Univ.AI Programs

Programs

Univ.AI offers focussed certificate programs in AI & ML, each designed for tangible learning and professional outcomes for various levels of your learning journey. We also offer short free preparatory courses that help you learn the basics and get you ready for our programs. All our programs are LIVE online with intensive mentorship and foster hands-on learning.

Master ML & AI

Our flagship 43-week Master Program with 6 core courses and 2 optional electives (up to 9 weeks) provides you with a wide and deep background in Machine Learning (ML) and Artificial Intelligence (AI). 

We begin by training you in the fundamentals of Machine Learning and Data Science. Advanced Modules enable you to develop deep expertise in topics like natural language processing, generative models, and reinforcement learning. You will also put your training to work in challenging module-end projects. This program will provide you the skills that you need to stay at the bleeding edge of this field to succeed in cutting-edge corporate and research organisations.

Certification Requirement

To complete this program you need to complete AI-1, AI-2, DS-1, DS-2, AI-3, AI-5, and the two optional elective courses AI-4A and AI-4B.

Duration

43 weeks, part-time

Program Format

Live, Online

Program Fee

₹3,00,000
$4,250

Next Cohort

January 2, 2022

Programs Details

220+

Contact hours

90+

Hours of classes

90+

Hours of guided labs

45+

Faculty office hours

6+

Group projects

Program Fee & Tuition Options

Introductory Price: $4,250

List Price: $10,000

Explore Tuition Option

Who is it for?

This program is for those who seek the most challenging and rewarding careers in ML and AI and are prepared to invest time and effort to master this field.

Pre-requisites

Knowledge of python as represented by the topics covered in PyDS

If you wish to attend this program, but need assistance with the prerequisites, please enrol for our introductory Python for Data Science course or write to us at apply@univ.ai for assistance.

Program Outcomes

At the end of the program, you will be a master in creating AI & ML models in any domain you choose to pursue, such as medicine, finance and e-commerce.

Exceptional performers (those who score grade "A") get a guaranteed state-of-the-art placement in a top-tier organisation.

Scholarship

Students who are currently enrolled in full time academic programs and have more than 9 months to graduate may be eligible for a scholarship based on demonstrated need. Please indicate that you wish to be considered for a scholarship in the tuition section of your application.

Program Contents

BEGINNER

AI-1: AI Basics

You will become familiar with and gain expertise in Supervised Learning models including regression models (KNN, linear, multi, poly) and classification models (KNN, Logistic). You will then learn about Modern Neural Networks.

Topics Covered

  • Regression Models
  • ~KNN Regression
  • ~Linear Regression
  • ~Multi- Regression
  • ~Poly- Regression
  • Model Selection
  • ~Training/Validation
  • ~Cross Validation
  • Inference in Linear Regression
  • Regularization: Ridge, Lasso and Elastic Net Regressions
  • Logistic Regression 
  • Metrics 
  • ~ROC Curves
  • ~Precision and Recall
  • Data Imbalance
  • Neural Networks 1 – Perceptron and MLP, Anatomy of Neural Networks and Design Choices
  • Neural Networks 2 – Fitting Neural Networks
  • Project Week

DS-1: Data Science Basics

You will learn how to get, clean, and process data from different sources. You will then gain skills in exploratory data analysis, visualization, and communication. You will learn to build classification and recommendation engines.

Topics Covered

  • Http and scraping
  • Exploratory Data Analysis
  • Visualisation basics
  • SQL (and sqlite, with pandas)
  • Viz for communication, Dashboards
  • Data Analysis pipelines
  • Using (not theory of) word embeddings and similarity search
  • Collaborative Filtering 
  • Recommendations‍ engines
  • Project Week

AI-2: Convoluted Neural Networks

Continue your data science journey with convolutional neural networks. Obtain a deeper intuition with network architecture choices, activation functions feed forward and auto encoders. At the end of this course, you will be able to run advanced machine learning models and apply them to practical image recognition problems.

Topics Covered

  • Feed Forward Neural Networks
  • ~Network Architecture
  • ~Design Choices
  • Backpropagation and Optimizers
  • Regularization for Neural Networks
  • ~Early Stopping
  • ~Data Augmentation
  • ~Dropout and Batch Normalization
  • Convolutional Neural Networks
  • ~Basic Concepts and Architectures
  • ~Receptive Fields, Strides and Saliency Maps
  • ~State of the art CNNs
  • Neural Net Transfer Learning
  • Compression and Distillation
  • Auto-encoders
  • Generative Adversarial Networks
  • Project- Week

AI-3: Language Models

This is an advanced course for developing proficiency with Natural Language Processing. You will start with the traditional language models, learn about word embeddings, attention and then move on to transformer models. At the end of this course, you will be able to build efficient language models, and tell how well they are performing.

