Data Science & AI · Live Instructor-Led
Data ScienceCourse with Placement Assistance
A live, instructor-led data science online course — 150+ hours across 10 modules, from Python, SQL and statistics to machine learning, deep learning, MLOps and Generative & Agentic AI. Certification in collaboration with Microsoft, and a 100% Job Opportunity Guarantee.
3 seats left
150+
Live Hours
10
Modules
10
Projects
6 mo
Duration
Admissions Open
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You'll Work In
Scikit-learn
TensorFlow
PyTorch
OpenCV
Docker
FastAPI
MLflow
Power BI
Tableau
LangChain
Python
SQL
Excel
Pandas
NumPy
Scikit-learn
TensorFlow
PyTorch
OpenCV
Docker
FastAPI
MLflow
Power BI
Tableau
LangChain
Python
SQL
Excel
Pandas
NumPy
4.9/5(1000+ reviews)
Ranked No. 1
4.9/5 rating
What is the PaperLive Data Science Course?
The PaperLive programme is a 6-month, live online course of 150+ instructor-led hours across 10 modules — Python, SQL, Excel, statistics, machine learning, deep learning and computer vision, MLOps, and Generative & Agentic AI. Classes run on weekday morning and evening slots, include 10 hands-on projects plus a domain capstone, and end in a certification issued in collaboration with Microsoft. Fees start from ₹66,795 + GST with no-cost EMI available.
Format
Live, instructor-led online classes
Duration
6 months · 150+ hours · 10 modules
Certification
Data Science & AI Expert, with Microsoft
Outcome
100% Job Opportunity Guarantee
Who it is for
Freshers & working professionals · no coding needed
Support
100+ hours of 1:1 mentorship & interview prep
100%
Job Opportunities
24/7
Live Mentor Support
5–45 LPA
Opportunity Range
25+ Years
Experienced Industry Faculty
01
Program at a Glance
Data Science Course Highlights
350+ hours of training, labs and mentorship — plus the projects, interview prep and guarantee behind the placement promise.
01
Live Training & Mentorship
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150+ hours of live, instructor-led training
Learn Python, SQL, ML and AI directly from certified industry mentors in real-time sessions.
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100+ hours of hands-on lab & project work
Build real skills through projects and case studies, not passive video lectures.
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100+ hours of 1:1 mentorship & career coaching
Personalized guidance from enrollment through to offer letter.
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Practitioners with 10–25+ years of industry experience
Train under data scientists who have actually built and shipped ML systems.
02
Curriculum & Portfolio
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Python, SQL, Machine Learning, Deep Learning & Generative AI
A complete, job-focused curriculum — no filler modules.
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10 hands-on projects for your portfolio
Walk into interviews with real, demonstrable work recruiters want to see.
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8 real-world case studies across industries
Apply data science to problems from finance, retail, healthcare and more.
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Job-ready in just 6 months
Nearly 2x faster than industry-standard programs.
03
Interview & Placement Support
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Mock interviews with industry experts
Practice under real interview conditions before it counts.
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Personalized interview preparation sessions
Sharpen your technical storytelling before you sit across the table.
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Dedicated hiring network access
We actively put your profile in front of our hiring partner network.
04
Guarantee & Fees
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100% job opportunity guarantee
No opportunity secured, career fee refunded — backed by a signed agreement.
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Opportunity range: 5–45 LPA
Real earning potential once you are placement-ready.
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Career fee = 15% of total fee
The only portion at risk — and it is fully refundable if we do not deliver.
02
Curriculum
What's Inside the Data Science Online Course
Four instructor-led tracks build on each other — foundations first, production AI last — plus self-paced Power BI and Tableau.
Modules 01–05
Programming & Data Foundations
64 Hours
Python, SQL, Excel, data wrangling with NumPy and Pandas, and the statistics and probability every analyst is tested on.
Modules 06–07
Machine Learning & Deep Learning
40 Hours
Classical ML and ensembles, time-series forecasting, then neural networks, CNNs, transfer learning and computer vision.
Modules 08–09
MLOps & Generative AI
24 Hours
Take a model out of the notebook — versioning, tracking, Docker and FastAPI serving — then LLMs, RAG, prompting and agentic AI.
Module 10 + Self-Paced
Capstone & Business Intelligence
8 Hours + Self-Paced
A domain capstone with mentor review, plus self-paced Power BI and Tableau tracks on the learning dashboard.
