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.
Sr Advisor, Architecture
Perfect for those who want to get started in this field with little or no prior knowledge.
This program helped me re-enter the industry without having any relevant background.
Senior Lead Engineer
Right from their online portal to the training videos, everything is spot on with highly skilled professors to efficient mentor sessions.
Technical Safety Lead
Concepts accessible for professionals without programming background and sufficiently challenging for those with advanced knowledge in related fields.
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.
Module & Process Owner
The course gave me a fair coverage in terms of both breadth and depth of AI ML in 6 months
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
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
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
Additional Bootcamp to learn the foundations of programming
Bootcamp Designed for Learners without Programming Experience
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
MS - Business Analytics
QS World University Rankings, 2022
Executive Education - Custom Programs
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.
- Introduction to Programming
- Introduction to Python
- Creating Your First Program With Data Structures, Loops, and Functions
- Final Assessment
For professionals seeking to upskill in Artificial Intelligence and Machine Learning without any prior programming experience, this bootcamp is focused on helping you master the fundamentals required to get started. With a strong foundation, your learning journey in AI and ML will be more streamlined.
Get a glimpse into the world of computer programming and the different hardware components in a machine affecting the performance of data models and processing
Learn the basics of Python to strengthen your programming foundations. This module includes introduction to variables, data types, print, input and output statements in Python. It is designed in such a way that you can start solving Data Analysis problems right away
Understand how loops and functions are written and leveraged in Python. This module will also take you through the pre-existing functions in Python, and how they can be utilized to solve data challenges. With all of this basic programming knowledge, you will be able to design and write your first program in Python.
This module allows you to demonstrate the ability to utilize Python programming for solving Data Science and Data Analysis problems through hands-on assessments
- Introduction to Python
- NumPy, Pandas
- Exploratory Data Analysis
- Matplotlib, Seaborn
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.
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 is a library to create statically animated, interactive visualisations, whereas Seaborn is a Matplotlib based data visualisation library in Python.
This module will give you a deep understanding of exploring data sets using Matplotlib and Seaborn.
- Descriptive Statistics
- Inferential Statistics
- Probability & Conditional Probability
- Hypothesis Testing
- Chi-square & ANOVA
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 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 helps you how to use data for estimation and assess theories. You will know how to work with Inferential Statistics using Python.
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 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 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.
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.
- Introduction to Machine Learning
- Linear Regression
- Logistic Regression
- Model Evaluation
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.
Machine Learning is a subset of Artificial Intelligence (AI), which builds applications that learn from data and automatically improve their performance and accuracy through the experience without being programmed.
This module will teach you everything you need to know about the introduction to machine learning.
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.
This module will teach you how to evaluate the performance of your machine learning models with the help of model evaluation metrics.
- Decision Trees
- Random Forests
- Ensemble Methods - Bagging, Boosting, and Stacking
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.
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.
Random Forest is a popular supervised ML algorithm. As the name implies, it comprises several decision trees on the provided several subsets of datasets. Later, it calculates the average for enhancing the predictive accuracy of the dataset.
In this module, you will learn several essential Ensemble Methods like Bagging, Boosting, and Stacking, where you can enhance the stability and accuracy of machine learning algorithms, converting them into robust classification, etc.
Model Selection and Tuning
- Feature Engineering
- Sampling and Smote, Regularization
- Model Performance Measures
Model building is an iterative process. Employing Feature Engineering techniques, along with a careful model selection exercise, helps to improve the model. Further, tuning the model is an important step to arrive at the best possible result. This module of the machine learning online course talks about the steps and processes around the same.
Feature engineering is the process of transforming data from the raw state to a state where it becomes suitable for modelling.
You will learn several steps involved in Feature Engineering in this module.
Sampling is a process to retrieve information about the population predicated on statistics. SMOTE stands for Synthetic Minority Oversampling Technique, which helps you increase your dataset’s total cases in a balanced manner.
Regularization is used to modify your ML models to prevent overfitting and create an optimal solution.
This module will give you a deep understanding of automating your ML workflows using Pipelining.
This module will teach you how to optimise the performance of your machine learning models with the help of model evaluation metrics.
- K-Means Clustering
- Hierarchical Clustering
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.
- Packaging Models
- Rest APIs, Dockers
- ML Pipeline and Model Scalability
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.
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.
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.
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.
- Artificial Neural Networks
- Activation Function, Loss Function
Deep Learning carries out the Machine Learning process using an ‘Artificial Neural Net’, which is composed of several levels arranged in a hierarchy. 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.
This module will begin with the introduction to Artificial Intelligence (AI) and Deep Learning.
AI is an intelligent entity created by humans that is capable of performing tasks intelligently without being explicitly instructed. It is also capable of thinking and acting rationally and humanely.
Deep Learning is a subset of Artificial Intelligence – a machine learning technique that teaches computers and devices logical functioning.
Artificial Neural Networks (ANN) are inspired by the biological neurons within the human body, which activate under certain circumstances resulting in a related action performed by the body in response.
