Master of Science in Artificial Intelligence

This course helps students translate advanced mathematical/statistical/scientific concepts into code. This is a module for writing code to solve real-world problems. It introduces programming concepts (such as control structures, recursion, classes and objects) assuming no prior programming knowledge, to make this course accessible to advanced professionals from scientific fields like Biology, Physics, Medicine, Chemistry, Civil & Mechanical Engineering etc.  After building a strong foundation for converting scientific knowledge into programming concepts, the course advances to dive deeply into Object-Oriented Programming and its methodologies. It also covers when and how to use inbuilt-data structures like 1-Dimensional and 2-Dimensional Arrays before introducing the concepts of computational complexity to help students write optimised code using appropriate data structures and algorithmic design methods.

This course is designed to provide students with a comprehensive overview of the key concepts, techniques, and applications of AI. This course covers the history and evolution of AI, fundamental theories, and essential algorithms, including search methods, knowledge representation, machine learning, and neural networks. Students will explore the practical applications of AI in various domains such as robotics, natural language processing, computer vision, and expert systems, gaining an understanding of how AI technologies are transforming industries and society.

Through a mix of theoretical lectures and hands-on exercises, students will develop a solid grounding in AI principles and practices. They will engage in projects and case studies that illustrate real-world AI applications, enhancing their problem-solving and critical-thinking skills. By the end of the course, students will have a thorough understanding of AI fundamentals and be prepared to delve deeper into specialised AI topics, positioning themselves for success in advanced courses and professional roles within the field of artificial intelligence.

This course focuses on building basic classification and regression models and understanding these models rigorously both with a mathematical and an applicative focus. The module starts with a basic introduction to high dimensional geometry of points, distance-metrics, hyperplanes and hyperspheres. We build on top this to introduce the mathematical formulation of logistic regression to find a separating hyperplane. Students learn to solve the optimization problem using vector calculus and gradient descent (GD) based algorithms. The module introduces computational variations of GD like mini-batch and stochastic gradient descent. Students also learn other popular classification and regression methods like k-Nearest Neighbours, NaiveI Bayes, Decision Trees, Linear Regression etc. Students also learn how each of these techniques under various real world situations like the presence of outliers, imbalanced data, multi class classification etc. Students learn bias and variance trade-off and various techniques to avoid overfitting and underfitting. Students also study these algorithms from a Bayesian viewpoint along with geometric intuition. This module is hands-on and students apply all these classical techniques to real world problems.

This course is aimed at providing students with a comprehensive understanding of how to design and implement systems that exhibit intelligent behaviour. This course explores a range of topics including expert systems, autonomous agents, knowledge representation, and reasoning. Students will delve into the principles of how these systems can mimic human decision-making processes, adapt to changing environments, and perform complex tasks autonomously.

The course integrates theoretical concepts with practical applications through hands-on projects and case studies, allowing students to develop and deploy intelligent systems in real-world scenarios. By working with advanced tools and techniques, students will learn to build systems that can handle uncertainty, learn from experience, and interact effectively with users and other systems. Upon completion, students will be equipped with the skills to create sophisticated AI solutions and contribute to the development of cutting-edge intelligent technologies in their professional careers.

This course provides a strong mathematical and applicative introduction to Deep Learning. The course starts with the perceptron model as an over simplified approximation to a biological neuron. We motivate the need for a network of neurons and how they can be connected to form a Multi Layered Perceptron (MLPs). This is followed by a rigorous understanding of back-propagation algorithms and its limitations from the 1980s. Students study how modern deep learning took off with improved computational tools and data sets. We teach more modern activation units (like ReLU and SeLU) and how they overcome problems with the more classical Sigmoid and Tanh units. Students learn weight initialization methods, regularisation by dropouts, batch normalisation etc., to ensure that deep MLPs can be successfully trained.

The course teaches variants of Gradient Descent that have been specifically designed to work well for deep learning systems like ADAM, AdaGrad, RMSProp etc. Students also learn AutoEncoders, VAEs and Word2Vec as unsupervised, encoding deep-learning architectures. We apply all of the foundational theory learned to various real world problems using TensorFlow 2 and Keras. Students also understand how TensorFlow 2 works internally with specific focus on computational graph processing.

This course is designed to bridge the gap between data theory and real-world applications. This course focuses on the end-to-end process of data analytics, including data collection, cleaning, exploratory data analysis, and visualisation. Students will learn how to apply statistical methods and machine learning techniques to analyse and interpret complex datasets, uncovering actionable insights that drive strategic decision-making across various domains such as business, healthcare, and technology.

The course combines theoretical instruction with hands-on projects, allowing students to work with real datasets and employ state-of-the-art tools and software. By engaging in case studies and practical exercises, students will develop the skills necessary to tackle data-driven problems and present their findings effectively. Upon completion, students will be well-equipped to leverage data analytics to solve real-world challenges and contribute to data-informed decision-making processes in their professional careers.

This course builds professional skills in developing and deploying software robots using the UiPath RPA platform. Learners will explore core RPA concepts, automate business processes with UiPath tools, refresh essential programming skills, and practice data scraping, debugging, and working with Orchestrator queues and assets.

This course is designed to bridge the gap between theoretical AI concepts and their real-world applications across various industries. This course explores how AI technologies are implemented to solve industry-specific challenges and drive innovation in fields such as healthcare, finance, manufacturing, and retail. Students will examine case studies and practical examples of AI solutions that optimise processes, enhance decision-making, and create value for businesses and organisations.

Through hands-on projects and collaborative assignments, students will gain experience in deploying AI systems and tools tailored to industry needs. They will work with real-world datasets and use industry-standard platforms to develop and implement AI solutions, learning to address unique operational and strategic problems. By the end of the course, students will be equipped with the skills and insights needed to apply AI technologies effectively in various industrial contexts, making them valuable assets in transforming and advancing industry practices through artificial intelligence.

