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Home AI and Machine Learning Machine Learning Syllabus: Course-wise Subjects [2024]

Machine Learning Syllabus: Course-wise Subjects [2024]

Mar 6, 2024 1.2K Reads

With the cutting edge technology of machine learning transforming the purview of digital and technological operations, a larger number of students are turning to formal education in this domain. Not only are they looking to learn the fundamentals of ML in courses like degrees, diplomas and certificates, they are also actively exploring the various subdomains of ML to assess their fit in each domain.

An exploration of the various subjects and curricular structure of ML courses reveals the various pertinent areas of the field one can look to upskill in. 

If you are also a student or enthusiast interested in knowing more about machine learning courses with respect to their curriculum and subjects, then this blog is just the spot for you, as we have detailed the various courses in machine learning, their curriculum and important subject areas that one can look to upskill in. 

What are the Major Courses in Machine Learning? 

There are a vast number of courses including those at the undergraduate, postgraduate and executive levels available for students and working professionals to pursue in machine learning. Most of the courses in this field are offered in dual specialisations, commonly with interrelated domains of ML like artificial intelligence, deep learning, data science, NLP etc.

Similarly, there are a number of degree programs, diplomas and certification courses on a short-term basis available to be pursued in machine learning. 

The most commonly pursued courses in machine learning in India available to be pursued as offline and online learning programs have been listed herein. 

Undergraduate Courses in Machine Learning

Postgraduate Courses in Machine Learning 

BCA In Artificial Intelligence & Machine Learning Online 

MBA in Artificial Intelligence & Machine Learning Online

BCA Data Analytics Online

MCA in Machine Learning Online 

B.Tech in CSE (AI & ML)

MCA in Artificial Intelligence & Machine Learning Online

Diploma in Artificial Intelligence & Machine Learning Online

Executive Postgraduate Program In AI & ML Online


PG Diploma In Data Science Online


PG Certification Course in AI & ML Online


Job-guaranteed Certification Course in Machine Learning Online


Executive PG Certificate in Machine Learning & Deep Learning Online


Executive PG Certificate in Deep Learning & NLP Online


PG Diploma in Machine Learning Online

The syllabus of a machine learning course is tailored to meet the dual needs of learning–having strong foundational bases and developing hands-on skills to run ML technologies and develop models. Accordingly, the curricular structure, course pedagogy, syllabus contents etc. are designed to ensure these learning outcomes in students enrolling for such courses. 

Machine learning courses are now available to be pursued in both the on-campus regular mode and full-time online learning (OL) mode. The syllabus and course curriculum of these online ML programs are similar and equivalent to regular courses, and include simulation and lab facilities for practical learning and skill-building as well. 

As the UGC-DEB has granted equivalent status to online programs as regular degrees (provided they have been pursued from duly accredited institutes/platforms) the course curriculum of online ML programs are also tailored to ensure equivalent rigour and depth of learning as the regular courses. 

Syllabus in Machine Learning & Artificial IntelligenceDegree Courses 

There are a number of highly popular degrees in the machine learning specialisation including those like an MCA, an MBA, a BCA, a B.Tech, an M.Tech, a B.Sc and so on. 

Herein, we have outlined the course curriculum and subjects commonly covered in the major undergraduate and postgraduate degree courses in ML. Undergraduate Degree Syllabus & Subjects in Machine Learning and Artificial Intelligence .

 Find more details about the course structure, the syllabus and important subjects in undergraduate degree courses like BCA, B.Sc, B.Tech in the specialisation of machine learning etc. 

1) BCA in Artificial Intelligence & Machine Learning (AI & ML) 

A BCA is a technological course in computer applications having a duration of 3 years. The course structure comprises 6 semesters (each approximately lasting 6 months) wherein the fundamental concepts of AI and ML are covered in the first two semesters.

Through semesters 3-6, the students are taught about the nuances of artificial intelligence and machine learning through lectures and relevant practicum sessions and seminars. Hands-on learning is facilitated through regular projects and assignments. 

