Machine Learning Course in Ahmedabad

Types of Machine Learning

Vectors, matrices, and matrix operations are the language of every ML algorithm — covered with the specific operations used in real models, not abstract linear algebra

Python for ML

Gradient descent and optimisation is the single most commonly asked "explain it from scratch" question in ML interviews — you will be able to derive and code it

Linear Algebra & Calculus Review

Cost functions and loss functions (MSE, cross-entropy) are asked about constantly — understanding why each is used for its respective problem type is the expected standard

Data Preprocessing

Building a model from scratch with NumPy before using a library is what makes the library use meaningful rather than magical

Regression Algorithms

Support Vector Machines and kernel methods are a step beyond the generalist data science curriculum — covered here with the math behind kernel tricks

Classification Algorithms

XGBoost and gradient boosting are the most widely used production algorithms in real ML systems — covered with hyperparameter tuning, not just default settings

Ensemble Methods

Bias-variance tradeoff is asked about in essentially every ML interview — you will be able to diagnose it from a learning curve, not just define it

Model Tuning

Hyperparameter tuning (GridSearch, RandomSearch, Bayesian optimisation) is what separates a notebook model from a competition-grade one

Clustering

Overfitting vs underfitting diagnosis via learning curves and validation curves is tested with real plots in interviews, not just definitions

Dimensionality Reduction

Data leakage is one of the most common silent failures in real ML projects — recognising the patterns that cause it is a skill experienced practitioners are specifically hired for

Anomaly Detection

Class imbalance techniques (SMOTE, class weighting, resampling) are asked about whenever a dataset resembles fraud detection or churn prediction — extremely common interview scenario

Q-Learning Basics

Cross-validation strategies (k-fold, stratified, time-series split) chosen correctly for the problem type is a detail that distinguishes ML specialists from generalists

Neural Network Architecture

Forward propagation, backpropagation, and activation functions are asked about conceptually in nearly every ML specialist interview — covered here with worked examples, not just diagrams

Backpropagation

CNNs and their use in image tasks are the most common practical deep learning project in interviews and portfolios — built here on a real dataset

CNNs & RNNs

Overfitting in deep learning (dropout, batch normalisation, early stopping) is a distinct skill from classical ML regularisation — covered specifically for neural networks

Transfer Learning

TensorFlow/Keras is the most widely deployed deep learning framework in Indian industry roles — hands-on fluency, not just familiarity, is the goal here

Model Versioning with MLflow

Model serialisation and serving via REST API is the minimum production skill expected from an ML specialist in 2025

Feature Stores

Concept drift and data drift are increasingly asked about in ML interviews as companies have been burned by silently degrading models in production

Model Monitoring

Docker and containerisation for ML models is now a common requirement in job postings, even at companies that previously only needed it for software engineers

Deployment Pipelines

Experiment tracking (even basic logging of metrics and parameters) is a discipline that distinguishes a hobby project from an industry-grade one