22+
Domains
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. Dan Mitchell
Assistant Professor, McCombs School of Business
Dr. Abhinanda Sarkar
Faculty Director, Great Learning
Prof. Mukesh Rao
Director- Data Science
Dr. Bradford Tuckfield
Data Science Expert
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
"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)
"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)
"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)
Program Fees
Program Fees:
4,200 USD
Upfront Payment & Referral
4,000 USD
4,050 USD
Payment Partners
*Subject to partner approval based on regions & eligibility. dLocal for Brazil, Colombia & Mexico learners. Other partners for U.S. learners only.
Benefits of learning from us
- High-quality content
- 8+ 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
Application Process
Fill the application form
Apply by filling a simple online application form.
Interview Process
Go through a screening call with the Admission Director’s office.
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: 4th Jul 2024
Apply Now
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help@mygreatlearning.comBatch Start Dates
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Frequently Asked Questions
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.
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.
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.
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.
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.
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.
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.
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.
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.
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