AI & Machine Learning at MBU with PW Skills: From Python to Generative AI

AI & Machine Learning at MBU with PW Skills is a proposed 200-hour track across four semesters, covering Python, machine learning, deep learning, GenAI, RAG, MLOps and LLMOps, alongside DSA and interview preparation.

Priyanka Agarwal13 Oct, 2026

Artificial intelligence powers tools such as voice assistants, recommendation systems and chatbots. Building these systems, however, requires more than knowing how to use AI tools. Students need programming skills, mathematical foundations, practical experience and guidance as they progress to advanced concepts.

The proposed AI & Machine Learning at MBU with PW Skills track is designed to help students develop these skills alongside their degree. It covers 200 hours of learning across four semesters, progressing from Python and machine learning fundamentals to deep learning, generative AI and AI operations. The proposed teaching model combines live online sessions by PW Skills industry experts with on-campus mentoring.

What Is the AI & Machine Learning Track at MBU with PW Skills?

The PW Skills AI ML course at MBU is planned as a 200-hour track spread across four semesters, with 50 hours of learning each semester. It covers Python, machine learning, deep learning and generative AI, alongside DSA and interview preparation. The aim is to help students build technical knowledge progressively while pursuing their degree.

Feature	Details
Total duration	200 hours
Learning period	Four semesters
Hours per semester	50 hours
Core focus	Python, machine learning, deep learning and generative AI
Additional learning	Data Structures and Algorithms (DSA) and Interview Preparation
Teaching model	Live online expert sessions with an on-campus mentor

The proposed track is part of the wider skill development programme at MBU, with the aim of integrating technical learning into students’ academic journey.

What Will Students Learn Across Four Semesters?

The curriculum moves from foundational programming and mathematics to advanced AI systems, giving students a base before they progress to more complex applications.

Semester	Curriculum	Learning focus
Semester 1	Core Python for AI Engineers	Learn Python programming fundamentals for AI development.
Semester 2	Mathematics, Statistics and Machine Learning Foundations	Understand the mathematical concepts and methods behind machine learning models.
Semester 3	Deep Learning and MLOps	Study neural networks and learn the fundamentals of deploying and managing machine learning models.
Semester 4	Generative AI, Agentic AI, RAG and LLMOps	Explore generative models, AI agents, document-based retrieval and the management of large language model applications.

Students interested in the broader data science field can also explore the Data Science with Generative AI course.

How Do Python and Machine Learning Build the Foundation?

The first two semesters focus on programming, mathematics, statistics and machine learning basics. These concepts help students understand how data is processed and how models identify patterns to make predictions.

For example, a machine learning model may use past sales data to estimate future demand. Students first need to understand the data, work with Python and learn the principles behind the model before moving to more advanced applications.

For additional programming practice, students can explore Decode Programming Powerhouse: C, Java, Python and DSA.

How Do Deep Learning and Generative AI Fit In?

Semester 3 and Semester 4 cover advanced AI concepts, helping students progress from deep learning to generative AI and its practical applications.

Semester	Topics	What Students Learn
Semester 3	Deep Learning and MLOps	A deep learning course for students introduces neural networks and how they identify patterns in data for applications such as image recognition and language processing. MLOps covers the deployment and management of machine learning models.
Semester 4	Generative AI, Agentic AI, RAG and LLMOps	Students explore generative AI, AI agents and retrieval-augmented generation (RAG), which retrieves relevant information from documents before generating an answer. These topics support a generative AI course for engineering students focused on understanding and applying modern AI systems.

Students who want to study AI application development in greater depth can explore the Gen AI Engineering course.

How Do DSA and Interview Preparation Fit Into the Track?

Alongside AI-ML subjects, the proposed learning plan includes DSA and Interview Preparation to support problem-solving and career readiness.

Data Structures and Algorithms (DSA): A shared 180-hour component planned across three semesters for the AI-ML, Data Analytics & Data Science, and Full Stack Development tracks.
Interview Preparation: A shared 32-hour component planned across four semesters for all four skill tracks.

DSA covers problem-solving concepts and algorithms that are useful in software development and technical interviews. Interview Preparation is designed to help students communicate their skills and prepare for recruitment processes.

How Will Classes and Mentoring Work?

The proposed teaching model combines online instruction with on-campus guidance, giving students access to an industry expert during live sessions and a mentor for classroom support.

Each class is planned around two teaching roles:

PW Skills industry expert: Delivers the live online session. Recordings are intended to be available through the learning management system (LMS).
On-campus mentor: Supports students with doubts, reinforces concepts and helps maintain classroom engagement.

The plan also includes assessments and programme reporting through the PW Skills LMS and LineupX.

What Practical and Career Support is Included?

The proposed programme combines technical learning with activities intended to help students apply their knowledge and prepare for recruitment.

The planned support includes:

Practical learning: Bootcamps and applied activities designed to introduce students to relevant technical concepts.
Resume and portfolio development: Guidance on presenting projects, skills and experience.
Mock interviews: Practice to help students prepare for technical and general interview questions.
LinkedIn optimisation: Guidance on presenting their professional profiles.
Soft skills sessions: Support with communication and workplace readiness.
AI Interview Coach and career portal: Additional tools planned to support interview practice and career preparation.

Students interested in related infrastructure and deployment concepts can also explore the DevOps and Cloud Computing course.

These activities are intended to support students’ preparation for opportunities in AI and software development. They should not be taken as a guarantee of placement or employment.

Who Is the AI & Machine Learning Track Designed For?

The track is intended for students who want to build technical skills in artificial intelligence alongside their degree. Its progression from Python to advanced AI concepts is designed to provide a learning path rather than requiring students to begin with complex topics.

The wider MBU skill development proposal lists programmes such as B.Tech, BCA, MCA, SOC, B.Com and MBA for consideration.

Students exploring related options can also review the Data Analytics With AI course to understand another pathway focused on working with data and AI tools.

Turning AI Knowledge into Practical Skills

Learning AI involves more than understanding algorithms or using AI tools. It requires programming, problem-solving and the ability to apply concepts to real-world challenges. Students also need opportunities to understand how AI models are developed, deployed and used in practical applications.

Through the proposed AI & Machine Learning track at MBU with PW Skills, PW Nexus aims to help students progress from Python and machine learning fundamentals to deep learning, generative AI and modern AI applications. By combining technical learning with expert-led sessions, on-campus mentoring and career preparation, the track seeks to help students connect academic knowledge with practical skills relevant to the evolving technology industry.