Machine Learning course page — syllabus, duration, fees, and curriculum details
ML
ML Certification
New batch
Hiring support
Live + recorded
Machine
Learning
Go from zero to production-ready ML engineer. Master supervised & unsupervised learning, deep neural networks, NLP, computer vision, and model deployment on real-world datasets.
4 mo
Duration
10
Modules
150+
Coding hrs
6
Projects
92%
Placement
Syllabus
10 modules · 16 weeks
01
Python & data science foundations
Python syntax, OOP, file I/O
NumPy, Pandas, Matplotlib, Seaborn
Lab: EDA on real-world dataset
02
Statistics & probability for ML
Probability distributions, Bayes theorem
Hypothesis testing, p-values, confidence intervals
Lab: statistical analysis of customer data
03
Supervised learning — regression
Linear, ridge, lasso, polynomial regression
Loss functions, gradient descent, overfitting
Lab: house price prediction
Project: sales forecasting model
04
Supervised learning — classification
Logistic regression, SVM, KNN, Naive Bayes
Decision trees, random forests, XGBoost
Confusion matrix, ROC/AUC, precision/recall
Project: churn prediction system
05
Unsupervised learning & clustering
K-Means, DBSCAN, hierarchical clustering
PCA, t-SNE, dimensionality reduction
Lab: customer segmentation
06
Deep learning & neural networks
Perceptrons, backpropagation, activation functions
PyTorch & TensorFlow/Keras fundamentals
Regularisation, batch norm, dropout
Project: digit recogniser (MNIST)
07
Computer vision with CNNs
CNN architectures: ResNet, VGG, EfficientNet
Transfer learning, data augmentation
Project: image classification web app
08
Natural language processing
Tokenisation, TF-IDF, word embeddings
Sentiment analysis, text classification
Transformers & HuggingFace basics
Project: sentiment analyser API
09
Model evaluation, tuning & pipelines
Cross-validation, GridSearch, Optuna
Sklearn pipelines, feature engineering
Lab: end-to-end ML pipeline with MLflow
10
Capstone: production ML project
Docker, FastAPI, model serving
Deploy to AWS / HuggingFace Spaces
Build & deploy your own ML product
Demo day + certification ceremony
Learning timeline
Weeks 1–3
Foundation phase
Python mastery, NumPy/Pandas, statistics & probability, exploratory data analysis.
Weeks 4–8
Core ML phase
Supervised & unsupervised algorithms, model evaluation, feature engineering, ensemble methods.
Weeks 9–13
Deep learning phase
Neural networks, CNNs for vision, NLP with transformers, HuggingFace model hub.
Weeks 14–16
Production & capstone phase
MLOps, model deployment, capstone project build, demo day, portfolio review, job prep.
Tools & technologies
Languages
Python
SQL
R (basics)
ML / DL Libraries
Scikit-learn
PyTorch
TensorFlow
HuggingFace
XGBoost
MLflow
Data & Visualisation
Pandas
Matplotlib
Seaborn
Plotly
NumPy
Deployment & MLOps
Docker
FastAPI
AWS SageMaker
GitHub Actions
Fees & enrolment
₹34,999
₹59,999
Save 42%
EMI from ₹2,900/mo · 0% interest available
150+ hours live sessions
Lifetime recorded access
6 real-world projects
1:1 mentor sessions
Resume & LinkedIn review
Mock interviews (3 rounds)
Placement assistance
UpsSkills certificate
Skills you gain
Python
Scikit-learn
PyTorch
Deep Learning
NLP
Computer Vision
MLOps
Statistics
Certification
UpsSkills Certified Machine Learning Engineer
Industry-recognized credential. Shareable on LinkedIn with a verified badge and project portfolio link.
Your mentors
RK
Ravi Kumar
Senior ML Engineer · ex-Google
NV
Nisha Verma
Data Scientist · ex-Amazon
SK
Suresh Krishnan
AI Research Engineer · ex-DeepMind
FAQs
Do I need prior coding experience?
No. We start from Python basics. Basic math (10th grade level) helps but is not required — we cover all the statistics you need.
Live or self-paced?
Primarily live sessions on weekends (Sat–Sun, 10am–1pm IST) with all recordings available for lifetime access.
What kind of jobs can I get?
ML Engineer, Data Scientist, AI Developer, Research Analyst. Our hiring partners include top product companies and funded startups.
What's the batch size?
Capped at 25 students per batch to ensure quality mentoring and 1:1 attention from instructors.