You will learn everything you need to know about artificial neural networks in this module.
Activation Function is used for defining the output of a neural network from numerous inputs. Loss Function is a technique for prediction error of neural networks.
Google created Tensorflow, which is an open-source library for numerical computation and wide-ranging machine learning. This library is widely used in Python for AI ML.
This module will teach you how to implement TensorFlow from scratch.
Keras is a powerful and open-source API, which is designed to develop and evaluate deep learning models. This library is widely used in Python for AI ML.
This module will teach you how to implement Keras from scratch.
- Business Applications of Computer Vision
- Working with Images
- Introduction to CNNs
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.
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.
This module will teach you how to work with convolutions from scratch.
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.
- Business Applications of NLP
- Working with Text Data
- Libraries for working with text data
- NLP Applications - Sentiment Analysis
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.
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 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.
This module will lecture you on working with text data using various libraries in natural language processing with python.
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.
- 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
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.
- 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
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.
- Popularity-based Model
- Market Basket Analysis
- Content-based Model
- Collaborative Filtering
- Hybrid Recommendation Systems
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.
A popularity-based model is a recommendation system, which operates based on popularity or any currently trending models.
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.
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 is a collective usage of algorithms where there are numerous strategies for identifying similar users or items to suggest the best recommendations.
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
Data sets from the industry
A campaign to sell personal loans
Predict Potential Customers
Feature Engineering & Model Tuning
Construction Material Strength
Bank Customer Segmentation
Identify Street View House Numbers
Natural Language Processing
Sarcastic News Detection
e-Commerce Recommendation System
Faculty and Mentors
The program brings together leading academicians and industry experts to give you a practical understanding of core concepts. While varied in their experiences, they are all motivated by the goal of inspiring a love for AI and Machine Learning in you.
Prof. Dan Mitchell
Dr. Abhinanda Sarkar
Prof. Mukesh Rao
Dr. Sunil Kumar
GM - Engineering Innovation
Industry Mentors from Top Organisations
Software Engineer, Machine Learning, Meta
Senior Machine Learning Engineer, Adobe
Senior Data Scientist, Paper
Vybhav Reddy K C
Senior Data Scientist, Socure
Staff Data Scientist, One Concern
Senior Research Data Scientist | Alternative Data, RBC Capital Markets
Senior Data Scientist, Cvent
Senior Data Scientist, Advanced Analytics, Gordon Food Service
Senior Data Engineer, CGI
Assistant Professor, Nipissing University
Senior Data Scientist, Kroll
Senior Data Scientist, Wider Circle
Machine Learning Engineer, Highmark Inc.
Lead Data Scientist, TELUS Communications
Senior Software Engineer 1 - Data, Tech Lead, BenchSci
Data Scientist, Teck Resources Limited
Marcelo Guarido de Andrade
Senior Data Scientist and Head of the CREWES Data Science Initiative, University of Calgary
Staff Data Scientist, AI/ML, Walmart
Teaching Assistant to Professor Stuart Urban for Quantitative Financial Analysis course., Johns Hopkins University Carey School of Business
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
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.
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.
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
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.
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.
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
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
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.
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!
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.
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.
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.
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.
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.
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.
Learner Feedback on Mentorship and PM Support
Good sessions! The instructor was above and beyond the case study, explaining the perspective of how the problem can be confronted and analyzed.READ MORE
The mentor session was super helpful! Nirupam was great! He was patient, addressed my individual concerns as well as giving me broad industry-level advice as well as provided insight into the concerns of the typical hiring manager. He also gave me great advice on the roles I should pursue based on my professional background, region, and interests.READ MORE
Thank you all so much for your efforts and care during my learning journey. I'm so happy and thankful. I have already received amazing job offers. This is one of my greatest accomplishments in life. ;-)READ MORE
Starting at 317 USD/month*
Program Fees: 3800 USD
Pay in InstallmentsRecommended
As low as 317 USD/month
for 12 monthsView All Installment Plans
Upfront Payment & Referral
*Subject to approval by payment partners
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
“This program helped me gain hands-on skills with guidance from industry practitioners. And this is just what employers require.”
Information Systems Engineer
Fill the application form
Apply by filling a simple online application form.
Go through a screening call with the Admission Director’s office.
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: 9th Feb 2023Apply 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 email@example.com
Batch Start Dates
11th Feb 2023
Frequently Asked Questions
No, PGP-AIML is an online professional certificate program offered by the McCombs School of Business in collaboration with Great Learning. Since it is not a degree/full-time program offered by the university, therefore, there are no grade sheets or transcripts for this program by the university. You will receive marks on each assessment to test your understanding and marks on each module to determine your eligibility for the certificate.
Upon successful completion of the program, i.e. after completing all the modules as per the eligibility of the certificate, you are issued a certificate from the McCombs School of Business at the University of Texas at Austin.
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.
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.
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.
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.
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.
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
Still have queries? Contact Us
Check out the program and fee details in our brochure
Oops!! Something went wrong, Please try again.
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