Topics Covered

  • Traditional language modelling
  • Embeddings
  • RNNs, GRUs and LSTMs
  • Language Modelling (predict next word) with RNNs
  • Attention and Seq2Seq models
  • Transformer Models
  • BERT, ELMO and GPT
  • Project- Week

DS-2: Data Science II

You will develop your ability to use generative models and clustering. You will learn about text and tree models, ensembles, recommendation systems, clustering, and Bayesian Statistics.

Topics Covered

  • Clustering: K-means
  • Naive Bayes
  • Bayesian Models
  • Trees
  • Random Forests
  • Ensemble Methods
  • ~Bagging, Boosting and Stacking
  • Model Interrogation, Metrics and Validation
  • Project Week

AI-5: Productionizing AI (MLOps)

This is a 8 weeks (plus 4-6 weeks of extended project) hands-on course on industrial AI concepts & practices. This advanced course is ideal for those who have completed AI-3, AI-4A or AI-4B, or have equivalent preparation to join this course directly. At the end of the course, you will be proficient at applying cutting-edge skills to solve real-world problems. You will be well prepared for top employment opportunities worldwide. We guarantee top-tier placements to exceptional performers in the program (who complete the program with an ‘A’ grade). Direct admission (for those who have not taken AI-3, AI-4A or AI-4B) to the course is through an application followed by an interview.

Topics Covered

AI-4B: Generative Models

This advanced course will give an overview of network building blocks, followed by a review of Generative Adversarial Networks and their applications. The course also touches on latent space interpretation. At the end of this module, you will be able to build effective generative adversarial networks.

Topics Covered

  • Network Building Blocks
  • Generative Adversarial Networks (GANs)
  • Mode Collapse 
  • Latent Space Representation

AI-4A: Reinforcement Learning

This advanced course will provide a fundamental understanding of the concepts behind Reinforcement Learning and how to apply them to real-world problems. The course covers the basic concepts, dynamic programming, Q-learning and Policy Gradient Methods. At the end of this module, you will be able to efficiently work with reinforcement learning problems.

Topics Covered

  • Reinforcement Learning
  • Markov Decision Processes
  • Bellman equation
  • Q- Learning
Advanced
Master
Elective

Certificate in Data Science

This 29-week program with 4 core courses and 1 optional elective course (up to 5 weeks) provides you with a wide and deep background in Machine Learning (ML) and Artificial Intelligence (AI).

The program starts off with fundamental skills in Machine Learning and Data Science. Advanced Modules build on this for you to develop deep expertise in Data Science and Engineering including large-data, databases, cluster develops, and performant applications. You will also put your training to work in challenging module-end projects.

Certification Requirement

To complete this program you need to complete AI-1, DS-1, DS-2, AI-5, and, optionally, one of the two offered elective courses, AI-4A or AI-4B.

Duration

29 weeks, part-time

Program Format

Live, Online

Program Fee

₹2,35,000
To be announced

Next Cohort

January 2, 2022

Programs Details

150+

Contact hours

60+

Hours of classes

60+

Hours of guided labs

30+

faculty office hours

4+

Group projects

Program Fee & Tuition Options

To be announced

Who is it for?

This program is for those who seek to specialise in Data Science and Machine Learning.

Pre-requisites

Knowledge of python as represented by the topics covered in PyDS

If you wish to attend this program, but need assistance with the prerequisites, please enrol for our introductory Python for Data Science course or write to us at apply@univ.ai for assistance.

Program Outcomes

Upon completion, you will be a wizard at building cutting-edge Data Science and AI applications with complex embedded models, and state-of-the-art AI pipelines.


Exceptional performers (those who score grade "A") get a guaranteed state-of-the-art placement in a top-tier organisation.

Scholarship

Students who are currently enrolled in full time academic programs and have more than 9 months to graduate may be eligible for a scholarship based on demonstrated need. Please indicate that you wish to be considered for a scholarship in the tuition section of your application.

Program Contents

BEGINNER

AI-1: AI Basics

You will become familiar with and gain expertise in Supervised Learning models including regression models (KNN, linear, multi, poly) and classification models (KNN, Logistic). You will then learn about Modern Neural Networks.