The Full Curriculum, Module by Module
10 modules · 150+ instructor-led hours · Python, SQL, ML, Deep Learning, MLOps, Gen AI & Capstone
Module 2 · 12 Hours
Database Fundamentals with SQL
- Databases and query basics: creating/managing tables, constraints, and keys
- Querying data: SELECT, WHERE, ORDER BY, LIMIT, DISTINCT, COUNT, and column aliases
- Modifying data and transactions: INSERT, UPDATE, DELETE, and NULL handling
- Combining tables: INNER/LEFT/RIGHT/FULL OUTER, CROSS and SELF joins, UNION and UNION ALL
- Aggregation and subqueries: GROUP BY, HAVING, ROLLUP/CUBE, CASE, correlated subqueries, EXISTS/IN, and CTEs
Module 3 · 6 Hours
Spreadsheet Analytics with Excel
- Excel essentials: ribbon, workbooks, formatting, AutoSum, AutoFill, and cell references
- Organizing and cleaning data: sorting, AutoFilter, data validation, Find & Replace, and text functions
- Formulas that do the work: SUM/AVERAGE/MIN/MAX/COUNT, IF/AND/OR/NOT, and VLOOKUP/HLOOKUP/XLOOKUP/INDEX-MATCH
Hands-On Project — Classroom Insights Dashboard
Clean and structure a raw attendance-and-marks dataset, apply validation rules, and calculate performance metrics using formulas to surface actionable insights.
Module 4 · 16 Hours
Data Wrangling & Exploratory Analysis
- Numerical computing with NumPy: arrays, broadcasting, and statistical functions
- Data manipulation with Pandas: loading, filtering, indexing, missing data, grouping, and aggregation
- Exploratory Data Analysis: descriptive statistics, outlier detection/treatment, correlation and covariance
- Hypothesis testing: null vs alternative hypotheses, t-tests, ANOVA, Chi-square, and p-values
- Visualization with Matplotlib and Seaborn: line/bar/scatter plots, heatmaps, boxplots, and violin plots
Hands-On Project — Titanic Survival Dataset Deep-Dive
Clean, explore and visualize the Titanic passenger dataset to prepare it for modeling — including Z-score outlier handling, feature engineering and bivariate/multivariate analysis.
Module 5 · 12 Hours
Statistics & Probability for Analytics
- Statistical thinking: descriptive vs inferential statistics, data types and measurement scales
- Describing data: central tendency, spread, and distribution shape (normal, skewed, kurtosis)
- Making inferences: sampling techniques, confidence intervals, t-tests, and p-values
- Probability foundations: conditional probability, Bayes' theorem, Binomial/Poisson/Normal distributions
- Linear algebra essentials: vectors, matrices, transposition, inverses, eigenvalues, and eigenvectors
Hands-On Project — Product Performance Insight Report
Analyze a product dataset to test hypotheses, quantify relationships between variables, and build a simple regression model that identifies key performance drivers.
Module 6 · 28 Hours
Machine Learning
- Foundations: supervised, unsupervised, and reinforcement learning; real-world applications
- Regression techniques: simple/multiple linear regression, polynomial regression, Ridge and Lasso
- Classification techniques: logistic regression, K-Nearest Neighbors, SVMs, and Naive Bayes
- Evaluating and tuning models: confusion matrix, precision/recall/F1, ROC-AUC, cross-validation, grid/random search
- Tree-based methods: decision trees, random forests, and bagging
- Clustering: K-Means, hierarchical clustering, and DBSCAN
- Ensemble learning: bagging vs boosting, AdaBoost, Gradient Boosting, and XGBoost
- Forecasting with time series: ARIMA, exponential smoothing, and moving averages
- Introduction to text data: tokenization, stop-words, stemming/lemmatization, Bag of Words, TF-IDF, and word embeddings
- Guided case studies in regression, classification, and clustering
Hands-On Project — Subscriber Retention Predictor
Build a logistic regression model that predicts customer churn, from raw data to evaluated model — encoding, missing-data strategy, outlier treatment, scaling and scikit-learn evaluation metrics.
Module 7 · 12 Hours
Deep Learning & Computer Vision
- Neural network foundations: neurons, weights, activation functions, forward/backpropagation, gradient descent
- Building with TensorFlow and Keras: framework overview, building and training a basic network
- Convolutional Neural Networks: convolution/pooling layers, image preprocessing, data augmentation
- Transfer learning: reusing pre-trained models (VGG, ResNet) and fine-tuning
- Sequence models: RNN basics, LSTMs, and applications in text, speech, and time series
- Guided case studies: image classification with a CNN and object detection with YOLO
Hands-On Project — Everyday Object Vision Classifier
Train and evaluate a CNN that recognizes everyday objects from an image dataset, complete with preprocessing and performance visualizations.
