How to understand Machine Learning for GATE DA 2026?

GATE DA 2026 Syllabus, Tips & Study Guide

Data science and artificial intelligence are some of the new hot topics in the technical domain. Often used together, data science (DS) and artificial intelligence (AI) are two different domains. Data science is an interdisciplinary field that uses a combination of various subjects like statistics, mathematics, programming, etc., to extract meaningful insights from unstructured data, whereas AI is the technology that enables computers to perform different advanced tasks, such as the ability to understand human languages, translate spoken and written languages, make recommendations, etc.

Many students generally go for certificate courses for entry-level jobs in the field of DS and AI. However, instead of going for certificate courses, engineering students/graduates should go for postgraduate degrees in these domains. Engineering students/graduates should appear in the GATE exam from the DA stream to get admission to postgraduate courses (M.Tech and M.E.) in AI and DS courses. Machine learning is an important subject for the GATE DA exam. In this blog, we would see how students can prepare for machine learning for GATE DA. This blog would be useful for the students who are preparing for the GATE DA 2026 exam.

What is Machine Learning?

Machine learning (ML) is a branch of artificial intelligence (AI) that allows algorithms to unveil hidden patterns in a data set. Machine learning discovers previously unknown relationships in data by searching large data to discover patterns and trends that go beyond simple statistical analysis.

Supervised, unsupervised, semi-supervised, and reinforced are some of the most common types of machine learning.

Machine learning has applications in many fields, like speech recognition, image recognition, natural language processing, fraud detection, portfolio optimization, etc.

For engineering students/graduates, a higher degree like an M.Tech or M.E. in Data Science, Artificial Intelligence, or related fields from top colleges of India enhances the weightage of their CV. Students can get placement in different national or international companies with good packages after completing their M.Tech or M.E. in Data Science, Artificial Intelligence, or a related field. To pursue an M.Tech or M.E. from top colleges of India, students need to appear for GATE. After clearing GATE with a good rank, candidates can join their desired colleges and pursue their desired course.

GATE Data Science and Artificial Intelligence 2026 syllabus

Before moving to the preparation strategy let us have a look at the syllabus of GATE 2026 Data Science and Artificial Intelligence.

Probability and Statistics: Counting (permutation and combinations), probability axioms, Sample space, events, independent events, mutually exclusive events, marginal, conditional and joint probability, Bayes Theorem, conditional expectation and variance, mean, median, mode and standard deviation, correlation, and covariance, random variables, discrete random variables and probability mass functions, uniform, Bernoulli, binomial distribution, Continuous random variables and probability distribution function, uniform, exponential, Poisson, normal, standard normal, t-distribution, chi-squared distributions, cumulative distribution function, Conditional PDF, Central limit theorem, confidence interval, z-test, t-test, chi-squared test.

Linear Algebra: Vector space, subspaces, linear dependence and independence of vectors, matrices, projection matrix, orthogonal matrix, idempotent matrix, partition matrix and their properties, quadratic forms, systems of linear equations and solutions; Gaussian elimination, eigenvalues and eigenvectors, determinant, rank, nullity, projections, LU decomposition, singular value decomposition.

Calculus and Optimization: Functions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima, optimization involving a single variable.

Programming, Data Structures and Algorithms: Programming in Python, basic data structures: stacks, queues, linked lists, trees, hash tables; Search algorithms: linear search and binary search, basic sorting algorithms: selection sort, bubble sort and insertion sort; divide and conquer: merge sort, quicksort; introduction to graph theory; basic graph algorithms: traversals and shortest path.

Database Management and Warehousing: ER-model, relational model: relational algebra, tuple calculus, SQL, integrity constraints, normal form, file organization, indexing, data types, data transformation such as normalization, discretization, sampling, compression; data warehouse modelling: schema for multidimensional data models, concept hierarchies, measures: categorization and computations.

Machine Learning: (i) Supervised Learning: regression and classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias-variance trade-off, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network; (ii) Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple-linkage, dimensionality reduction, principal component analysis.

