MSBTE K-Scheme Syllabus for AI And Machine Learning Diploma
MSBTE AI And Machine Learning Third Semester Syllabus (AN3K)
Download the AI And Machine Learning Third Semester Syllabus PDF using the link below.
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Looking for the MSBTE AI and Machine Learning Third Semester Syllabus PDF under the K Scheme? You’ve come to the right place! This page offers the official and most updated AN3K syllabus PDF for the Third Semester of the Artificial Intelligence and Machine Learning diploma course by MSBTE (Maharashtra State Board of Technical Education).
The MSBTE AN3K syllabus PDF includes key subjects such as Data Structures, Python Programming, Discrete Mathematics, Computer Networks, and AI Tools & Platforms. It also incorporates essential practicals, skill-based learning, and mini-project work to build a strong foundation in AI and ML technologies. The syllabus features subject codes, weekly lecture/practical hours, course outcomes, and evaluation schemes—ideal for effective academic and technical preparation.
Designed as per the latest MSBTE K Scheme, this syllabus is perfect for AI and Machine Learning (AN) diploma students to transition from foundational to core technical concepts in artificial intelligence and programming.
In addition to core subjects, students enrolled in the MSBTE K-Scheme Computer Engineering Diploma will have the option to choose electives based on their interests and career goals. The availability of elective subjects may vary across different colleges and institutes.
✅ Free PDF download of AN3K syllabus
✅ Official MSBTE Third Semester syllabus under K Scheme
✅ Includes data structures, Python, AI tools, and project-based learning
✅ Designed for third-semester AI and Machine Learning diploma students
Click the link below to download the MSBTE AI and Machine Learning Third Semester syllabus PDF (AN3K) and boost your academic journey with the right study material.
The third semester of the MSBTE AI and Machine Learning (AIML) diploma is where students begin their real journey into the world of artificial intelligence, data science, and computational thinking. Under the K-Scheme curriculum, the AN3K semester introduces core subjects such as Python Programming, Data Structures, Database Management Systems (DBMS), Digital Techniques, Statistics for AIML, and Computer Networks. These subjects build the technical foundation needed for machine learning algorithms, neural networks, data analytics, deep learning, and AI-based application development in higher semesters. Understanding the complete AN3K syllabus is essential for students because it marks the transition from basic engineering principles to dedicated AIML concepts.
Python Programming is one of the most important components of the AN3K semester. Python is the backbone of modern AI development, widely used for machine learning, data preprocessing, visualization, automation, and neural networks. In this subject, students learn Python syntax, control structures, functions, lists, tuples, dictionaries, file handling, modules, and error handling. These concepts prepare students for libraries like NumPy, Pandas, Matplotlib, TensorFlow, Scikit-Learn, and Keras in later semesters.
Data Structures helps students develop logical thinking and computational efficiency. Topics such as arrays, stacks, queues, linked lists, trees, and searching & sorting algorithms lay the groundwork for understanding how machine learning models handle data internally. AIML students especially benefit from this subject because efficient data handling is crucial when working with large datasets and training models.
Database Management Systems (DBMS) teaches fundamental concepts of data storage, relational models, SQL queries, schema design, normalization, and transactions. Data is the heart of AI and machine learning, and understanding how data is stored, retrieved, and managed is essential for building intelligent applications. These DBMS skills will support future subjects like Data Analytics and Big Data tools.
Digital Techniques introduces students to logic gates, Boolean algebra, combinational circuits, sequential circuits, flip-flops, counters, and registers. This subject builds the basic understanding of how computers process information—a concept that becomes important when AIML students deal with hardware accelerators like GPUs and TPUs or embedded AI systems.
Statistics for AIML is another crucial subject in this semester. Statistics forms the mathematical backbone of machine learning by helping students understand mean, median, variance, correlation, probability distributions, hypothesis testing, and sampling methods. These concepts are directly applied in model evaluation, data preprocessing, probability-based algorithms, and predictive analytics.
Computer Networks introduces the basics of data communication, network layers, IP addressing, routing, switching, and network security fundamentals. With AI integrated into cloud systems, IoT devices, and distributed computing platforms, understanding networking is essential for modern AIML professionals.
The AN3K syllabus also includes practical sessions, assignments, lab work, and term-work activities that give students hands-on experience with Python coding, SQL queries, data structure implementation, circuit diagrams, and statistical computations. These practical exercises help students apply theoretical concepts to real AI and computer science problems, making learning more interactive and industry-relevant.
The K-Scheme syllabus clearly outlines unit-wise content, weekly hours, credit distribution, learning outcomes, and internal/external assessment patterns. Understanding these details helps students prepare strategically for MSBTE exams, revise important units effectively, and score higher. Since AIML subjects become more advanced in upcoming semesters, a strong grip on the AN3K content is essential for long-term success.
At msbtesolutions.com, students can download the official MSBTE AI and Machine Learning Third Semester (AN3K) Syllabus PDF under the K-Scheme. The PDF includes full unit-wise details, practical components, exam schemes, course outcomes, and term-work structure in a clean and easy-to-read format.
If you are entering the third semester of the MSBTE AIML diploma, reviewing the AN3K syllabus will help you strengthen your programming skills, build strong data handling capabilities, and prepare confidently for advanced subjects like Machine Learning Fundamentals, Data Analytics, Neural Networks, and AI Tools. Download the MSBTE AN3K Third Semester Syllabus PDF today and continue your AI journey with clarity, confidence, and strong technical foundations.
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