With growing clutches of digitization and digital transformation initiatives across several organisations in India, the demand for AI talent is expected to skyrocket.
According to the Fortune Business Insights forecast, the global Machine Learning market size was valued at $19.20 billion in 2022 and it is expected to grow from $26.03 billion in 2023 to $225.91 billion by 2030. So, get ready to make the most of it.
The 11-month Post Graduate Certificate Programme in Artificial Intelligence and Machine Learning by BITS Pilani Work Integrated Learning Programmes is designed to help working professionals like you develop a deeper understanding of AI and ML, and get equipped with knowledge on its various building blocks.
Option to pay fees using easy EMI with 0% interest.
Admission Enquiry
Please fill the below fields for fee details, programme information, and application instructions
AI & ML, stepping stone to the future
Profile of PGP-AI & ML Programme Participants
Major Organisations where Participants work
Organisations where participants are employed at the time of joining the programme
Student Speak
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Suraj Singh
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Satish Kumar Tumu speaks about his WILP experience
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Naganandini
- Programme Highlights
- Programme Curriculum
- Eligibility
- Fee Structure
- How to Apply
- Mode of Examination
- Mode of Learning
Programme Highlights
- Post Graduate Programme in Artificial Intelligence and Machine Learning is a BITS Pilani Work Integrated Learning Programme (WILP). BITS Pilani Work Integrated Learning Programmes are UGC approved.
- Comprehensive and rigorous curriculum covering key concepts and technologies of Artificial Intelligence and Machine Learning.
- An 8-week Capstone project where you will work towards solving a Data Science related business problem under the mentorship of BITS Pilani faculty members and senior industry practitioners.
- Two Immersion modules of 2-days each at a BITS Pilani Campus or Online.
- Access to BITS Pilani instructors through technology-enabled contact classes which can be accessed from anywhere, Q&A support, and discussion forums.
- Participants who successfully complete the programme will become members of an elite & global community of BITS Pilani Alumni.
- Option to submit fee using easy-EMIs with 0% interest.
Programme Curriculum
The 11-month Post Graduate Programme in Artificial Intelligence and Machine Learning consists of 6 Courses and a Capstone Project
COURSE 1: REGRESSION (5 WEEKS)
Regression is a widely used statistical learning method, and this course will enable participants to have a deeper understanding of regression models both from theoretical and implementation perspective.
The course covers concepts such as lasso regression, ridge regression and the interpretability of the predicted models.
- Overview of certificate programme in ML & AI
- Introduction to Regression
- Mathematics Foundations
- Model Building using Least squares
- Model Accuracy & Selection
- Overfitting
- Interpretability of regression models
COURSE 2: FEATURE ENGINEERING (4 WEEKS)
Feature Engineering is an important step to develop and improve performance of Machine Learning models. In this course, students will learn different data wrangling techniques that help transforming the raw data to an appropriate form on which learning algorithms can be applied.
This course enables students to identify and implement appropriate feature extraction and pre-processing techniques. The Visualization techniques will also be taught in this course.
- Overview of Feature Engineering
- Data Preprocessing
- Dimensionality Reduction
- Visualization
COURSE 3: CLASSIFICATION (9 WEEKS)
The course on Classification lays down a strong foundation on the algorithmic perspective of popular classification algorithms - k-NN, Naïve Bayes, Decision Tree, Logistic Regression and SVM. The implementation details of these models along with tuning of parameters will be illustrated. The course also covers concepts such as ensemble methods like bagging, boosting, Random Forest, and interpretability of the predicted models.
- Overview of the Classification Module
- Nearest-neighbour Methods
- Naïve Bayes Classifier
- Logistic Regression
- Decision Tree
- Optimization Foundations for Support Vector Machines
- Support Vector Machines
- Support Vector Machines in overlapping class distributions & Kernels
- Ensemble Methods
COURSE 4: UNSUPERVISED LEARNING & ASSOCIATION RULE MINING (7 WEEKS)
The course on Unsupervised Learning & Association Rule Mining focuses in finding natural groups or clusters that are present in the data. The course will cover lustering algorithms like K-means, Hierarchical & DBSCAN algorithms, Hidden Markov Models for time series prediction, and market basket analysis to generate the interesting rules from a transactional database.
- Introduction to Unsupervised Learning, Clustering
- K-Means Algorithm, K-Means – Variations, Detecting Outliers
- Math Fundamentals for EM Algorithm, EM Algorithm, Clustering for Customer Segmentation
- Hierarchical Clustering
- Density Based Clustering, Clustering for Anomaly Detection
- Assessing Quality of Clustering, Significance of Clustering - Interpreting/ summarizing Clusters by businesses
- Association Rule Mining, Apriori Algorithm
- Time series Prediction and Markov Process, Hidden Markov Model
Course 5: Text Mining (5 weeks)
Text mining is the process of deriving high-quality information from text and this is the fifth course of the program. This course aims to equip students with adequate knowledge in extracting the relevant text data and skills to identify patterns therein. This course covers topics like converting documents to vectors, Parts of Speech Tagging, Topic modelling, sentiment analysis and recommender systems.
- Document vectorization and Parts of Speech Tagging
- Introduction to Part of speech tagging, Part of speech tagging using HMM-1, Implementing POS Tagging in Python
- Topic modelling using LDA
- Introduction to Sentiment Analysis
- Recommender Systems
Course 6: Deep Learning and ANN (6 weeks)
Deep learning is an evolving subfield of Machine Learning and this course starts with traditional Neural Networks followed by sequential networks, Convolution Networks, Autoencoders and Generative deep learning models. The implementation details of these deep learning models along with tuning of the parameters will be illustrated in this course.