This course builds foundational skills in cloud architecture and hands-on cloud operations using virtualization, AWS compute services, load balancers, auto scaling, VPC networking, distributed object storage, and relational database migration. Concepts are taught in a platform-agnostic manner so learners can apply virtualization, VPC design, load balancing, distributed storage, and cloud database scaling across AWS, Azure, and GCP.

This course introduces learners to the foundations of Generative AI and its practical applications. Students will explore text generation with large language models, image creation in computer vision, and the deployment of real-world generative systems. Through hands-on projects—including building chatbots and AI agents—learners gain the skills needed to create and deploy functional generative AI applications.

This course empowers learners to build AI-driven business solutions without writing code. Using Amazon Q Business, students will create AI-powered applications, automate workflows, and develop clear, data-driven insights. The program emphasizes intuitive design, scalability, and delivering measurable business impact through no-code AI tools.

This course is a hands-on course covering JavaScript from basics to advanced concepts in detail using multiple examples. We start with basic programming concepts like variables, control statements, loops, classes and objects. Students also learn basic data-structures like Strings, Arrays and dates. Students also learn to debug our code and handle errors gracefully in code. We learn popular style guides and good coding practices to build readable and reusable code which is also highly performant. We then learn how web browsers execute JavaScript code using V8 engine as an example. We also cover concepts like JIT-compiling which helps JS code to run faster. This is followed by slightly advanced concepts like DOM, Async-functions, Web APIs and AJAX which are very popularly used in modern front end development. We learn how to optimise JavaScript code to run on both mobile apps and mobile browsers along with Desktop browsers and as desktop apps via ElectronJS. Most of this course would be covered via real world examples and by learning from JS code of popular open-source websites and libraries.

This course builds the skills needed to design and architect modern data ecosystems. Learners will explore advanced database design, data modeling, cloud integration, and data governance to create secure, efficient, and scalable enterprise data solutions.

This course advances your Java development skills through real-world projects in web services, security, persistence, and DevOps. Learners will work with industry-standard tools such as Spring Boot, Jenkins, Selenium, Docker, and Spring Data JPA while building scalable applications using REST APIs, GraphQL, Swagger, and modern configuration management practices.

This course introduces basic probability theory, statistical methods, and computational algorithms to perform rigorous data analysis. Students learn foundational concepts such as random variables, histograms, PMF, PDF, CDF, and study discrete and continuous distributions including Bernoulli, Binomial, Poisson, Gaussian, Exponential, Pareto, and log-normal. The course covers statistical measures such as mean, median, percentiles, quantiles, variance, and interquartile range, along with their practical applications. Learners explore conditional probability, Bayes theorem, non-parametric statistics, correlation methods, hypothesis testing, and A/B experimentation. Computational tools such as Bootstrapping, Monte-Carlo methods, and RANSAC are applied to real-world data problems.

This course strengthens problem-solving skills through extensive practice with algorithms and data structures. Learners will apply Python-based techniques including basic algorithms, sorting, trees, graphs, BFS, DFS, A* search, and core Python implementations.

This is a project-based course, with the aim of building the required skills for creating web-based software systems. The course covers the entire lifecycle of building software projects, from requirement gathering and scope definition from a product document, to designing the architecture of the system, and all the way to delivery and maintenance of the software system.

The course covers both frontend, which is, building browser-based interfaces for users, using frontend web frameworks, and also building the backend, which is the server running an API to serve the information to the frontend, and running on an SQL or similar database management system for storage.

All aspects of delivering a software project, including security, user authentication and authorisation, monitoring and analytics, and maintaining the project are covered. The course also covers the aspects of project maintenance, like using a version control system, setting up continuous integration and deployment pipelines and bug trackers.

The Applied Computer Science Project will focus on the outcome of how computer science and its associated tools as a field of study can be beneficial to other fields of study or how such tools can be applied to  modify existing processes for better outcomes. Students will apply AI techniques—such as machine learning models, neural networks, computer vision, or NLP—to solve a real-world computational problem. The project should demonstrate an understanding of AI theory and its application to practical scenarios.

Students will gain real-world skills in building, securing, and monitoring scalable applications using industry-standard tools like Prometheus, Jaeger, ArgoCD, gRPC, and Grafana.

This course teaches learners to scale machine learning solutions in the cloud using Azure Machine Learning. Students will work with Azure ML Studio, the SDK, AutoML, and Azure Pipelines to deploy and monitor models. The program also covers pipeline automation, Kubernetes security, and API management for building robust, production-ready ML systems.

This course is dedicated to equipping students with the skills to develop and apply models that forecast future trends and behaviours based on historical data. This course covers a range of predictive techniques, including linear and logistic regression, time series analysis, ensemble methods, and advanced machine learning algorithms. Students will learn how to build, validate, and deploy predictive models to make accurate forecasts in various domains such as finance, healthcare, and marketing. The course emphasises both theoretical understanding and practical application, with students engaging in hands-on projects and case studies that demonstrate real-world uses of predictive modelling. By working with diverse datasets and employing state-of-the-art tools, students will gain experience in model selection, performance evaluation, and optimization. By the end of the course, students will be adept at creating robust predictive models that drive data-informed decision-making and contribute to strategic planning in their professional fields.

This course introduces learners to digital art and graphic design using Canva. Students will explore essential tools and design principles to create clean, professional, and visually compelling compositions with ease.

This course builds practical expertise in cloud computing, microservices, and serverless technologies. Learners will master AWS fundamentals, design and deploy scalable full-stack applications, and apply modern cloud architecture patterns. Through hands-on exercises, students will work with microservices, service replication, independent scaling, and serverless REST APIs while implementing essential security best practices.