Module 8 · 10 Hours
MLOps & Model Deployment
- Why models need MLOps: the machine learning lifecycle from data through training, deployment and monitoring
- Reproducibility: data and model versioning with DVC, Git and MLflow
- Experiment tracking: comparing runs, parameters and metrics in MLflow
- Packaging a model: Docker images and dependency pinning
- Serving predictions with FastAPI: /predict endpoints, Pydantic validation and Uvicorn
- Managed training and hosting with Amazon SageMaker
- Monitoring in production: drift, degradation and retraining triggers
Module 9 · 14 Hours
Generative & Agentic AI
- AI and Generative AI foundations: predictive AI vs generative AI, everyday AI applications
- How LLMs work: foundation models, tokenization, embeddings, transformer architectures
- Retrieval and search: semantic similarity, vector databases, and Retrieval-Augmented Generation (RAG)
- Prompting for results: prompt engineering fundamentals and role-based prompting
- Agentic AI: goal-driven behavior, memory, tool use, and feedback loops
- Connecting agents to tools: LLM APIs, function calling, and multi-step reasoning workflows
- AI in operations (AIOps) and Responsible AI: bias, privacy, deepfakes, and enterprise ethics
Hands-On Project — Domain Support Assistant
Design an AI assistant that simulates customer-support conversations for a chosen domain such as retail or tech support, using LLM APIs and conversational design.
Module 10 · 8 Hours + Self-Paced
Capstone Immersion & Business Intelligence
- Choose one domain capstone from eight industry challenges, with mentor review
- Self-paced Power BI: installation and setup, Power Query, DAX and functions, visualizations, reports and dashboards
- Self-paced Tableau: setup and UI, connecting to Excel data sources, charts, graphs, functions, reports and dashboards
- Both BI tracks stay available on the learning dashboard throughout the program
03
The Process
Your Journey: From Enrollment to Dream Job
From your first click to your first paycheque — how this data science and AI course moves you from enrolment to a data scientist role, with placement assistance at every step.
Phase 01
Get set up
Week 0 · 1 week
01
Day 1
Course Enrolment
Counselling call, batch allotment and fee plan — no-cost EMI available at enrolment.
02
Week 1
Microsoft Kit
Learner licence, cloud credits and the official study material unlocked in your dashboard.
Phase 02
Learn and build
Month 1–5 · 150+ live hours
03
Month 1–5
Live Training
Instructor-led classes across 10 modules — Python, SQL, statistics, ML, deep learning and Gen AI.
04
Ongoing
Hands-On Projects
10 graded projects reviewed line by line, built into a portfolio recruiters can open.
05
Monthly
Guest Lectures
Working data scientists from product companies on how the work is really done.
06
Month 5
Certifications
Assessment, capstone defence and your certification in collaboration with Microsoft.
Phase 03
Get placed
Month 5–6 · until you are hired
07
Month 5
Interview Prep
Role-based resume, LinkedIn profiling, 1000+ questions and unlimited mock interviews.
08
Month 6+
Placement
Referrals to hiring partners with drives running until you accept an offer.
5,000+
Hiring partners across India offering data analyst, data scientist and machine learning roles.
₹5–45 LPA
Typical package range for graduates, by prior experience and target role.
Unlimited
Mock interviews with MNC experts, plus role-based resume and LinkedIn profiling.
100%
Job Opportunity Guarantee — placement assistance continues until you are hired, or your career fee is refunded.
Our Hiring Partners
5,000+ hiring partners across India recruiting Paperlive Learning graduates.
Tech Mahindra
Honeywell
Amazon
Microsoft
Cisco
Dell
Wipro
Capgemini
Netflix
Cognizant
IBM
Tech Mahindra
Honeywell
Amazon
Microsoft
Cisco
Dell
Wipro
Capgemini
Netflix
Cognizant
IBM
The Tools You'll Master in These Data Science Classes
The exact stack hiring managers test in every interview — taught hands-on in live classes, not recorded demos.