Topics Covered

  • Regression Models
  • ~KNN Regression
  • ~Linear Regression
  • ~Multi- Regression
  • ~Poly- Regression
  • Model Selection
  • ~Training/Validation
  • ~Cross Validation
  • Inference in Linear Regression
  • Regularization: Ridge, Lasso and Elastic Net Regressions
  • Logistic Regression 
  • Metrics 
  • ~ROC Curves
  • ~Precision and Recall
  • Data Imbalance
  • Neural Networks 1 – Perceptron and MLP, Anatomy of Neural Networks and Design Choices
  • Neural Networks 2 – Fitting Neural Networks
  • Project Week

DS-1: Data Science Basics

You will learn how to get, clean, and process data from different sources. You will then gain skills in exploratory data analysis, visualization, and communication. You will learn to build classification and recommendation engines.

Topics Covered

  • Http and scraping
  • Exploratory Data Analysis
  • Visualisation basics
  • SQL (and sqlite, with pandas)
  • Viz for communication, Dashboards
  • Data Analysis pipelines
  • Using (not theory of) word embeddings and similarity search
  • Collaborative Filtering 
  • Recommendations‍ engines
  • Project Week

DS-2: Data Science II

You will develop your ability to use generative models and clustering. You will learn about text and tree models, ensembles, recommendation systems, clustering, and Bayesian Statistics.

Topics Covered

  • Clustering: K-means
  • Naive Bayes
  • Bayesian Models
  • Trees
  • Random Forests
  • Ensemble Methods
  • ~Bagging, Boosting and Stacking
  • Model Interrogation, Metrics and Validation
  • Project Week

AI-5: Productionizing AI (MLOps)

This is a 8 weeks (plus 4-6 weeks of extended project) hands-on course on industrial AI concepts & practices. This advanced course is ideal for those who have completed AI-3, AI-4A or AI-4B, or have equivalent preparation to join this course directly. At the end of the course, you will be proficient at applying cutting-edge skills to solve real-world problems. You will be well prepared for top employment opportunities worldwide. We guarantee top-tier placements to exceptional performers in the program (who complete the program with an ‘A’ grade). Direct admission (for those who have not taken AI-3, AI-4A or AI-4B) to the course is through an application followed by an interview.

Topics Covered

AI-4B: Generative Models

This advanced course will give an overview of network building blocks, followed by a review of Generative Adversarial Networks and their applications. The course also touches on latent space interpretation. At the end of this module, you will be able to build effective generative adversarial networks.

Topics Covered

  • Network Building Blocks
  • Generative Adversarial Networks (GANs)
  • Mode Collapse 
  • Latent Space Representation

AI-4A: Reinforcement Learning

This advanced course will provide a fundamental understanding of the concepts behind Reinforcement Learning and how to apply them to real-world problems. The course covers the basic concepts, dynamic programming, Q-learning and Policy Gradient Methods. At the end of this module, you will be able to efficiently work with reinforcement learning problems.

Topics Covered

  • Reinforcement Learning
  • Markov Decision Processes
  • Bellman equation
  • Q- Learning
Advanced
Master
Elective

Certificate in Deep Learning

This 29-week program with 4 core courses and 1 optional elective course (up to 5 weeks) provides you with a wide and deep background in Machine Learning (ML) and Artificial Intelligence (AI).

This certification program trains you to be proficient at Deep Learning with specialised knowledge of Natural Language Processing, Generative Models, and Reinforcement Learning. It prepares you for top jobs in Artificial Intelligence (AI) and Deep Learning as well as for research opportunities and advanced studies. You will also put your training to work in challenging module-end projects.

Certification Requirement

To complete this program you need to complete AI-1, DS-1, DS-2, AI-5, and, optionally, one of the two offered elective courses, AI-4A or AI-4B.

Duration

29 weeks, part-time

Program Format

Live, Online

Program Fee

₹2,35,000
$3,250

Next Cohort

January 2, 2022

Key Features

150+

Contact hours

60+

Hours of classes

60+

Hours of guided labs

30+

Faculty office hours

4+

Group projects

Program Fee & Tuition Options

₹2,35,000

Explore Tuition Options

Introductory Price: $3,250

List Price: $7,500

Explore Tuition Option

Who is it for?

This program is for those who particularly seek to specialise in Deep Learning.

Pre-requisites

Knowledge of python as represented by the topics covered in PyDS

If you wish to attend this program, but need assistance with the prerequisites, please enrol for our introductory Python for Data Science course or write to us at apply@univ.ai for assistance.

Program Outcomes

Upon completion, you will be a wizard at building cutting-edge Data Science and AI applications with complex embedded models, and state-of-the-art AI pipelines.
For those who finish the final course, AI 5, with a Grade A, we guarantee them a top-tier placement.