The commonly covered ML subjects in this course include data structures and algorithms, programming in C, applied mathematics for machine learning, statistics for ML and so on. Click Here | BCA Online Program!

Further details about the course subjects of machine learning in a BCA degree are provided below :

Introduction to Programming and C

Database Management System

Foundations of Applied Mathematics 

Data Structures and Algorithms 

C# and .NET Framework

Object Oriented Programming using Java

Designing human computer interfaces

Numerical Analyses and Statistical Techniques 

Internet Technologies 

Fundamentals of AI 

Web Applications

Relational Database Management System

Web Applications

Machine Learning Tools

AI Using Azure 

Elements of Mathematics 

2) BCA in Machine Learning (ML) & Data Science 

Another popular ML specialisation available in BCA is the dual specialisation in machine learning and data science. This course brings a course of subjects including a blend of fundamentals of both ML and data science principles, such as mathematics and statistics, big data analytics, systems analysis, programming foundations for both the domains, data structures and algorithms, fundamentals of AI, data ethics and privacy and so on. This course is also divided into 6 semesters across 3 years, during which a number of theoretical foundations as well as hands-on skill building are built in students.

Further details about the course subjects and syllabus are provided below :

Programming Fundamentals 

Machine Learning 

Deep Learning 

Mathematics for Data Science 

Statistical Foundations for Data Science

Object Oriented Programming Using C++

Programming Foundation

Data Structures & Algorithms 

System Analysis & Design

Operating System & Shell Programming

AI for Business 

Fundamentals of AI 

AI for Robotics 

Reinforcement Learning 

Data Handling and Visualization

Machine Learning

Big Data Technologies

Data Ethics & Privacy 

Computer Vision

Natural Language Processing 

Deep Generative Models

AI for Industry Applications 

Time Series Analysis 

AI in Research & Development 

3) B.Tech in CSE (AI & ML)

A B.Tech in Computer Science Engineering (CSE) is one of the most popular specialisation domains of the engineering course in India. While most institutes provide an overview of courses in computer science engineering in the CSE specialisation, some institutes provide the course with a specific focus on artificial intelligence and machine learning.

A B.Tech in CSE (AI & ML) is a 4-year course divided into 8 semesters, with the first two semesters focusing on the foundations of engineering, followed by a detailed coverage of topics and subjects related to artificial intelligence and machine learning.

A characteristic feature of a B.Tech degree is that it lays a strong emphasis on practical skill-building and working knowledge of ML needed to sustain in the IT industry. The commonly included subjects related to AI and ML in a B.Tech course include Python programming, algebra, applications and use cases of machine learning, data structures and algorithms, probability and random processes, reinforcement learning etc. Click Here | BTech After Diploma!

The details of the course syllabus are provided below for reference :

Linear Algebra

Introduction to Python Programming

Statistics for Data Science

Software Tools for AI & ML

Application and Use Cases of Machine Learning

Probability and Random Processes

Communication Skills 

Data Structures and Algorithms

Supervised Machine Learning

Reinforcement Learning

Optimization Techniques for Machine Learning

Data Visualization 

AI Ethics

Database Concepts for Data Science

Deep Learning

Natural Language Processing

Generative Adversarial Networks

Data Reprocessing

AI Ethics

Programming in C

4) BCA in Data Science & Big Data Analytics  

A BCA in data science and big data analytics, while not directly pertaining to machine learning, is a field closely related to machine learning, as the underlying principles of machine learning and data science both rely on mathematical and statistical principles and foundations for systematic data analysis and insight development.

The course covers a range of subjects including database management, data structures, Java, Python programming, foundations of artificial intelligence, information system management and their security, system programming and so on through the 6 semesters of the degree. The focus on hands-on skill building is also quite high in this course, considering it is a technical course.