Python
SQL
Excel
NumPy
Pandas
scikit-learn
TensorFlow
Keras
MLflow
FastAPI
Docker
SageMaker
Power BI
Tableau
Program Benefits
What You'll Gain from This Course
10
Modules
10
Hands-On Projects
100%
Job Guarantee
✓
Python & SQL Fluency: The Non-Negotiable Foundation
30 hours across Python and SQL — from control flow and functions to joins, window-style aggregation, subqueries and CTEs. The two skills every data interview starts with.
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Statistical Rigour: Know Why the Model Works
Hypothesis testing, confidence intervals, distributions, Bayes' theorem and the linear algebra behind the models — so you can defend your results, not just run them.
✓
Machine Learning Depth: 28 Hours on the Core Craft
Regression, classification, tree ensembles, clustering, XGBoost and time-series forecasting, with cross-validation and hyperparameter tuning built into every case study.
✓
Deep Learning & Computer Vision: Beyond Tabular Data
Build and train networks in TensorFlow and Keras, apply CNNs and transfer learning, and work through image classification and object detection with YOLO.
✓
Production Skills: MLOps That Employers Ask About
Version data and models with DVC, Git and MLflow, track experiments, package with Docker, and serve live predictions with FastAPI — the half of the job notebooks never teach.
✓
Generative & Agentic AI: The Skills of the Next Five Years
Foundation models, embeddings, vector databases and RAG, prompt engineering, tool-using agents, and Responsible AI practice for enterprise settings.
04
Career Outcomes
Career Outcomes & Job Market
Where our graduates get hired — real roles, real cities, real salary growth.
Top Job Roles
Data Scientist
Machine Learning Engineer
Data Analyst
Business Intelligence Analyst
NLP Engineer
MLOps Engineer
Top Job Locations
Bangalore
Hyderabad
Pune
Chennai
Gurugram
Mumbai
Career Growth
Jan
Feb
Mar
Apr
May
Jun
Jul
Live Job Market
50,000+
Data Science, ML & Analytics openings live across India right now
How PaperLive Learning Helps You Win
Learning Assistance
Career Guidelines
Career Guidelines
- •
Get personalized career guidance tailored to the data analyst, data scientist or machine learning engineer track you are aiming for.
- •
Learn how to structure your resume, prepare for interviews, and stand out to hiring managers.
- •
Access insights from industry experts on which skills are in demand — and how to stay ahead in the data science job market.
Student Support
- •
Questions? Need help with your training journey?
- •
Our friendly Student Support Team is just a click away — whether it's about your certification course, live sessions, access, or anything urgent, we've got your back, every step of the way.
- •
Simply hit the "Contact Us" button on the Learn platform and we'll respond ASAP.
- •
Real people. Real support. Real fast.
Data Science Course Fee — Simple & Transparent
No hidden costs. One clear investment for a career-transforming Data Science certification.
₹66,795
+ GST · No-Cost EMI available
₹8,888 / month
via Bajaj Finserv, ShopSe & Northern Arc
Scholarships & early-bird discounts available — up to 35% off.
Upcoming Batches — Data Science Online Classes
Weekday online classes designed for working professionals — no career break needed.
Weekdays
21 Sep - 20 Mar 2027
8:00 PM
Sold Out
Weekdays · Next Batch
28 Sep - 27 Mar 2027
8:00 PM
3 Seats Left
Weekdays
05 Oct - 03 Apr 2027
8:00 PM
5 Seats Available
★
Limited-Time Offer
Looking for a Data Science Course Discount Coupon?
Check if you qualify for a scholarship or early-bird discount before the batch fills up. Takes less than a minute.
05
Meet the Team
Who Teaches Your Data Science Classes
Your data scientist classes are led by practising analysts, ML engineers and Microsoft Certified Trainers — each with a decade or more in industry.
Lead Data Science Faculty photo
Lead Data Science Faculty
Microsoft Certified Trainer
A decade-plus building production ML systems, with deep teaching experience across Python, statistics and supervised learning for career switchers.
Machine Learning Engineer photo
Machine Learning Engineer
MLOps & Deployment Specialist
Ships models for a living — SageMaker, MLflow, Docker and FastAPI — and teaches the deployment half of the curriculum from real production experience.
Applied AI Practitioner photo
Applied AI Practitioner
Generative & Agentic AI Lead
Works on LLM applications, RAG pipelines and tool-using agents, and leads the Generative AI module with live, hands-on builds.
PF
Portfolio
Your Project Portfolio
Guided hands-on projects plus a domain capstone — a portfolio you can walk an interviewer through.