Scholarship

Students who are currently enrolled in full time academic programs and have more than 9 months to graduate may be eligible for a scholarship based on demonstrated need. Please indicate that you wish to be considered for a scholarship in the tuition section of your application.

Program Contents

BEGINNER

AI-1: AI Basics

You will become familiar with and gain expertise in Supervised Learning models including regression models (KNN, linear, multi, poly) and classification models (KNN, Logistic). You will then learn about Modern Neural Networks.

Topics Covered

  • Regression Models
  • ~KNN Regression
  • ~Linear Regression
  • ~Multi- Regression
  • ~Poly- Regression
  • Model Selection
  • ~Training/Validation
  • ~Cross Validation
  • Inference in Linear Regression
  • Regularization: Ridge, Lasso and Elastic Net Regressions
  • Logistic Regression 
  • Metrics 
  • ~ROC Curves
  • ~Precision and Recall
  • Data Imbalance
  • Neural Networks 1 – Perceptron and MLP, Anatomy of Neural Networks and Design Choices
  • Neural Networks 2 – Fitting Neural Networks
  • Project Week

AI-2: Convoluted Neural Networks

Continue your data science journey with convolutional neural networks. Obtain a deeper intuition with network architecture choices, activation functions feed forward and auto encoders. At the end of this course, you will be able to run advanced machine learning models and apply them to practical image recognition problems.

Topics Covered

  • Feed Forward Neural Networks
  • ~Network Architecture
  • ~Design Choices
  • Backpropagation and Optimizers
  • Regularization for Neural Networks
  • ~Early Stopping
  • ~Data Augmentation
  • ~Dropout and Batch Normalization
  • Convolutional Neural Networks
  • ~Basic Concepts and Architectures
  • ~Receptive Fields, Strides and Saliency Maps
  • ~State of the art CNNs
  • Neural Net Transfer Learning
  • Compression and Distillation
  • Auto-encoders
  • Generative Adversarial Networks
  • Project- Week

AI-3: Language Models

This is an advanced course for developing proficiency with Natural Language Processing. You will start with the traditional language models, learn about word embeddings, attention and then move on to transformer models. At the end of this course, you will be able to build efficient language models, and tell how well they are performing.

Topics Covered

  • Traditional language modelling
  • Embeddings
  • RNNs, GRUs and LSTMs
  • Language Modelling (predict next word) with RNNs
  • Attention and Seq2Seq models
  • Transformer Models
  • BERT, ELMO and GPT
  • Project- Week

AI-5: Productionizing AI (MLOps)

This is a 8 weeks (plus 4-6 weeks of extended project) hands-on course on industrial AI concepts & practices. This advanced course is ideal for those who have completed AI-3, AI-4A or AI-4B, or have equivalent preparation to join this course directly. At the end of the course, you will be proficient at applying cutting-edge skills to solve real-world problems. You will be well prepared for top employment opportunities worldwide. We guarantee top-tier placements to exceptional performers in the program (who complete the program with an ‘A’ grade). Direct admission (for those who have not taken AI-3, AI-4A or AI-4B) to the course is through an application followed by an interview.

Topics Covered

AI-4B: Generative Models

This advanced course will give an overview of network building blocks, followed by a review of Generative Adversarial Networks and their applications. The course also touches on latent space interpretation. At the end of this module, you will be able to build effective generative adversarial networks.

Topics Covered

  • Network Building Blocks
  • Generative Adversarial Networks (GANs)
  • Mode Collapse 
  • Latent Space Representation

AI-4A: Reinforcement Learning

This advanced course will provide a fundamental understanding of the concepts behind Reinforcement Learning and how to apply them to real-world problems. The course covers the basic concepts, dynamic programming, Q-learning and Policy Gradient Methods. At the end of this module, you will be able to efficiently work with reinforcement learning problems.

Topics Covered

  • Reinforcement Learning
  • Markov Decision Processes
  • Bellman equation
  • Q- Learning
Advanced
Master
Elective
Get started

Frequently asked questions

What certifications do I get after completing the programs?

Yes. We have a 2-tier certification system for all our programs. Students who score an A grade are awarded a special Certificate of Exceptional Performance. All students who successfully complete a program receive a Certificate of Successful Completion. 


In addition, within a program, for each course you complete successfully, you get a course completion certificate and grade. 


All certificates are granted by Univ.AI, and signed by our founding faculty, who are Harvard and UCLA professors.


In addition you have a github repository of all your projects, and a detailed transcript for the program, comprising your grades in each of the courses.