This is a hands-on course on designing responsive, modern, and lightweight UI for

web, mobile, and desktop applications using HTML5, CSS, and Frameworks like

Bootstrap 4. This course starts with an introduction to how web browsers, mobile

apps, and web servers work. We then dive into each of the nitty-gritty details of

HTML5 to build webpages. We would start with simple web pages and then

graduate to more complex layouts and features in HTML like forms, iFrames,

multimedia playback, and using web APIs. We then go on to learn stylesheets based on CSS 4 and how browsers interpret CSS files to render web pages. Once again, we use multiple real-world example web pages to learn the internals of CSS4. We learn popular good practices for writing responsive HTML and CSS code, which is also interoperable on mobile browsers, apps, and desktop apps. We would introduce students to building desktop apps using HTML and CSS using toolkits like Electron. We also study popular frameworks for front end development like Bootstrap 4, which can speed up UI development significantly.

This course strengthens intermediate-level Python skills for real-world applications across web development, data science, and automation. Learners will work with file I/O, functional programming, data structures, object-oriented Python, command-line tools, and essential libraries to build efficient, scalable programs. By the end, students will create a portfolio that demonstrates practitioner-level Python capability.

preadsheets for Data Understanding introduces students to the principles and techniques of data cleaning, handling data sets of varying sizes, and visualising data/data storytelling. Students will also learn the basics of predictive modelling from data sets. These are all introduced through the means of Microsoft Excel, the industry-standard spreadsheet program. Students will learn how to use inbuilt functions, as well as techniques such as creating and modifying pivot tables.

This course teaches learners how to track, analyze, and interpret user data using Google Analytics 4 (GA4). Students will uncover meaningful insights, optimize user experiences, and support data-driven decision-making through hands-on GA4 practice.

This course builds a strong foundation in cybersecurity, equipping learners to protect digital assets and manage risk effectively. Students will develop core skills in threat analysis, vulnerability assessment, and network defense, while learning to implement security controls, use industry-standard tools, and apply best practices to safeguard sensitive data in modern personal and professional environments.

This course develops core ethical hacking and penetration testing skills for cybersecurity professionals. Learners will practice reconnaissance, OSINT, password attacks, and vulnerability management through hands-on projects guided by expert instructors.

This is a core and foundational course which aims to equip the student with the ability to model, design, implement and query relational database systems for real-world data storage & processing needs. Students would start with diagrammatic tools (ER-diagram) to map a real world data storage problem into entities, relationships and keys. Then, they learn to translate the ER-diagram into a relational model with tables. SQL is then introduced as a de facto tool to create, modify, append, delete, query and manipulate data in a relational database. Due to SQL’s popularity, the course spends considerable time building the ability to write optimised and complex queries for various data manipulation tasks. The module exposes students to various real world SQL examples to build solid practical knowledge. Students then move on to understanding various trade-offs in modern relational databases like the ones between storage space and latency. Designing a database would need a solid understanding of normal forms to minimise data duplication, indexing for speedup and flattening tables to avoid complex joins in low-latency environments. These real-world database design strategies are discussed with practical examples from various domains. Most of this course uses the open source MySQL database and cloud-hosted relational databases (like Amazon RDS) to help students apply the concepts learned on real databases via assignments.

This course is focused on equipping students with the skills and knowledge needed to effectively incorporate AI technologies into various business and industrial processes. This course explores a wide range of strategies for integrating AI, including deployment methodologies, system interoperability, and change management. Students will learn about the challenges and best practices for implementing AI solutions in real-world environments, ensuring seamless integration with existing systems and maximising organisational benefits.

Throughout the course, students will engage in case studies, practical projects, and interactive discussions that highlight successful AI integration across different sectors. By understanding the strategic, technical, and ethical considerations involved in AI deployment, students will be prepared to lead AI initiatives and drive innovation in their organisations. This course is essential for aspiring AI professionals aiming to bridge the gap between cutting-edge AI technologies and their practical applications, ultimately contributing to the advancement of the AI field.

This course builds on foundational AWS knowledge, diving deeper into the platform's sophisticated features and services. Students will explore advanced networking configurations, security and compliance measures, and serverless architectures. Emphasis will be placed on practical applications, allowing students to design and implement complex AWS architectures, automate infrastructure management with Infrastructure as Code (IaC) tools, and optimize costs for scalable solutions.In addition to technical skills, the course covers advanced topics in containerization, orchestration with AWS services, and the development of continuous integration/continuous deployment (CI/CD) pipelines. Students will gain hands-on experience through labs and projects that simulate real-world scenarios, ensuring they can effectively deploy, manage, and scale applications on AWS. By the end of the course, students will be proficient in leveraging AWS's full potential to meet specific business requirements, ensuring security, compliance, and cost-efficiency in cloud environments.

Students will develop the skills to thrive as a digital freelancer. This course teaches marketing strategies, client management, and project delivery, with hands-on experience creating a portfolio website and working with a mock client.

This course strengthens your machine learning expertise through practical AWS-based training. Learners will build, train, and deploy models using Amazon SageMaker and design automated ML workflows with AWS Lambda and Step Functions. The program emphasizes hands-on implementation of scalable, cloud-native ML solutions.

Student will develop the skills to thrive as a digital freelancer. This course teaches marketing strategies, client management, and project delivery, with hands-on experience creating a portfolio website and working with a mock client.

This course provides in-depth training in Google Analytics 4 (GA4), enabling learners to track, analyze, and interpret user data to optimize digital experiences and support evidence-based decision-making. Learners work with acquisition reports, attribution reports, advanced GA4 displays, Google Analytics event management, and Google Cloud Looker.