More relevant details about the subjects and courses in a BCA program in data science and big data analytics are provided below :

Introduction to IT & Programming in C

Database Management System

Data Structures 

C Programming 

Internet Technologies 

Software Engineering 

Object-Oriented Programming in C

Basics of Cloud Using Azure 

Introduction to Data 


Fundamentals of AI 

Computer System Architecture 

Programming in .NET Using C++

Python Programming 

Machine Learning 

Data Visualisation in Business Intelligence 

Security of Information System

Introduction to Management Information System (MIS)

Relational Database Management System (RDBMS)

System Programming 

5) B.Sc in Data Science & AI 

A Bachelor of Science in Artificial Intelligence and Data Science is another undergraduate course relevant to machine learning that includes courses related to machine learning, principles of AI, ML and data science, deep learning, advanced data analytics, NLP, applications of AI, time series analysis and so on.

The course structure comprises 6 semesters spanned across 3 years of the course, integrating a number of pedagogies to promote skill-based and industry-relevant learning for professional competency. This course curriculum is designed to hone the skills of students in not only data science and artificial intelligence but also machine learning, deep learning and applications of AI in various industries, thus promising a holistic perspective development in the field. Click Here | B.Sc Online Course!

Find more details about the course subjects for B.Sc in data science and AI provided below :

Machine Learning 

Reinforcement Learning 

Cloud Computing 

Natural Language Processing 

Deep Learning 

Data Ethics & Privacy 

Advanced Data Analytics 

AI in Business 

IoT and Data Science 

Deep Generative Models 

Time Series Analysis 

AI & Ethics 

AI in Research & Development 

AI for Robotics 

Big Data Technologies

Computer Vision 

Postgraduate Degree Syllabus & Subjects in Machine Learning and Artificial Intelligence

Course details including course structure, syllabus and important conceptual areas for various postgraduate degree courses in machine learning have been elaborated upon below. While the major subjects and conceptual arenas of the specialisations of these courses are often similar to those in undergraduate courses, the major difference lies in the degree of depth and advanced level in which they are explored at the PG level through both theoretical concept-building and practical skill-building. 

1) MBA in Artificial Intelligence & Machine Learning (AI & ML) 

A Masters in Business Administration (MBA) in artificial intelligence and machine learning is a two-year degree course consisting of 4 semesters that guide the student through the foundations of management as well as management in the specific context of AI and ML technologies. Thus, this course lays emphasis on the nuanced aspects of using AI and ML in managerial operations and functions.

The prominent subjects in this course include technology transformation in business, business analytics, NLP, deep learning, applications of AI in business, data visualisation, machine learning and neural networks, reinforcement learning and so on.

The common learning provisions to facilitate hands-on learning in an MBA (AI & ML) include seminars and industry-interactions, hands-on skill building activities, case studies, practicum papers and so on. Generally, the course curriculum is designed to provide a foundational overview of management in the first two semesters and cover the specialisation courses of AI and ML in the third and fourth semesters. Click Here | Online MBA Program!

Find further details about the MBA in AI and ML given here :

Business Communications

Organisational Behaviour

Accounting for Managers

Technology Transformation for Business

Business Analytics 

Managerial Economics-Micro and Macro

Natural Language Processing

Deep Learning

Introduction to Machine Learning

Applications of AI in Business 

Advanced Machine Learning

Artificial Intelligence and its Applications

Data Visualization

Machine Learning 

Performing Analytics 

R-Programming for Data Analytics & Data Visualisation

Neural Networks & Machine Learning Algorithms 

Foundations of Computer Systems

2) MCA in Machine Learning  (ML)

An MCA in machine learning is a 2-year PG degree that is available to be pursued in the offline regular mode as well as in the fully online learning mode in India. It is a course designed to equip students with advanced skills of computer applications and IT, with a specialised focus on machine learning technologies (as covered in the third and fourth semesters).

This course covers in-depth the major functional areas of machine learning including those like deep learning, neural networks, natural language processing, time series analysis, reinforcement learning, data structures and algorithms, data visualisation, programming in languages like Python, C++ and so on. Click Here | Online MCA Program!

