MSBTE K-Scheme Syllabus for AI And Machine Learning Diploma
MSBTE AI And Machine Learning First Semester Syllabus (AN1K)
Download the AI And Machine Learning First Semester Syllabus PDF using the link below.
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Searching for the MSBTE AI and Machine Learning First Semester Syllabus PDF as per the K Scheme? You’re in the right place! This page offers the official and updated AN1K syllabus PDF for the First Semester of Artificial Intelligence and Machine Learning diploma course under the MSBTE (Maharashtra State Board of Technical Education).
The MSBTE AN1K syllabus PDF includes foundational subjects like Basic Mathematics, Applied Science, Communication Skills, Engineering Graphics, and Fundamentals of ICT. It also features practicals and workshops that set the stage for advanced AI and ML studies in future semesters. The syllabus outlines subject codes, weekly hours, course outcomes, and internal/external evaluation criteria.
This syllabus is carefully designed for AI and Machine Learning (AN) diploma students, aligned with the latest MSBTE K Scheme, and provides a solid foundation in essential engineering and technical concepts.
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 AN1K syllabus
✅ Official MSBTE First Semester syllabus under K Scheme
✅ Includes theory subjects, practicals, and basic engineering components
✅ Ideal for first-year AI and Machine Learning diploma students
Click the link below to download the MSBTE AI and Machine Learning First Semester syllabus PDF (AN1K) and begin your academic journey with the most up-to-date curriculum.
The first semester of the MSBTE AI and Machine Learning (AIML) diploma marks the beginning of a highly future-oriented academic journey. Under the K-Scheme curriculum, the AN1K semester introduces essential foundational subjects that prepare students for advanced AI, data science, machine learning, neural networks, big data, and deep learning modules taught in later semesters. The goal of this semester is to build strong analytical, mathematical, technical, and communication skills, ensuring that students are fully prepared for the fast-expanding world of artificial intelligence. Understanding the complete AN1K syllabus is extremely important because it helps students establish the right learning approach from day one.
The AN1K syllabus includes core first-semester subjects such as Basic Mathematics, Applied Science, Engineering Graphics, Fundamentals of ICT, Communication Skills, and Workshop Practice. Even though these are common foundational subjects, they play an essential role in preparing AIML students for algorithmic thinking, data handling, logical reasoning, and computational analysis. Mathematics strengthens the student’s ability to solve equations, understand functions, analyze patterns, and process numerical data—skills that are critical for machine learning and AI algorithms. Applied Science helps students understand physical principles and scientific reasoning, which support computational models and simulation concepts seen in later semesters.
Fundamentals of ICT is particularly important for AIML students because it introduces them to essential computer concepts, operating systems, productivity tools, digital communication, and basic data handling—all of which are fundamental to AI development. Engineering Graphics enhances a student's ability to visualize data, interpret diagrams, and improve spatial reasoning. These skills become useful when dealing with neural network diagrams, flowcharts, architecture models, and algorithm mapping in higher semesters.
Communication Skills is another crucial subject in the first semester as it trains students to communicate effectively—whether drafting reports, presenting technical work, or collaborating with teams in real-world AI and IT environments. AI professionals must often explain complex ideas in simple terms, and this subject lays the foundation for that ability. Workshop Practice offers hands-on mechanical and practical exposure, teaching students the importance of precision, safety, tools, and basic fabrication—skills that indirectly support hardware integration, robotics, and embedded AI applications.
The AN1K K-Scheme syllabus also provides complete details about course outcomes, weekly hours, credit structure, unit-wise content, assignments, practical components, and the internal & external assessment scheme. Understanding these details helps students plan their semester efficiently, focus on high-weightage topics, and prepare strategically for MSBTE examinations. Since the AIML diploma progresses rapidly toward advanced technologies in the third and fourth semesters, having a strong foundational start is essential.
At msbtesolutions.com, students can download the official MSBTE AI and Machine Learning First Semester (AN1K) Syllabus PDF under the K-Scheme. The PDF includes complete unit-wise content, course outcomes, practical requirements, and exam patterns in a clean, easy-to-read format ideal for students, teachers, and institutions.
If you are beginning your journey in Artificial Intelligence and Machine Learning under MSBTE, reviewing the AN1K syllabus will help you understand what to expect in the first semester, build the right academic foundation, and prepare for future subjects like Python Programming, Data Analytics, Machine Learning Algorithms, Neural Networks, and AI Tools. Download the MSBTE AN1K First Semester Syllabus PDF today and start your AIML diploma journey with clarity and confidence.
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