Crack GATE 2025 DS & AI: Fast-Track to Success

About Course

Prepare effectively for the GATE 2025 Data Science (DS) and Artificial Intelligence (AI) exam with this focused and concise course. Covering all essential topics, this program combines theoretical insights, problem-solving approaches, and hands-on practice to equip you with the skills to excel. Ideal for aspirants aiming for top scores, this course is your gateway to achieving academic and professional excellence in the fast-evolving field of DS and AI.

Master Fundamental Concepts: Build a strong foundation in mathematics, programming, and AI.
Enhance Problem-Solving Skills: Learn to approach and solve GATE-level problems effectively.
Gain Practical Knowledge: Apply concepts in programming, machine learning, and AI.
Optimize Exam Performance: Improve speed and accuracy through rigorous practice.
Excel in GATE 2025: Prepare to secure top scores and pursue your dream opportunities.

Course Content

Section 1: Probability and Statistics
Learn core topics such as probability distributions, hypothesis testing, and statistical inference. These concepts are critical for understanding machine learning and AI algorithms.

Probability and Statistics in One Shot (Part 1)

00:00
Probability and Statistics in One Shot (Part 2)

00:00
Prob and Stats GATE 2024 Practice

00:00

Section 2: Linear Algebra
Master vector spaces, matrices, eigenvalues, and eigenvectors. Linear algebra forms the backbone of machine learning and optimization techniques.

Section 3: Calculus and Optimization
Understand differential and integral calculus, gradient descent, and constrained optimization. These topics are essential for model training and optimization in AI.

Section 4: Programming, DS and Algos
Dive into data structures, algorithms, and programming concepts in Python or other languages. Topics include recursion, dynamic programming, trees, and graph algorithms.

Section 5: Database Management and Warehousing
Explore database design, SQL, data warehousing, and ETL (Extract, Transform, Load) processes. These concepts are crucial for managing and analyzing large datasets.

Section 6: Machine Learning
Learn supervised and unsupervised learning techniques, regression, classification, clustering, and evaluation metrics. Get hands-on experience with real-world datasets.

Section 7: Artificial Intelligence

Student Ratings & Reviews

No Review Yet

About Course

What Will You Learn?

Course Content

Section 1: Probability and Statistics Learn core topics such as probability distributions, hypothesis testing, and statistical inference. These concepts are critical for understanding machine learning and AI algorithms.

Probability and Statistics in One Shot (Part 1)

Probability and Statistics in One Shot (Part 2)

Prob and Stats GATE 2024 Practice

Section 2: Linear Algebra Master vector spaces, matrices, eigenvalues, and eigenvectors. Linear algebra forms the backbone of machine learning and optimization techniques.

Linear Algebra in One Shot

Linear Algebra Tricks, Formulas & Patterns

Linear Algebra, Calculus and Optimisation in One Shot

Linear Algebra GATE 2024 Practice

Section 3: Calculus and Optimization Understand differential and integral calculus, gradient descent, and constrained optimization. These topics are essential for model training and optimization in AI.

Linear Algebra, Calculus and Optimisation in One Shot

Calc and Opti Part 1: Functions of a Single Variable

Calc and Opti Part 2: Limits

Calc and Opti Part 3: Continuity and Differentiability

Calc and Opti Part 4: Maxima and Minima

Calc and Opti GATE 2024 Practice

Section 4: Programming, DS and Algos Dive into data structures, algorithms, and programming concepts in Python or other languages. Topics include recursion, dynamic programming, trees, and graph algorithms.

Programming: Python in One Shot

DSA Part 1: Applications of Stack

DSA Part 2: Hash Tables and Queues

DSA Part 3: Linked Lists in Python

DSA Part 4: Tree Fundamentals

DSA Part 5: BST and Binary Heap

DSA Practice (Part 1)

DSA GATE 2024 Practice

Section 5: Database Management and Warehousing Explore database design, SQL, data warehousing, and ETL (Extract, Transform, Load) processes. These concepts are crucial for managing and analyzing large datasets.

Warehousing in One Shot (Part 1)

Warehousing in One Shot (Part 2)

DSMS in One Shot

DBMS & Warehousing GATE 2024 Practice

Section 6: Machine Learning Learn supervised and unsupervised learning techniques, regression, classification, clustering, and evaluation metrics. Get hands-on experience with real-world datasets.

ML Part 1: Regression

ML Part 2: Classification

ML Part 3: Decision Tree

ML Part 4: KNN

ML Part 5: Naive Bayes

ML Part 6: SVM

ML Part 7: MLP

Machine Learning GATE 2024 Practice

Section 7: Artificial Intelligence

Artificial Intelligence in One Shot