Find details about the major subjects covered in an MCA in machine learning provided below :

Introduction to Machine Learning 

Reinforcement Learning 

Deep Learning 

Neural Networks 

Time Series Analysis

Natural Language Processing 

Data Structures & Algorithms 

Applied Mathematics for ML 

Applications of Machine Learning 

Unsupervised and Supervised Machine Learning 

Complex Network Analysis 

Data Visualization

Programming in Python 

Programming Using C++

3) MCA in Artificial Intelligence & Machine Learning (AI & ML)

An MCA in AI and ML is a dual specialisation course that covers a number of fundamental areas of these two closely related fields of computer science. The course is structured into 4 semesters across 2 years covering domains ranging from foundations of computer applications like computer systems, business analytics and visualisation, software development, shell programming etc.

to the specialisation courses of AI and ML including data structures and algorithms, data visualisation, reinforcement learning, AI for robotics, applications of ML, NLP, complex network analysis, predictive analytics etc.

More details about the syllabus and subjects in this course are provided below :

Foundations of Computer Systems

Data Structures and Algorithms

Deep Learning for AI

Data Visualization

Data Engineering for AI

Business Analytics and Visualization

Foundations of Machine Learning

Mathematical Foundations for Computer Applications

Artificial Intelligence for Robotics

Reinforcement Learning

Cloud Computing

Applications of Machine Learning

Complex Network Analysis

Natural Language Processing

Representation Learning

Applied Predictive Analytics

Advanced Software Engineering Principles

Network Security 

4) M.Tech in Artificial Intelligence & Machine Learning (AI & ML)

An M.Tech in AI and ML is a 2-year postgraduate degree in engineering that is centred on the coverage of advanced concepts of artificial intelligence and machine learning through the 4 semesters into which the course is divided. This course is often divided into core subject papers and elective papers, the latter of which include a host of specialisation-relevant courses from which a student can select courses they wish to pursue.

Commonly offered subjects in an M.Tech program in AI & ML include deep neural networks, mathematical foundations of machine learning, deep reinforcement learning, advanced deep learning, NLP and its applications, ML operations (MLOps), data mining and data management, graph neural networks and so on. Click Here | M.Tech for working Professionals!

Find further details provided below :

Mathematical Foundations for Machine Learning

Deep Neural Networks

Machine Learning

Deep Reinforcement Learning

Advanced Deep learning

Introduction to Statistical Methods

ML System Optimization

Graph Neural Networks

Natural Language Processing (NLP)

Fair, Accountable, Transparent Machine Learning

NLP Applications

Information Retrieval


Computer Vision

Advanced Data Mining AI and ML techniques for Cyber Security

Data Management for Machine Learning

Probabilistic Graphical Models

Video Analytics

Syllabus in Machine Learning & Artificial Intelligence: Diploma Courses 

In addition to degree courses in machine learning, there are also a number of diploma programs offered in the subject area of machine learning to meet the learning needs of students who want to pursue a relatively shorter course equipping them with the central skill areas and concepts of ML.

Diploma courses, whether at the UG, PG or executive levels, are designed to meet this academic need, as they cover important domains of ML including those like probability and statistics for data science and ML, ML algorithms, NLP, decision modelling, data visualisation, data analytics etc. within an approximate period of 10 to 18 months. Click Here | Online Diploma Programs!

Find a few of the major subjects and papers covered in diploma programs of machine learning listed here : 

Probability & Statistics for Data Science

Data Visualisation 

Digital & Social Media Analytics

Decision Modelling 

Methods & Algorithms in Machine Learning

AI & Decision Sciences

Economics for Analytics

Programming in Python 

Behaviour Science & Analytics 

Natural Language Processing 

Syllabus in Machine Learning & Artificial Intelligence: Certificate Courses 

In addition to degrees and diplomas, certifications in machine learning are a beneficial educational option to take up when you want to upskill and increase proficiency in a specific subdomain of artificial intelligence and machine learning. Depending upon the duration of the certificate course, the course program might be covered in one or two semesters.