AI: Search: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics — conditional independence representation, exact inference through variable elimination, and approximate inference through sampling.

GATE Data Science and Artificial Intelligence subject wise weightage

DA as a subject was introduced in GATE 2024 for first time and therefore it’s been only two years of GATE DA exam. In this section we are providing subject wise weightage of GATE DA so that candidates preparing for GATE DA 2026 can have an idea of different subject asked in GATE DA.

GATE 2024 subject wise weightage

GATE 2025 subject wise weightage

GATE DA 2026 exam pattern

As we know, GATE exam is conducted in CBT mode. The total duration of GATE exam is 3 hours with a total weightage of 100 marks. GATE is conducted in English language only in different cities of India. There would be two sections in the GATE 2026 DA exam, viz., the aptitude part and the technical/candidate-selected paper. Different types of questions like MCQ, MSQ, and NAT-type questions will be asked in the GATE 2026 exam. The questions designed would test the recall, comprehension, application, analysis, and synthesis ability of the student.

Importance of Machine Learning for GATE DA

From the above subject wise weightage, readers might have got an idea of important subjects for GATE DA 2026. As per the subject wise weightage, one can easily deduce that Machine Learning is one of the most important subject for GATE DA. In last two year, 8 questions (12.3% of total questions) were asked from machine learning subject with an average of 11.5 marks. This data shows that GATE DA aspirants cannot ignore machine learning subject. Also machine learning is one of the basic subject of Data Science, therefore it is very important that GATE DA 2026 aspirants prepare this subject properly. But the question is how? How students can prepare machine learning for GATE DA, tips and study guide of machine learning for GATE DA.

How to approach machine learning for GATE DA

Data Science and Artificial Intelligence is a relatively new subject in GATE. Only last two year of exam papers are available for DA stream and therefore it is important that aspirants should take proper care while preparing machine learning for GATE DA. Let us look at some tips which can help aspirants to prepare machine learning for GATE DA 2026.

• Get the syllabus: The first and basic tip to clear any exam is to get the official updated syllabus. Aspirants who are preparing Machine Learning for GATE DA stream should get the updated official syllabus of Data Science and Artificial Intelligence for GATE.
• Arrange all the resources: before starting preparation, aspirants should arrange all the resources required for study. This includes standard books, video lectures, reference materials etc. Those students who have joined a good coaching institution like MADE EASY need not to worry about resources. The faculties of MADE EASY are highly experienced and knowledgeable. The class notes of MADE EASY are sufficient to clear GATE exam.
• Join a good coaching institute: A good coaching institute like MADE EASY help students to prepare for competitive examinations with relative ease. The faculties of MADE EASY are highly experienced and knowledgeable and helps students in note making as well as revision of different subject which helps in preparing for exams like GATE. Aspirants should attend classes on regular basis so that they don’t miss any important concept.
• Build mathematical foundation: As we know, GATE is generally numerical based exam, therefore aspirants preparing machine learning for GATE DA exam should focus upon mathematical concepts like linear algebra, probability and statistics etc.
• Revise multiple times: Revision is very important for exams like GATE. Aspirants should revise machine learning multiple times to perform well in GATE DA 2026. While revision, candidates should also practice GATE type questions and GATE previous year questions for better understanding of concepts.
• Make short notes: Candidates should make short notes after revision, the short notes should include important facts, concepts, formulas and other important points in their short notes so that they don’t need to go through the complete notes for last minute revision.
• Attempt mock test: Attempting mock test help students to check their conceptual clarity. It also helps in enhancing student’s confidence.

 Following the above mentioned tips can help GATE DA aspirants to prepare machine learning. However, these tips are general tips and aspirants can change or modify them as per their need.

Courses in IITs from GATE DA

As said earlier, DA is an interdisciplinary field, which means students of different engineering streams are eligible for GATE DA. In this section we are going to list some famous and preferred M.Tech courses in IITs from GATE DA.

