Back to Premium Vault
Premium Course Syllabus

Machine Learning

Condensed Placement Preparation Syllabus

Core ML · Supervised & Unsupervised Learning · Neural Networks · Deployment Basics

7 Modules
45 Topics
7 Quizzes
Industry

Ideal For

Students preparing for Data Science / ML roles in campus placements
Engineers moving from software to ML / AI teams
Anyone building a portfolio of end-to-end ML mini-projects
Interview prep for ML/DS rounds at product companies

Module-Wise Curriculum

A concise, placement-focused syllabus — covers every key concept without overloading.

1

Module 1

Introduction to Machine Learning

  • What is Machine Learning — definition, motivation, and real applications
  • AI vs ML vs Deep Learning — where each fits in the stack
  • Types of ML — supervised, unsupervised, reinforcement learning
  • ML workflow and lifecycle — data → model → deployment
  • Data collection, preprocessing, and Exploratory Data Analysis
  • Feature engineering basics and math foundations for ML
  • Bias, variance, overfitting, underfitting and generalization
Quiz — ML Foundations
2

Module 2

Supervised Learning Basics

  • Features, labels, targets — how supervised data is structured
  • Regression vs classification — when to use each
  • Train / validation / test split and cross-validation
  • Loss and cost functions — MSE, cross-entropy intuition
  • Evaluation metrics — RMSE, MAE, accuracy, precision, recall, F1
  • Confusion matrix and error analysis
  • Overfitting and underfitting in supervised models
Quiz — Supervised Learning
3

Module 3

Linear Models, kNN, Naive Bayes, Decision Trees, SVM

  • Linear and multiple linear regression — assumptions and interpretation
  • Logistic regression for classification
  • k-Nearest Neighbors and distance metrics
  • Naive Bayes classifier and Bayes theorem
  • Decision trees — Gini impurity and entropy
  • Support Vector Machines — margin, hyperplane, kernel trick
  • Model comparison and selection strategy
Quiz — Classical ML Algorithms
4

Module 4

Unsupervised Learning

  • Introduction to unsupervised learning and use cases
  • K-Means, Hierarchical clustering, DBSCAN
  • Dimensionality reduction and Principal Component Analysis (PCA)
  • Anomaly detection basics
  • Association rule learning — support, confidence, lift
  • Cluster evaluation and workflow
Quiz — Unsupervised Learning
5

Module 5

Ensemble Methods & Feature Engineering

  • Ensemble learning — bagging, boosting, stacking, voting
  • Random Forest and Gradient Boosting intuition
  • Feature engineering — encoding, scaling, interaction features
  • Handling missing values and outliers
  • Text feature engineering basics
  • Model selection and hyperparameter tuning
Quiz — Ensembles & Features
6

Module 6

Neural Networks & Deep Learning Basics

  • Artificial neuron and activation functions
  • Feedforward neural networks and loss functions
  • Gradient descent and backpropagation intuition
  • Epoch, batch, iteration — training loop mechanics
  • Regularization, dropout, vanishing / exploding gradients
  • Introduction to CNNs, RNNs, LSTM and GRU
Quiz — Neural Networks
7

Module 7

Deployment, MLOps, Ethics & Projects

  • Model deployment — inference, serialization, APIs
  • Batch vs real-time prediction
  • Model monitoring, data drift and retraining
  • Introduction to MLOps — version control, experiment tracking, CI/CD
  • Ethics — bias, fairness, privacy, explainability
  • End-to-end ML mini-project workflow
Practice — End-to-End ML Project

Pattern-First

Learn recurring patterns, not one-off tricks — the fastest path to solving new problems.

Placement-Ready

Every module ends with an assessment or drill mapped to the way rounds are actually run.

AI-Assisted

Unlimited AI Tutor, Mock Interviews and Resume Review are included with your subscription.

Unlock Machine Learning and 13 more courses

One subscription. Every structured course, every AI tool, every private update — for a full year.

Machine Learning Notes & Interview Questions

A machine learning course scoped to what fresher interviews and ML internships ask: the supervised and unsupervised split, linear and logistic regression, decision trees and ensembles, SVM, k-NN, naive Bayes, k-means and dimensionality reduction, with the intuition and the maths you must be able to state.

It also covers the workflow questions that decide interviews — bias and variance, overfitting and regularisation, cross-validation, feature engineering and scaling, class imbalance, and the correct metric for a given problem (accuracy versus precision, recall, F1, ROC-AUC) — plus a short neural network primer.

What you'll learn in Machine Learning

  • Introduction to Machine Learning
  • Supervised Learning Basics
  • Linear Models, kNN, Naive Bayes, Decision Trees, SVM
  • Unsupervised Learning
  • Ensemble Methods & Feature Engineering
  • Neural Networks & Deep Learning Basics
  • Deployment, MLOps, Ethics & Projects

Why Machine Learning matters for placements

ML interviews for freshers focus far less on model zoo trivia than students expect, and far more on whether you can justify a metric, spot leakage and explain overfitting. Getting those fundamentals right is what makes a project on your resume defensible.

Free vs Premium — what's included

Free

  • This full module-wise syllabus and topic list
  • The free revision course for this subject
  • Free core CS question bank and roadmaps
  • Aptitude and DSA practice sheets

Premium

  • Full lesson content for every module listed above
  • Module quizzes and interview question sets
  • Worked examples, numericals and revision drills
  • All 14 premium placement courses, lifetime access

Frequently asked questions

What maths do I need for machine learning interviews?

Linear algebra basics, probability and statistics, and gradient descent intuition. You should be able to explain a cost function and how the model minimises it.

Which ML metric should I use?

It depends on the cost of errors: precision when false positives are expensive, recall when false negatives are, F1 for a balance, and ROC-AUC for ranking quality. Accuracy misleads on imbalanced data.

Is a machine learning project necessary on a fresher resume?

One end-to-end project you can explain — data, features, model choice, metric, failure cases — is worth more than three tutorials you cannot defend.

Also revise

Data ScienceSQL CourseDSA Course