The commonly covered subjects in such certificate programs include types of machine learning, applications of AI and ML, data mining, big data analytics, deep learning, NLP, model deployment in machine learning, computer vision, regression, clustering for machine learning, programming and development using R, C++, Python and so on.

Relevant subjects and courses of machine learning certificate programs have been enlisted here :

AI & ML: An Imperative for Digital Business

Introduction to Deep Learning Algorithms

Unsupervised Machine Learning 

Foundations of Reinforcement Learning 

Practising Machine Learning 

Applications of AI & ML

Introduction to Artificial Intelligence 

Data Mining & Big Data Analytics 

Natural Language Processing 

Feature Engineering & Selection 

Model Deployment & Selection 

Computer Vision

Understanding Supervised Learning 

Machine Learning Applications

Data Mining: Decision Trees & Random Forest 

Linear & Logistic Regression

Clustering Techniques of Data Mining 

Data Visualisation 

Data Manipulation Using R

Data Analytics Foundation 

Recommended Books & Course Materials in AI & ML 

Provided below are a few of the important books for reference and study materials for machine learning and related domains like artificial intelligence, deep learning, programming and development etc. 




Python Machine Learning 

S. Raschka

Packt Publishing Ltd. 

Artificial Intelligence: A Modern Approach 

S. Russell, P. Norvig 

Berkeley Edu.

Deep Learning 

I. Goodfellow, Y. Bengio, A. Courville 

MIT Press

Fundamentals of Data Structures in C

E. Horowitz, S. Sahni, S. A. Freed

Universities Press

Machine Learning Yearning


Andrew NG © 

Artificial Intelligence and Machine Learning

V. Chandra S. S., A. Hareendran S. 

PHI Learning

Advanced Programming in the UNIX Environment

W.R. Stevens

Pearson Education

Core Python Programming

Wesley J. Chun

Pearson Education

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As can be seen from the blog, the course syllabi of various degree, diploma and certificate programs in machine learning cover a range of foundational subjects of the discipline and place an emphasis on the development of practical and hands-on skill development. Both online courses and on-campus programs help students develop a strong skill set necessary to thrive in the ever-evolving ITES industry through the curriculum. Such upskilling courses can set the stepping stone in enhancing one's career in machine learning.

FAQs (Frequently Asked Questions)

Some of the most important subjects for machine learning courses include mathematics and statistics for data analytics and model development, Python programming, deep learning, natural language processing etc. 


The syllabus of a machine learning course varies depending on whether it is a degree, diploma or certificate course. Moreover, whether the course is a beginners course, an undergraduate program or a postgraduate program also affects the papers taught in the course and their level of difficulty. Find more details about the syllabus of various ML courses provided in the blog above. 


Machine learning courses include subjects and papers that require a working knowledge of mathematics, statistics, applied mathematics as well as programming and coding skills (in languages such as Python, C++, Java etc.). The syllabus of a machine learning course may be difficult to grasp for someone not having a strong foundation in these domains. However, with working knowledge of these fields, one may find machine learning courses quite manageable. 


Since machine learning is a field within computer science, it requires interested learners to have foundational knowledge of mathematics at least till the higher secondary (i.e. grade 11 and 12) level. Many institutions also mandate the candidates enrolling in an ML course to have studied mathematics as a subject at least till the Higher Secondary level. So, you either need to have studied mathematics till class 12 or pursue a bridging course in mathematics for machine learning to pursue ML programs. 


Yes, most of the degree courses, diplomas and certificate courses in machine learning provide sufficient practical exposure to students to develop industry-relevant skills and competencies in them. This is done through regular practicum classes, seminars and industry interactions, workshops, ML projects and assignments. This is true for both offline and online machine learning course syllabi. 


Knowledge of certain key domains of computer science including principles of mathematics and statistics needed for CS operations, software development, programming and coding etc. is needed to pursue machine learning courses.

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