This course introduces learners to autonomous aerial systems through hands-on drone robotics and advanced control techniques. Students will design, program, and test real software for real aircraft, gaining practical skills in building intelligent, autonomous flight systems.

This course prepares learners for the cloud DevOps field by developing skills in automation and deployment practices. Students work with logging, continuous integration and deployment (CI/CD), GitHub Actions, build and deployment automation, and Kubernetes to design and operate reliable and scalable cloud systems.

This course is aimed to build a strong foundational knowledge of Data Analytics tools used extensively in the Data Science field. There now are powerful data visualisation tools used in the business analytics industry to process and visualise raw business data in a very presentable and understandable format. A good example is Tableau, used by all data analytics departments of companies and in data analytics companies in various fields for its ease of use and efficiency. Tableau uses relational databases, Online Analytical Processing Cubes, Spreadsheets, cloud databases to generate graphical type visualisations. Course starts with visualisations and moves to an in-depth look at the different chart and graph functions, calculations, mapping and other functionality. Students will be taught quick table calculations, reference lines, different types of visualisations, bands and distributions, parameters, motion chart, trends and forecasting, formatting, stories, performance recording and advanced mapping. At the end of this course, students will be prepared, if they desire, to earn such industry desktop certifications as a Tableau Desktop Specialist, a Tableau Certified Associate, or a Tableau Certified Professional.

Students will gain hands-on experience with cost optimization, infrastructure monitoring, and cutting-edge cloud security practices, including Zero Trust Architecture and advanced IAM solutions.

This course equips learners with the essential skills to navigate and manage Linux servers effectively. The course begins by demystifying the Linux command line interface, learning the fundamentals students need to interact with the system. As learners progress, they will explore powerful command-line utilities like grep, sed, awk, and more, allowing them to manipulate and analyse data efficiently.Next, they will delve into the Linux file system structure, gaining a solid understanding of user permissions and access control. This knowledge is crucial for ensuring secure and organised server environments.Finally, the course empowers students to write their own shell scripts. They will learn how to craft conditional statements (if/else) and loops (for loops) to automate repetitive tasks and streamline the workflow. By the end, they will be able to write scripts to solve real-world problems and significantly boost their Linux server administration skills.

The Sensor Fusion Engineer Nanodegree program teaches core concepts in sensor fusion and perception for self-driving cars. It covers lidar, radar, cameras, and Kalman filters, with hands-on work involving real-world data, filtering, segmentation, clustering, and object tracking. Learners complete a capstone project focused on building a complete sensor fusion pipeline for autonomous vehicles.

This course trains learners to use business intelligence analytics to turn data into strategic insights. Students will learn how organizations blend diverse data sources, ensure data accuracy and consistency, and build reliable analytical models. By the end, learners will be able to create advanced BI models that support strategy, process optimization, and high-impact business decisions.

This core foundational course equips students with knowledge of Database Management Systems (DBMS) and Computer Networks.The course starts with Entity-Relationship (ER) diagrams, a visual tool for mapping real-world data storage problems.Students learn to translate ER diagrams into a relational model with tables. SQL, the standard language for relational databases, is then introduced. Students will spend significant time building proficiency in writing optimised and complex SQL queries for various data manipulation tasks. Real-world examples will be used to solidify practical knowledge.

Next, the course explores trade-offs in modern relational databases, such as storage space versus latency. Designing efficient databases requires understanding normal forms to minimise data duplication, indexing for speed improvements,and flattening tables to avoid complex joins in low-latency environments. These real-world database design strategies are discussed with practical examples.The course utilises open-source MySQL databases and cloud-hosted relational databases (like Amazon RDS) for assignments, allowing students to apply learned concepts on real databases.Following the DBMS section, the course transitions to Computer Networks. Here, students will delve into foundational concepts like the OSI model, TCP/IP model, TCP/UDP protocols, subnetting, DNS (Domain Name System), Network Address Translation (NAT), private networks, Secure Sockets Layer (SSL), and network security principles.

This course develops the ability to frame business problems, prepare and clean data, and apply predictive modelling techniques for informed decision-making. Learners also build strong data storytelling and visualization skills using Tableau, creating dashboards and visual narratives that support business insights.

This course is aimed at deepening students' understanding of cutting-edge topics in artificial intelligence. This course delves into advanced methodologies such as generative adversarial networks (GANs), meta-learning, and advanced reinforcement learning techniques. Students will explore the theoretical underpinnings and practical implementations of these sophisticated AI concepts, focusing on their applications in complex problem-solving and innovation across various domains. Through a blend of advanced theoretical discussions and hands-on projects, students will engage with state-of-the-art tools and techniques, working on real-world problems and research projects. The course encourages critical thinking and problem-solving, preparing students to tackle the challenges of implementing and advancing AI technologies. By the end of the course, students will have a robust understanding of advanced AI concepts and be well-equipped to contribute to cutting-edge research and development in the field of artificial intelligence.

This course introduces the principles and practices of Agile software development. Learners develop practical skills in Agile methodologies, sprint planning, and iterative improvement, while gaining experience with team communication, metrics, and real-world Agile workflows.

This course introduces the core skills of front-end development, enabling learners to build responsive web pages and interactive user experiences. Using HTML, CSS, JavaScript, Flexbox, and DOM events, students will create optimized, modern interfaces while working with essential web development tools and workflows.