• AI
• Computational and Data Science
• Signal Processing through Machine Learning (ML) and Deep Learning (DL)
• Cognitive System
• Computational Mathematics
• Robotics and AI

These are some of the famous M.Tech courses in different IITs. We have not listed all the courses, but we encourage students to conduct thorough research before enrolling in any course.

Study resource for Machine Learning in GATE DA

Till now candidates would have gotten some idea about GATE DA, its important subjects of DA for the GATE examination, their weightage, and how to prepare. Let us now move to study resources for machine learning in GATE DA.

Students who are preparing for GATE DA can join MADE EASY classes’ classroom course for strengthening their preparation. MADE EASY also provides the facility of live online courses for those students who can’t join classroom courses. Not only this, but MADE EASY also offers recorded courses, tablet courses, and postal courses for working professionals or students who couldn’t study in live online mode or in classroom courses due to other commitments. These courses are conducted by expert faculties who have vast teaching experience and knowledge. These faculties have mentored lakhs of students, and MADE EASY results are the proof of the commitment and dedication of MADE EASY faculties.

To know about courses, students/readers can visit here.

Why MADE EASY

MADE EASY is one of the oldest coaching institutes, which provides guidance to the aspirants of GATE, ESE, and other engineering-related exams. In terms of results, MADE EASY is unmatchable. Let us have a look at the MADE EASY results of GATE 2025.

Civil Engineering

• 10 ranks in top 10
• 15 ranks in top 20
• 69 ranks in top 100
• 83% selections are from classroom and online courses.

Mechanical Engineering

• 9 Ranks in Top 10
• 13 Ranks in Top 20
• 64 Ranks in Top 100
• 66% Selections are from Classroom & Online Courses

Electrical Engineering + Computer Science Engineering

• 6 Ranks in Top 10
• 15 Ranks in Top 20
• 97 Ranks in Top 100
• 42% Selections are from Classroom & Online Courses

Electronics Engineering + Instrumentation Engineering

• 9 Ranks in Top 10
• 22 Ranks in Top 20
• 87 Ranks in Top 100
• 35% Selections are from Classroom & Online Courses

Engineering Science + Environmental Science and Engineering

• 7 Ranks in Top 10
• 10 Ranks in Top 20
• 36 Ranks in Top 100
• 79% Selections are from Classroom & Online Courses

Production and Industrial Engineering

• 5 Ranks in Top 10
• 13 Ranks in Top 20
• 48 Ranks in Top 100
• 75% Selections are from Classroom & Online Courses

These are the glimpse GATE 2025 results of MADE EASY. For complete results of GATE 2025 as well as previous results by MADE EASY, readers can visit here.

FAQs:

Is Machine Learning included in the GATE DA 2026 syllabus?

Answer: Yes, Machine Learning is included in GATE DA 2026 syllabus.

What is DA in GATE?

Answer: DA stands for Data Science and Artificial Intelligence.

How much weightage does Machine Learning have in the GATE DA exam?

Answer: Generally Machine learning has a weightage of 11-12 marks in GATE DA exam.

Which Machine Learning topics are most important for GATE DA 2026?

Answer: Candidates preparing for GATE DA 2026 should try to cover the whole syllabus of Machine Learning to score maximum marks.

What is the best way to prepare Machine Learning for GATE DA?

Answer: The best way to prepare for Machine Learning for GATE DA is that aspirants should finish the complete syllabus of machine learning given in the official notification and make their own handwritten notes. After completion of the syllabus, candidates should attempt previous year questions from the MADE EASY GATE Previous Year Solved Paper. After solving all the previous year’s papers, candidates should attempt the MADE EASY mock test to strengthen their preparation.

Which books are recommended for Machine Learning in GATE DA 2026?

Answer: Aspirants preparing for GATE DA 2026 can follow MADE EASY books to strengthen their preparation.

Do I need to study advanced Machine Learning or Deep Learning for GATE DA?

Answer: Candidates are advised to refer to the official syllabus of GATE DA to filter out topics.