Become an ML Engineer

ML Engineering

Train, evaluate, explain, and deploy machine learning models across 13 real Indian business problems. From data preparation and classical ML through deep learning, NLP, MLOps, and production monitoring.

10 months

13 Projects + 2 Capstones

Live Course

PythonScikit-learnXGBoostLightGBMTensorFlowPyTorchBERTMLflowFastAPIDockerEvidently AI

Roles you'll be ready for

ML EngineerData ScientistAI/ML DeveloperApplied Scientist

Industry range: ₹6–12 LPA(fresher, India — market benchmark, not a guarantee)

Modules

Duration

10 months

Mode

Live Online

Projects

13 Projects + 2 Capstones

Support

Career Prep

Curriculum

12 modules. Each module includes a hands-on project. The final module contains your capstone assignments.

🚀 Opening Weeks

Opening Weeks — Python & Mathematics Foundations

26Topics

No prior Python or mathematics assumed. These four weeks build the foundation every module runs on — Python from zero through the mathematical intuitions behind every ML algorithm.

Installing Python, Anaconda, and Jupyter Lab; virtual environments

Variables — integer, float, string, boolean — and why data types matter for data work

if, elif, else — conditional logic; comparison and logical operators

for loop, while loop, range(), break, continue

Defining functions — def, parameters, return, default argument values

Lists — indexing, slicing, append(), remove(), len(), list comprehensions

Dictionaries — key-value access, nested dicts, iteration

NumPy arrays — what they are and why Python lists are too slow for ML

Creating arrays — 1D (vector), 2D (matrix), shape, reshape, dtype

NumPy operations — element-wise arithmetic, boolean indexing, statistical functions

Pandas basics — reading a CSV, .head(), .info(), .describe(), .groupby()

pip install, import, try/except; Joblib — saving and loading trained models

What a function is: input → output — plotting y = mx + b

What slope is: rate of change — interpreting it in a business context

The idea of optimisation: finding the minimum of a cost curve

Gradient descent — the marble rolling down a hill; learning rate (too small vs too large)

Visualising gradient descent on a 2D loss curve; implementing from scratch in NumPy

Probability as a fraction; independent vs dependent events

Conditional probability — the most important concept in business ML

What a distribution is: shape of all possible outcomes; normal distribution

What a matrix is: a grid of numbers — your dataset is a matrix

Matrix addition, scalar multiplication, dot product — the fundamental ML operation

Mean Squared Error — squaring errors penalises big mistakes more

Cross-entropy loss — the loss function for classification problems

Bias: model too simple, misses the pattern; Variance: model memorised training data

The bias-variance tradeoff visualised: underfitting vs overfitting

Module 1

Python for ML

1 Sample Project12Topics

Python proficiency tuned for ML work — NumPy broadcasting, Pandas for feature engineering, and the scikit-learn interface that all ML tools share.

Module 2

Mathematics for ML

1 Sample Project15Topics

The linear algebra, calculus, and probability that every ML algorithm is built on — taught with code alongside theory so concepts connect directly to implementation.

What you will learn

Build end-to-end ML pipelines with scikit-learn Pipeline, preventing data leakage throughout

Train and tune XGBoost, LightGBM, and CatBoost with Optuna hyperparameter search

Explain every model prediction with global and local SHAP analysis

Train CNNs with TensorFlow and fine-tune BERT with PyTorch for NLP tasks

Track experiments reproducibly with MLflow and register models to Model Registry

Deploy prediction endpoints as FastAPI applications containerised with Docker

Monitor deployed models for data drift and concept drift using Evidently AI

Handle class imbalance, evaluate with PR-AUC, and tune thresholds for real business costs

Job outcomes

ML EngineerData ScientistAI/ML DeveloperApplied Scientist

Industry range:₹6–12 LPA

Frequently asked questions

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