DevOps Tools Part 2 is an advanced course designed for students building on the foundational knowledge from DevOps Tools Part 1. his course delves deeper into advanced DevOps tools and techniques that drive modern software development and operations. Students will explore sophisticated CI/CD pipelines with tools like GitLab CI/CD and Azure DevOps, and master infrastructure as code (IaC) with Terraform and AWS CloudFormation. The course emphasizes practical, hands-on experience, enabling students to automate and manage complex cloud environments effectively.In addition to advanced CI/CD and IaC, students will learn about service mesh architectures with Istio, advanced container orchestration with Kubernetes, and continuous monitoring and observability with tools such as Grafana and Jaeger. The course also covers security practices in DevOps, including integrating security tools into the CI/CD pipeline and managing secrets with HashiCorp Vault. By the end of the course, students will have the expertise to design, implement, and manage scalable, secure, and efficient DevOps workflows, preparing them for leadership roles in the field.

This course introduces foundational programming concepts used across software development, web development, and data analysis. Learners gain hands-on experience with Python programming, control flow, functions, object-oriented concepts, and basic command-line usage. The program also covers essential web technologies such as HTML and CSS to provide a well-rounded introduction to programming.

This course builds foundational to advanced skills in Android development, covering core app components, Material Design, and Google Play services. Learners will create real-world projects—including a media player and apps integrated with Google services—to gain practical, industry-ready Android expertise.

This course teaches learners how to use data to shape and optimize product strategy. Students will build data pipelines, analyze key product metrics, and apply data-driven insights to guide product innovation and drive successful outcomes.

This course will focus on the principles and techniques involved in training large language models (LLMs). This course provides an in-depth understanding of the architectures and training methodologies used to develop powerful language models like GPT-3, BERT, and their successors. Students will explore the complexities of model training, including data preprocessing, tokenization, model architecture design, and fine-tuning. Emphasis will be placed on understanding the computational resources and optimization strategies required to train LLMs effectively. Throughout the course, students will engage in hands-on projects that involve training and fine-tuning LLMs on diverse datasets, gaining practical experience with tools and frameworks such as TensorFlow and PyTorch. Case studies will illustrate the application of LLMs in natural language processing tasks, such as text generation, translation, summarization, and question answering. By the end of the course, students will be equipped with the knowledge and skills to train large language models, enabling them to contribute to the development of state-of-the-art AI systems that leverage advanced language understanding and generation capabilities

This course is designed to provide students with a comprehensive understanding of the principles and practices of robotics and automated systems. This course covers key topics such as robot kinematics, dynamics, control systems, and sensor integration. Students will explore the development and deployment of robotic systems for various applications, including manufacturing, logistics, and autonomous vehicles, gaining insights into the design, programming, and operation of robots.

Combining theoretical concepts with practical experience, the course features hands-on projects and case studies that illustrate the real-world implementation of robotics and automation technologies. Students will work with simulation tools and robotic platforms to design, test, and refine robotic systems, developing the skills necessary to address complex automation challenges. By the end of the course, students will be adept at integrating robotics and automation into various industries, driving innovation and efficiency through advanced technology solutions.

This course teaches Python developers to build autonomous AI agents using LangChain and LangGraph. Learners start with core concepts—prompt templates, chains, memory, and single-tool agents—and progress to multi-tool planning, self-critique loops, and deployment. The program concludes with advanced capabilities, including retrieval-augmented generation, long-term memory, and multi-agent collaboration.

This course is focused on exploring the intersection of artificial intelligence and human cognitive processes. This course provides an in-depth look at how cognitive computing systems mimic human thought processes, including perception, reasoning, learning, and problem-solving. Students will study the underlying principles of cognitive models, natural language processing, and machine learning techniques used to develop systems that can understand, reason, and interact in ways that approximate human cognition.

Through a combination of theoretical frameworks and practical applications, students will engage with cutting-edge tools and technologies used in cognitive computing, such as neural networks and advanced data analytics. The course includes hands-on projects and case studies that demonstrate how cognitive systems are applied in areas such as personalised recommendations, intelligent assistants, and decision support systems. By the end of the course, students will have a deep understanding of cognitive computing principles and be prepared to develop sophisticated AI solutions that enhance human-computer interactions and decision-making processes.

This course prepares learners for a career in front-end development by teaching them to build professional, responsive, and high-performance web pages. Students will use JavaScript, CSS, and modern web tools to create interactive user experiences while mastering DOM manipulation, Flexbox layouts, DOM events, and performance optimization.

This course builds job-ready digital marketing skills through hands-on practice and expert guidance. Learners will run live campaigns, analyze performance, and apply proven strategies to engage audiences and strengthen brand presence. Topics include social media advertising, content planning, audience targeting, and campaign evaluation—preparing students for real-world digital marketing roles.

This course builds practical expertise in Natural Language Processing through hands-on projects guided by industry experts. Learners will work on sentiment analysis, machine translation, speech recognition, and voice interface design, gaining the skills to develop real-world NLP applications.

This course is designed to introduce students to the core concepts and methodologies of data science. This course covers a broad range of topics, including data collection, cleaning, and preprocessing, as well as statistical analysis, data visualisation, and exploratory data analysis. Students will learn how to apply various data science techniques to extract valuable insights from large datasets, empowering them to make data-driven decisions in diverse fields such as business, healthcare, and technology. Throughout the course, students will engage in practical exercises and projects that emphasise the application of data science principles to real-world problems. By working with actual datasets and using state-of-the-art tools and software, students will develop the skills necessary to analyse, interpret, and present data effectively. Upon completion of the course, students will have a strong foundation in data science, enabling them to leverage data to solve complex problems and drive innovation in their professional careers within the realm of artificial intelligence.