- Artificial Neural Network
- Sequence Modeling in Neural Network
- Deep Learning
- Convolution Networks with Deep Learning
- Autoencoders with Deep Learning
- Generative deep learning models
COURSE 7: CAPSTONE PROJECT (8 WEEKS)
During the 8-week Capstone Project, participants will work in teams to design and solve a real-world business problem encompassing data science pipeline using AI&ML techniques under the mentorship of BITS Pilani faculty members and senior Industry practitioners.
REFRESHER COURSE
In addition to the Curriculum above, participants will have the option of taking an optional course on Python at the beginning of the Programme. This will allow participants to revisit essential concepts that will help in all other courses during the programme. Topics covered include Introduction to Python programming and installation, Data Types, Program constructs, Numpy, Pandas, Matplotlib, and Debugging python programs
Eligibility
Minimum eligibility to apply:
Employed professionals holding BE/ B.Tech. or equivalent, and working in relevant fields are eligible to apply.
Candidates holding M.Sc. in Mathematics or Statistics, and working in relevant roles are also eligible to apply to this programme.
A working knowledge of languages such as Python is recommended.
Fee Structure
For details on Fee Structure, please click here to download the Programme Brochure
How to Apply
For application instructions, programme information and fee structure, please click here to download the Programme Brochure
Mode of Examination
Mode Of Examination
Mode of Examinations applicable for students admitted in Batch starting in Oct/Nov 2024:
Comprehensive examinations will be conducted for each Course in the programme. These exams are typically conducted at the end of Course 3 (for Courses 1, 2, 3) and for Courses 4, 5, 6 before starting the Capstone Project. These examinations are mostly scheduled on Friday, Saturday or Sunday. Students need to appear in person for taking the examinations at the institution’s designated examination centres as per the examination schedule, Instructions, rules and guidelines announced before every examination.
For Indian Students:
Students can take their examination at any of our 33 designated examination centres in India at the following locations:
- South Zone: Bangalore - North, Bangalore - Central, Bangalore - South, Bangalore - East, Chennai - North, Chennai - Central, Chennai - South, Hyderabad, Secunderabad, Vijayawada, Visakhapatnam, Kochi, Thiruvananthapuram and Coimbatore.
- North Zone: Delhi, Gurugram, Noida, Jaipur, Chandigarh, Lucknow and Pilani.
- West Zone: Mumbai, Navi-Mumbai, Pune, Pune - Pimpri Chinchwad, Goa, Ahmedabad, Indore and Nagpur.
- East Zone: Kolkata, Bhubaneswar, Guwahati and Jamshedpur.
For International Students:
- In addition to the above locations, the institution also has a designated international examination centre, located in Dubai.
- To facilitate the learning of international students, applying from any other location except India and Dubai, the mode of examinations will be online, which can be availed by meeting the requirements of the institute.
- To know more about the requirements for online examinations, Click here
- Indian students, who are temporarily based out of India, can also avail of online examinations on request by meeting the above-mentioned requirements of the institute.
Mode of Learning
Mode of Learning
The Mode of Learning used in this programme is called - Work Integrated Learning. Internationally, Work Integrated Learning (WIL) is defined as "An educational approach involving three parties - the student, educational institution, and employer organization(s) - consisting of authentic work-focused experiences as an intentional component of the curriculum. Students learn through active engagement in purposeful work tasks, which enable the integration of theory with meaningful practice that is relevant to the students' discipline of study and/or professional development*.
An education model can be considered as WIL if and only if:
- The programs are designed and developed by the institute in collaboration with industry.
- Work-focused experiences form an active part of the curriculum.
- The program structure, pedagogy and assessment enable integration of theory-with relevant practice.
The innovative Work Integrated Learning Programs (WILP) of BITS Pilani are quite aligned with the above definition and requirements. The programs are designed in collaboration with its industry partners, subject matter experts from industry and academia that enable the students to remain relevant in their chosen profession, grow in their career and retain the habit of lifelong learning. Case studies, simulation exercises, labs and projects further strengthen this integration.
The WILP of BITS Pilani is comparable to its campus-based programs in terms of structure, rigor, instruction, labs, assessment, faculty profile and learning support. The pervasive adoption of technology in all its academic processes makes the same high-quality education of BITS Pilani available to the aspirants at scale with the required flexibility.
Key Benefits of BITS Pilani WILP
1)Students can pursue the programme without any career break and along with the job.
2) The programme curriculum is highly relevant to sectors, industries and organisations they work for.
3) Effective use of technology to deliver a range of learning interventions such as faculty contact sessions, asynchronous learning, remote, virtual and cloud labs, learner support, peer to peer collaboration etc.
4) Contact sessions with faculty take place mostly over weekends or after business hours and are conducted over a technology platform that can be accessed from anywhere.
5) Comprehensive examinations will be scheduled at the end of Course 3 (for courses 1,2, 3) and Course 6 (for courses 4, 5,6) and are conducted mostly at designated examination centres distributed across the country (for details, click here to download brochure)
6) Learners can access engaging learning material which includes recorded lectures from BITS Pilani faculty members, course handouts and recorded lab content where applicable.