A Machine Learning mock test from Toppersexam.com is a practice paper based on the latest exam pattern and syllabus, designed to help you assess your preparation level.
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To crack the Machine Learning 2026 exam, understanding the detailed syllabus and exam pattern is mandatory. Aspirants should focus on strategic planning and rigorous time management for each selection phase. Boosting your speed through online mock tests and regular practice is the key to securing a high score in this competitive recruitment. For premium study material and practice sets, follow the expert curated resources on toppersexam.com.
Machine Learning Free Mock Test
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Machine Learning Syllabus, Exam Pattern
Machine Learning Syllabus, Exam Pattern
Machine Learning Syllabus
Machine Learning :
1. Foundations of Machine Learning
Introduction to Machine Learning: Definition, scope, applications
Types of Learning:
i. Supervised learning
ii. Unsupervised learning
iii. Semi-supervised learning
iv. Reinforcement learning
v. Basics of Linear Algebra, Probability, and Statistics for ML
vi. Overfitting, underfitting, bias-variance tradeoff
2. Supervised Learning Algorithms
Regression Techniques:
i. Linear regression, Multiple regression
ii. Polynomial regression
iii. Logistic regression
Classification Techniques:
i. k-Nearest Neighbors (k-NN)
ii. Naïve Bayes Classifier
iii. Decision Trees
iv. Random Forests
v. Support Vector Machines (SVM)
vi. Gradient Boosting methods (XGBoost, LightGBM, CatBoost)
vii. Evaluation Metrics: Accuracy, Precision, Recall, F1-score, ROC, AUC
3. Unsupervised Learning Algorithms
Clustering Methods:
i. k-Means clustering
ii. Hierarchical clustering
iii. DBSCAN
Dimensionality Reduction:
i. Principal Component Analysis (PCA)
ii. Linear Discriminant Analysis (LDA)
iii. t-SNE, UMAP
iv. Association Rule Learning: Apriori, FP-growth
4. Neural Networks & Deep Learning
i. Basics of Neural Networks: Perceptron, Multilayer Perceptron (MLP)
ii. Backpropagation and Gradient Descent
iii. Activation Functions
iv. Convolutional Neural Networks (CNNs)
v. Recurrent Neural Networks (RNNs), LSTMs, GRUs
vi. Autoencoders and Variational Autoencoders (VAE)
vii. Generative Adversarial Networks (GANs)
5. Optimization Techniques
i. Gradient Descent (Batch, Stochastic, Mini-batch)
ii. Learning rate schedules, momentum
iii. Adam, RMSProp, Adagrad, Nadam
iv. Regularization techniques (L1, L2, Dropout, Batch Normalization)
6. Reinforcement Learning (RL)
i. Basics of RL: Agent, Environment, Rewards, Policy, Value Function
ii. Dynamic Programming
iii. Monte Carlo Methods
iv. Temporal Difference (TD) Learning
v. Q-Learning & SARSA
vi. Deep Reinforcement Learning (DQN, Policy Gradients, Actor-Critic methods)
7. Probabilistic Models
i. Bayesian Networks
ii. Hidden Markov Models (HMMs)
iii. Gaussian Mixture Models (GMMs)
iv. Markov Decision Processes (MDPs)
8. Natural Language Processing (NLP)
i. Text preprocessing: Tokenization, Stop words, Lemmatization, Stemming
ii. Word Embeddings: Word2Vec, GloVe, FastText
iii. Transformers: Attention Mechanism, BERT, GPT
iv. Applications: Text classification, Sentiment analysis, Machine translation, Chatbots
9. Computer Vision (CV)
i. Image preprocessing techniques
ii. Feature extraction (SIFT, SURF, HOG)
iii. Object Detection: R-CNN, Fast R-CNN, YOLO, SSD
iv. Image Segmentation: U-Net, Mask R-CNN
10. Model Evaluation and Deployment
i. Cross-validation and resampling methods
ii. Hyperparameter tuning (Grid Search, Random Search, Bayesian Optimization)
iii. Model explainability (SHAP, LIME)
iv. ML Pipelines (Scikit-learn, TensorFlow, PyTorch, Keras)
v. Model Deployment (Flask, FastAPI, Docker, Cloud platforms)
11. Advanced Topics
i. Transfer Learning
ii. Few-Shot and Zero-Shot Learning
iii. Federated Learning
iv. Online Learning
v. Ethical AI and Fairness in ML
vi. Bias and Interpretability issues
Machine Learning Exam Pattern
Duration : 60 Minutes
No.of Question : 100
Total Marks : 100
Machine Learning Free Online Test Series
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Machine Learning Exam Free Online Question Bank
Machine Learning question answer with solutions are available in Hindi & English. Join MCQ, Important Questions, Question Bank, questions and answers for Free Machine Learning exam preparation.
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Machine Learning Free Mock Test Frequently Asked Questions (FAQ's)
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