This course focuses on building basic classification and regression models and understanding these models rigorously both with a mathematical and an applicative focus. The module starts with a basic introduction to high dimensional geometry of points, distance-metrics, hyperplanes and hyperspheres. We build on top this to introduce the mathematical formulation of logistic regression to find a separating hyperplane. Students learn to solve the optimization problem using vector calculus and gradient descent (GD) based algorithms. The module introduces computational variations of GD like mini-batch and stochastic gradient descent. Students also learn other popular classification and regression methods like k-Nearest Neighbours, NaiveI Bayes, Decision Trees, Linear Regression etc. Students also learn how each of these techniques under various real world situations like the presence of outliers, imbalanced data, multi class classification etc. Students learn bias and variance trade-off and various techniques to avoid overfitting and underfitting. Students also study these algorithms from a Bayesian viewpoint along with geometric intuition. This module is hands-on and students apply all these classical techniques to real world problems.

Start mastering AI-powered trading with this Nanodegree. Learn to build, backtest, and optimize sophisticated AI-driven trading models, gaining practical skills to succeed in dynamic financial markets.

This course provides a comprehensive overview of Amazon Web Services (AWS), focusing on core services and best practices for building and managing cloud-based infrastructure. Students will learn the fundamentals of cloud computing, explore key AWS services such as EC2, S3, RDS, and VPC, and gain hands-on experience in deploying and managing applications on the AWS platform. The course emphasizes practical skills, enabling students to design scalable, secure, and cost-effective cloud solutions.In addition to foundational AWS services, the course covers essential topics such as identity and access management (IAM), networking and security configurations, and monitoring and logging with CloudWatch. Students will also be introduced to Infrastructure as Code (IaC) using AWS CloudFormation and gain insights into setting up continuous integration/continuous deployment (CI/CD) pipelines with AWS tools. By the end of the course, students will have a solid understanding of AWS basics, equipping them with the knowledge and skills to effectively utilize AWS services in their DevOps practices and prepare for more advanced AWS coursework.

This course develops practical skills in securing networks, endpoints, and applications within an enterprise environment. Learners will design layered defenses, safeguard data integrity, and apply zero-trust principles to strengthen overall organizational security.

This course builds essential data analysis skills for informed business decision-making. Learners will work with Excel, SQL, and Tableau to perform statistical analysis, create financial models, and design interactive dashboards. The program emphasizes effective chart selection, Tableau aggregations and visualizations, use of marks and filters, and strong data-visualization design principles applied to real-world business problems.

This course builds core computer vision skills used in modern robotics and automation. Learners will write programs to process and analyze images, implement feature extraction techniques, and apply deep learning models for object recognition.

This course provides advanced, hands-on training in optimizing machine learning models for performance and scalability. Learners will apply techniques such as quantization, pruning, profiling, low-rank compression, and knowledge distillation to both traditional ML models and large language models (LLMs). The program also covers hardware-efficient architectures using TensorRT and ONNX, enabling students to design fast inference pipelines and deploy highly optimized models for real-world applications.

This course teaches learners to process data in real time using modern data engineering tools. Students will build practical skills with Apache Spark, Kafka, Spark Streaming, and Kafka Streaming to develop fast, scalable, and reliable real-time data pipelines.

This course teaches learners to transform marketing data into actionable insights. Students will collect, analyze, and model data, then communicate findings using Excel and Tableau to support informed business and marketing decisions.

This course teaches students to build accessible, responsive, and offline-capable mobile web applications. Learners will apply mobile-first design principles, implement accessibility standards, use asynchronous browser features and local storage for offline functionality, and optimize performance through rendering and load-time improvements.

This course provides a detailed overview of Practical Software Engineering with a focus on Low Level Design. Students learn structured approaches to analysing and modelling real-world case studies, including Designing a Pen, TicTacToe, BookMyShow, Email Campaign Management System, and Splitwise. Through systematic decomposition, object modelling, and design evaluation, learners gain hands-on experience in applying software engineering design principles to build scalable, maintainable, and correctly modelled systems.

This course introduces the fundamentals of Android app development using Kotlin. Learners will build functional, interactive mobile applications while working with custom views, Firebase, RecyclerView, notifications, the activity lifecycle, and Google Maps. Hands-on projects guided by Google engineers help students develop practical, job-ready Android skills.

This course develops the skills needed to build high-quality iOS applications from concept to launch. Learners will gain proficiency in Swift, UI design, data management, data integration, and project planning, enabling them to create polished, fully functional iOS apps.

This course builds upon the introductory JavaScript course to acquaint students of popular and modern frameworks to build the front end. We focus on three very popular frameworks/libraries in use: React.js, jQuery and AngularJS. We start with React.js, one of the most popular and advanced ones amongst the three. students learn various components and data flow to learn to architect real world front end using React.js. This would be achieved via multiple code examples and code-walkthroughs from scratch. We would also dive into React Native which is a cross platform Framework to build native mobile and smart-TV apps using JavaScript. This helps students to build applications for various platforms using only JavaScript. jQuery is one of the oldest and most widely used JavaScript libraries, which students cover in detail. Students specifically focus on how jQuery can simplify event handling, AJAX, HTML DOM tree manipulation and create CSS animations. We also provide a hands-on introduction to AngularJS to architect model-view-controller (MVC) based dynamic web pages.

This course teaches students how to analyse the ways users engage with a service. This method, called product analytics, helps businesses track and analyse user data. Students will learn more deeply what is required to move a product from idea to implementation, through to launch, and then on to iterative improvements. The course teaches how to measure progress, validate or update product hypotheses, and present product learnings. Also, students will gain experience in making informed decisions, as well as how to present findings and make an analytics-informed business case to win support for a product

This course is designed to provide students with a deep understanding of the mathematical and theoretical foundations of computation. The course covers various computational models including finite automata, Turing machines, and formal grammars, which form the basis for understanding the limits and capabilities of different computational systems. Students will explore how these models apply to real-world problems and their significance in the development of advanced AI algorithms.