What career outcomes can I expect after successfully completing Univ.AI programs?

Univ.AI students may reasonably expect to get the best jobs in the industry, commensurate with their prior experience. Folks that come to Univ.AI without any prior work experience, and expect to get top entry-level positions in Data Science, and those with prior work experience, will be able to get more senior-level jobs.


In addition, we invite Univ.AI students to apply for positions at Univ.AI’s Data Science and AI consulting division, which offers top jobs at industry-leading compensation.

How can I get accepted to a program at Univ.AI?

At Univ.AI you can take entire programs or individual courses. 


Courses

Admission to individual courses is unrestricted, and you can enroll simply by selecting a course from the “courses” page at www.univ.ai and paying the associated fee. You could reproduce an entire program by taking all the courses in any program. 


Note: If you choose to complete a program one-course-at-a-time, you will be eligible for program certification. However, you can avail yourself of placement assistance, and other facilities only after completion of program certification. 


Those enrolled into programs get access to employment, internship and other enrichment opportunities well ahead of program completion.


Programs

Admission to programs is a two step process - 

  1. Application and test

You need to complete an application at www.univ.ai, which includes an aptitude test.

  1. Interview

Based on your application and test scores, if you qualify for the next step, you will be invited for an interview. Interviews are simple and meant to assess your background, preparedness, and motivation.

 

Based on your interview and eligibility, you are offered admission to the program of your choice and offered one or more of tuition payment options (including, if eligible, a ZERO upfront fee option).


Alternative ways of getting admitted

You can take either PyDS or AI-1 as a stand-alone course, and if you score an A in PyDS, or a B+ or higher in AI-1 and secure admission to the program of your choice.

Who can attend Univ.AI programs? Can I take them if I am a working professional? What if I am still studying?

Anyone! Data Science and AI are horizontal skill that increasingly professionals of all persuasions will need to know - whether they are social or physical scientists, engineers, or medical professionals. 


Our programs are part-time. Students currently enrolled at university, as well as working professionals, can apply for our programs.

How much time will I need to commit each week to do well at Univ.AI?

Programs at Univ.AI are run one-course-at-a-time, and require about 12-15 hours of your time each week, including live classes, labs, office hours, homeworks, and projects. 


Every week, you have two classes of about 90 


Each session lasts about 90 minutes. 


If you can commit that kind of time every week to learn a new field, then we welcome you to apply for Univ.AI programs in Data Science, and AI irrespective of your stage of career.

How are students graded and evaluated?

We conduct both group and individual evaluations. Evaluations are based on your class performance (exercises and quizzes), labs, homeworks, and projects. We ask that you always participate and attempt exercises even if you get them wrong: participation counts towards your grade. 


Detailed evaluation criteria may vary from course to course, and are published prior to each course. Below is a representative grading structure:

  • Quiz & Exercises: 25%
  • Homework assignments: 35%
  • Participation: 10%
  • ~Forums: 5%
  • ~Lectures: 5%
  • Course-end Project: 20%


What are the prerequisites for Master/Certificate programs at Univ.AI?

Our Master ML & AI, Certificate in Deep Learning and Certificate in Data Science programs require you to know (high-school level) -  linear algebra, calculus, and basic statistics. In addition we require you to know Python programming. 


We conduct an entrance exam as part of the application to test you on the prerequisites to ensure that you are well prepared to succeed in our programs.

What is a learner’s weekly schedule at Univ.AI? What time are classes and other live sessions held?

They are currently held at - 

USA

9:30 a.m. EDT (8:30 am EST)

6:30 am PDT (5:30 am PST)

 

India

7:00 pm IST.

Are programs at Univ.AI full time or part time?

At present we offer only part-time programs. They are designed such that working professionals can expect to finish them in a 6-9 month period with 12-15 hours of work per week (with breaks between courses). 


The workload is calibrated to keep the rate of learning new concepts at a level that allows you enough time to learn them thoroughly, and explore all practical nuances of application.

How are programs delivered at Univ.AI?

Each of our programs comprise a series of 5-6 week courses. With few exceptions, most individual courses are 5-weeks long. 


The learning system is entirely LIVE and comprises Interactive classes, labs, office hours, homework assignments, and an end-of-course project. 


Each week, for the first 4 weeks, there are two classes, one-to-labs, and office hours. In addition you are assigned homework. The final week is for a group project.


There is a short break at the end of each course and the start of the next one. For more details, please refer to the how it works page of our website.

More answers

Working professionals & students from diverse backgrounds go on to sought-after career opportunities.

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