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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Your Ultimate Study Guide for GATE 2025 in DS & AI

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 09 Dec, 2024
5 min read

Introduction

The Graduate Aptitude Test in Engineering (GATE) is an entrance examination conducted in India for postgraduate admission. The exam primarily tests the comprehensive understanding of undergraduate subjects in engineering and sciences. If you’re gearing up for the GATE 2025 in Data Science and AI, introduced by IIT Roorkee, you’re in the right place. This article is a treasure trove of references – study materials, lecture notes, and standard books – that will be your compass as you navigate this new and exciting GATE paper.

The main subjects that will form the cornerstone of your preparation include Probability and Statistics, Linear Algebra, Machine Learning, AI, and more. These are not just any subjects; they are the pillars upon which the edifice of Data Science and AI stands. The resources I’m about to reveal come straight from the hallowed halls of IISc Bangalore, tested and recommended by the professors.

Also Read: GATE DA 2025: Eligibility, Important Dates, Syllabus and More

Table of Contents

Probability and Statistics: The Game of Chance and Data

When it comes to Probability and Statistics, expect to be challenged. This subject carries significant weight in the GATE DS & AI paper, with many additional topics compared to the CSE syllabus. To conquer this beast, you’ll need to arm yourself with the right books. I recommend starting with “A First Course in Probability” by Sheldon Ross, a staple even at the undergraduate level. Once you’ve got the hang of it, level up with “Introduction to Probability Models” by the same author.

Probability and statistics for GATE 2024 DS & AI paper

For those hungry for more advanced knowledge, delve into “Introduction to Probability Theory” by S.C. Port and C.J. Stone, followed by “Introduction to Stochastic Processes.” These books will take you deeper into the realm of stochastic modeling and theoretical probability.

And when it comes to lecture videos, MIT’s offerings on Probability and Statistics are unparalleled. Whether you prefer the comprehensive older playlist or the newer, bite-sized topic-focused videos, they’ve got you covered. Don’t forget to check out probabilitycourse.com for a wealth of examples and exercises that align perfectly with the GATE syllabus.

Linear Algebra: The Fabric of Data Science

Linear Algebra is another subject that demands your attention. With new topics like Vector Spaces and Singular Value Decomposition added to the GATE syllabus, you can’t afford to skim the surface. To build a strong foundation, immerse yourself in the lecture videos by Gilbert Strang, available on YouTube through MIT’s channel. His teachings will not only educate but inspire you.

Accompany these lectures with Strang’s own book on Linear Algebra for a comprehensive understanding of the subject. “Linear Algebra and Its Applications” by David C. Lay is another gem on the topic. These texts are your keys to unlocking the mysteries of Linear Algebra.

Linear Algebra

The Calculus and Optimization Duo

Calculus might be a smaller portion of the syllabus, but it’s no less important. Stick to the resources you’ve used during your undergraduate days, as the GATE syllabus doesn’t stray far from the CSE topics. For Optimization, however, I suggest you turn to the NPTEL lecture series on Numerical Optimization by Professor Suresh Chandra. The first five lectures will guide you through the intricacies of single-variable optimization and the Taylor series, crucial for the GATE DS & AI paper.

Programming, Data Structures, and Algorithms: The Python Twist

While Data Structures and Algorithms remain similar to the CSE paper, the programming language of choice for GATE DS & AI is Python. To adapt, practice CSE paper programming questions and simultaneously hone your Python skills through platforms like LeetCode or Analytics Vidhya. This dual approach will prepare you for the type of programming questions you can expect in the exam.

Python for GATE 2024 DS & AI paper

Machine Learning: The Heart of Data Science

Machine Learning is a vast field with abundant resources. However, two lecture series stand out: “Pattern Recognition and Neural Networks” by Professor P.S. Sastry from IISc Bangalore and the Stanford CS229 course by Professor Andrew Ng. These comprehensive videos and the lecture notes from Professor Piyush Rai of IIT Kanpur will solidify your understanding of Machine Learning concepts.

To complement these lectures, turn to the books “Pattern Recognition and Machine Learning” by Christopher Bishop and “Machine Learning: A Probabilistic Perspective” by Kevin Murphy. These texts are revered in the field and will serve as your scholarly guides.

Artificial Intelligence: The Frontier of Innovation

AI is a vast domain, but the GATE 2025 syllabus narrows it down to a few key topics. While there aren’t as many consolidated resources for AI, you can still find valuable lecture videos and notes by searching for specific subtopics online. Courses by Professors Mausam from IIT Delhi and Deepak Khemani from IIT Madras can offer some guidance.

GATE 2024 Exam - Data Science & Artificial Intelligence

Database Management System and Data Mining: The Data Stewards

For Database Management Systems, stick to the resources you’ve used for the CSE paper. Data Mining, however, requires a more exploratory approach. Search for tutorials and lecture notes on each topic to find a wealth of PDFs from various institutions that can aid your study.

Remember, while Data Mining and AI might not dominate the paper, subjects like Machine Learning, Probability and Statistics, Linear Algebra, and Data Structures and Algorithms will. Focus your energy on these areas, as they will likely make up the bulk of the exam.

Conclusion

Data Science & Artificial Intelligence is the latest added paper in the GATE 2025 exam. This article was aimed at guiding aspirers to prepare better for the exam. Probability and Statistics are heavily weighted in the GATE DS & AI paper, with additional topics beyond the CSE syllabus. Most of the exams will likely cover Machine Learning, Probability and Statistics, Linear Algebra, and Data Structures and Algorithms. Don’t be overwhelmed by the list of topics. Rest assured that the above resources will help you cover them all extensively. So, good luck with your preparations, and I hope you clear GATE 2025 with flying colours!

Checkout the – Sample Question Paper for GATE DA 2025

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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