Through a blend of theoretical lectures and practical exercises, students will gain proficiency in constructing and analysing computational models, understanding their applications in solving complex problems, and appreciating their relevance in AI research. By the end of the course, students will be equipped with the skills to critically evaluate different computational frameworks and apply them to innovate and enhance AI systems. This course is essential for those looking to deepen their knowledge in the field of artificial intelligence and pursue careers in research, development, and advanced technological applications.

This course provides an introduction to the design and analysis of algorithms, focusing on how algorithms are constructed, evaluated, and applied to solve real-world problems. Students learn to analyze networks, understand how individuals or components are connected, and apply algorithmic thinking to optimise processes and decision-making.

This course is aimed at providing students with a comprehensive understanding of how to design and implement systems that exhibit intelligent behaviour. This course explores a range of topics including expert systems, autonomous agents, knowledge representation, and reasoning. Students will delve into the principles of how these systems can mimic human decision-making processes, adapt to changing environments, and perform complex tasks autonomously.

The course integrates theoretical concepts with practical applications through hands-on projects and case studies, allowing students to develop and deploy intelligent systems in real-world scenarios. By working with advanced tools and techniques, students will learn to build systems that can handle uncertainty, learn from experience, and interact effectively with users and other systems. Upon completion, students will be equipped with the skills to create sophisticated AI solutions and contribute to the development of cutting-edge intelligent technologies in their professional careers.

This course equips learners with the advanced skills needed to design, build, and manage secure enterprise systems. Key topics include identity management, infrastructure protection, and incident response, enabling students to architect resilient and comprehensive security defenses.

This course equips learners with the practical skills required for a security analyst role. Students will work with intrusion detection, SIEM tools, threat modeling, and vulnerability assessment to develop strong defensive and investigative capabilities.

This is a foundational course on building server-side (or backend) applications using popular JavaScript runtime environments like Node.js. Students will learn event driven programming for building scalable backend for web applications. The module teaches various aspects of Node.js like setup, package manager, client- server programming and connecting to various databases and REST APIs. Most of these concepts would be covered in a hands-on manner with real world examples and applications built from scratch using Node.js on Linux servers. This course also provides an introduction to Linux server administration and scripting with special focus on web-development and networking. Students learn to use Linux monitoring tools (like Monit) to track the health of the servers. The module also provides an introduction to Express.js which is a popular light-weight framework for Node.js applications. Given the practical nature of this course, this would involve building actual website backends via assignments/projects for ecommerce, online learning and/or photo-sharing.

This course trains learners to integrate Privacy by Design into the software development lifecycle. Students will learn to identify privacy risks, implement privacy policies, and recommend technical solutions that strengthen data protection. The program assumes intermediate Python and SQL skills, with optional familiarity in TypeScript.

This course provides a strong mathematical and applicative introduction to Deep Learning. The course starts with the perceptron model as an over simplified approximation to a biological neuron. We motivate the need for a network of neurons and how they can be connected to form a Multi Layered Perceptron (MLPs). This is followed by a rigorous understanding of back-propagation algorithms and its limitations from the 1980s. Students study how modern deep learning took off with improved computational tools and data sets. We teach more modern activation units (like ReLU and SeLU) and how they overcome problems with the more classical Sigmoid and Tanh units. Students learn weight initialization methods, regularisation by dropouts, batch normalisation etc., to ensure that deep MLPs can be successfully trained.

The course teaches variants of Gradient Descent that have been specifically designed to work well for deep learning systems like ADAM, AdaGrad, RMSProp etc. Students also learn AutoEncoders, VAEs and Word2Vec as unsupervised, encoding deep-learning architectures. We apply all of the foundational theory learned to various real world problems using TensorFlow 2 and Keras. Students also understand how TensorFlow 2 works internally with specific focus on computational graph processing.

The ability to solve problems is a skill, and just like any other skill, the more one practices, the better one gets. So how exactly does one practice problem solving? Learning about different problem-solving strategies and when to use them will give a good start. Problem solving is a process. Most strategies provide steps that help you identify the problem and choose the best solution. Building a toolbox of problem-solving strategies will improve problem solving skills. With practice, students will be able to recognize and choose among multiple strategies to find the most appropriate one to solve complex problems. The course will focus on developing problem-solving strategies such as abstraction, modularity, recursion, iteration, bisection, and exhaustive enumeration. The course will also introduce arrays and some of their real-world applications, such as prefix sum, carry forward, subarrays, and 2-dimensional matrices. Examples will include industry-relevant problems and dive deeply into building their solutions with various approaches, recognizing each’s limitations (i.e when to use a data structure and when not to use a data structure). By the end of this course a student can come up with the best strategy which can optimize both time and space complexities by choosing the best data structure suitable for a given problem

This course builds essential skills in data analysis and business analytics. Learners will use Excel, SQL, and Power BI to collect, clean, model, and visualize data, developing the ability to generate clear insights that support effective decision-making.

This course provides comprehensive training in Google AdWords across Search, Display, Video, Mobile App, and Shopping campaigns. Learners will run real campaigns with real budgets, analyze performance data, generate reports, and optimize results to build strong, practical advertising expertise.

This course trains learners to build and deploy generative AI solutions on Azure using OpenAI models, GPT Vision, and DALL·E. Students will design RAG pipelines, engineer prompts, automate workflows, and integrate multimodal applications using Azure and OpenAI APIs.

This course provides an introduction to operating systems, covering foundational abstractions, mechanisms, and implementations. Students explore concurrent programming with threads and synchronization, inter-process communication, and key concepts in distributed operating systems. The course is structured into four sections: Introduction, Process and Thread Management, Resource Management and Communication, and Distributed Systems.

This course introduces students to the latest advancements in artificial intelligence, including deep learning, neural networks, natural language processing, computer vision, and reinforcement learning. Learners explore emerging AI applications across domains such as healthcare, finance, robotics, and autonomous systems, while critically evaluating their capabilities and limitations. Through theoretical learning and hands-on projects, students gain practical experience with modern AI tools, case studies, and research activities, preparing them to contribute to the development and implementation of emerging AI technologies.

This course teaches the full design sprint process used by leading companies to rapidly solve problems and validate ideas. Learners will understand how a design sprint works, how to participate effectively, and how to lead one. Through a simulated 4-day sprint for a fictitious company, students experience each step, explore participant roles, and apply their skills through practical, project-based work.

This course is designed to equip students with the skills to harness AI technologies for enhanced decision-making processes. This course explores the integration of AI techniques, such as predictive analytics, decision trees, reinforcement learning, and optimization algorithms, to support and improve decision-making in various contexts. Students will learn how to develop and implement AI-driven models that can analyse complex data, predict outcomes, and provide actionable insights to inform strategic decisions in business, healthcare, finance, and other sectors.

Through a blend of theoretical knowledge and practical applications, students will engage in projects and case studies that illustrate the power of AI in transforming decision-making practices. They will gain hands-on experience with tools and methodologies used to build intelligent decision support systems, ensuring they can apply these skills to real-world challenges. By the end of the course, students will be adept at creating AI solutions that enhance decision-making capabilities, positioning themselves as valuable assets in any organisation seeking to leverage AI for competitive advantage.

This course equips learners with in-demand skills in data pre-processing, visualization, and analysis using Power BI. Students gain hands-on experience with Data transformation, Power Query, DAX, and Power BI Report Design while developing strong Data visualization design and Power BI Report Customization capabilities to build effective analytical dashboards.

This course builds practical, modern C++ skills for high-performance software development. Learners will work with memory management, concurrency, and advanced programming techniques used across robotics, autonomous systems, web browsers, media platforms, servers, and video games.

This course equips learners with advanced skills in Kotlin-based Android development. Students will build feature-rich mobile apps using Firebase, testing frameworks, animations, custom views, notifications, RecyclerView patterns, and Google Maps. Guided by insights from leading Android engineers, the program emphasizes creating polished, high-performance mobile experiences aligned with modern Android standards.

This course develops the skills needed to defend modern systems against cyber threats. Learners will secure infrastructure, assess vulnerabilities, and apply industry best practices to protect organizations from digital attacks. Key topics include static application security testing, the secure software development lifecycle, logging, access management, cybersecurity business context, and hashing.

This course is aimed at providing students with an in-depth understanding of the techniques and algorithms that enable intelligent behaviour in computational systems. This course covers a wide array of topics, including neural networks, fuzzy logic, evolutionary computation, and swarm intelligence. Students will explore how these methods can be applied to solve complex problems in optimization, pattern recognition, and adaptive systems, emphasising both theoretical foundations and practical implementations.

Through a combination of lectures, hands-on projects, and case studies, students will gain experience in designing and applying computational intelligence algorithms to real-world scenarios. They will learn to develop adaptive systems that can perform tasks such as classification, prediction, and decision-making with high accuracy and efficiency. By the end of the course, students will be proficient in using computational intelligence techniques to create innovative AI solutions, positioning themselves for advanced roles in research and industry where intelligent systems are pivotal.

This course provides a comprehensive pathway to mastering AWS cloud architecture. Learners will design scalable solutions, optimize performance, manage multi-account environments, and modernize systems through hands-on, real-world projects.

This course prepares learners to manage AI products from concept to launch while building strong programming foundations. Students will learn AI integration, dataset design, Generative AI strategy, PRD creation, and product roadmapping. Alongside this, they will develop practical skills in SQL, Git, NumPy, pandas, and the Unix shell to analyze data and support AI-driven product decisions.

This course prepares future leaders to strengthen organizational resilience through effective cybersecurity strategy. Learners will assess threats, develop response plans, and align security initiatives with long-term business goals. Key topics include social engineering, cybersecurity regulations, security devices, phishing prevention, workforce training, and incident response planning.

This course provides a practical introduction to blockchain technology, covering core concepts such as networks, wallets, and consensus mechanisms. Students will develop hands-on skills in Solidity, smart contract design patterns, dApp interactions, and Hardhat-based testing. Through real-world projects—including blockchain use-case evaluation and building a collateralized loan smart contract—learners gain the technical foundation to design and deploy secure, functional blockchain solutions.

Students will learn to translate business needs into actionable project plans, apply Agile and Waterfall methodologies, and drive successful outcomes across diverse industries. Gain practical skills in requirements gathering, risk mitigation, cost–benefit analysis, and developing professional project portfolios.

This course provides a practical pathway from blockchain fundamentals to smart contract development. Learners will explore how blockchain networks, wallets, and consensus mechanisms operate, and then advance to writing and deploying Solidity-based smart contracts. Through hands-on work with design patterns, dApp interactions, Hardhat testing, and testnet deployments, students will gain the skills to build secure, functional blockchain applications—including a collateralized loan smart contract.

DevOps Tools Part 1 provides foundational skills in Git version control, CI/CD pipelines with Jenkins, configuration management using Ansible, and containerization with Docker. Learners gain hands-on experience deploying applications with Kubernetes, implementing monitoring using Prometheus, and managing logs with the ELK Stack. The course builds practical proficiency in automation, collaboration, and microservices deployment—serving as the first step toward advanced DevOps competencies.

Data is the fuel driving all major organisations. This course helps you understand how to process data at scale. From understanding the fundamentals of distributed processing to designing data warehousing and writing ETL (Extract Transform Load) pipelines to process batch and streaming data. Students will learn a comprehensive view of the complete Data Engineering lifecycle.