Module 01
Command-Line Calculator
A console application handling four arithmetic operations with input validation and graceful error handling — Python fundamentals, control structures, functions and I/O.
Module 03
Classroom Insights Dashboard
Clean a raw attendance-and-marks dataset in Excel, apply validation rules, and calculate performance metrics with formulas and lookups to surface actionable insights.
Module 04
Titanic Survival Deep-Dive
Wrangle, explore and visualize the Titanic dataset with Pandas, Matplotlib and Seaborn — missing values, Z-score outliers, feature engineering, multivariate analysis.
Module 05
Product Performance Insight Report
Test hypotheses on a product dataset, quantify variable relationships, and build a regression model that identifies the real performance drivers.
Module 06
Subscriber Retention Predictor
End-to-end churn model in scikit-learn: encoding, missing-data strategy, IQR/Z-score outlier treatment, feature scaling and full evaluation metrics.
Module 07
Everyday Object Vision Classifier
Train a CNN to recognize everyday objects from an image dataset, with preprocessing, augmentation and performance visualizations in TensorFlow.
Module 09
Domain Support Assistant
An AI assistant that simulates customer-support conversations for a chosen domain, built on LLM APIs with deliberate conversational design.
Module 10
Domain Capstone
One industry challenge of your choosing, taken from raw data to a reviewed, presentable result, with mentor guidance across eight guided hours.
06
Capstone
Choose Your Domain Capstone
Eight hours of guided capstone immersion with mentor review — pick the industry you want to be hired into.
Healthcare Trends Analysis
Public health data
Emotion Recognition
Marketing & human-computer interaction
Distracted Driver Detection
Automotive safety
E-commerce Product Insights
Online retail
Retail Demand Forecasting
Supply chain
Customer Attrition Prediction
Telecom & finance
Movie Recommendation Engine
Media & entertainment
Crop Yield Prediction
Agriculture
07
Real Scenarios
Case Studies From the Field
Solve real modelling and deployment problems the way our graduates do on the job.
Retrieval-Augmented Support Assistant
Objective: Answer customer questions from a company knowledge base without hallucinating.
Case Study: A support team wants an assistant grounded in its own documentation. The build covers chunking and embedding the corpus, retrieval, prompt design, and guardrails for out-of-scope questions.
Key Tools:
- Embeddings and a vector database
- Retrieval-Augmented Generation (RAG)
- Prompt engineering and role-based prompting
- LLM APIs with function calling
- Responsible AI review — bias, privacy, scope limits
Outcome: A grounded assistant that cites its source passages, declines out-of-scope questions, and is documented against Responsible AI criteria.
08
Verified Partnership
In Collaboration with Microsoft
Learn through a program shaped by Microsoft-certified experts, backed by resources that follow global tech standards — giving your analytics and artificial intelligence career a trusted and competitive edge.
The Certification You'll Earn
Two credentials that document what you actually built — issued in collaboration with Microsoft, digitally verifiable, and shareable directly on LinkedIn.
Data Science & AI Expert Certificate
Awarded on completion of all 10 modules — Python and SQL through machine learning, deep learning, MLOps and Generative AI.
Data Science & AI Expert certificate
Project Internship Certificate
Documents your 10 hands-on projects and the domain capstone you completed under mentor review — evidence, not just attendance.
Project Internship certificate
Awards & Recognition — Training Excellence
Recognised for Excellence, Powered by Impact
From industry accolades to real learner success stories, every award reflects our mission — training that truly makes a difference.
We're proud to be recognised for the impact we create, both in education and in careers.
09
Why Now
Why Enrol Now — Industry Trends
Data & AI roles remain among the fastest-growing job families
Demand keeps outpacing supply across analytics, ML and AI engineering — which is exactly why a portfolio plus certification moves the needle now.
NASSCOM
India's data & AI talent gap is widening
Employers report unfilled analytics and machine learning positions year after year, with the sharpest shortage in candidates who can also deploy.
Industry Hiring Data
Deployment skills separate shortlists from offers
Candidates who can version, containerise and serve a model — not just train one in a notebook — consistently clear more interview rounds.
Refer & Earn Up to ₹20,000
Know someone ready to move into analytics and AI?
₹20,000
Refer a friend to PaperLive and earn for every successful enrolment.
Data Science Online Course Reviews on LinkedIn
Verified reviews from graduates on LinkedIn — real professionals, real career outcomes.
Anil Shrivastava
Hewlett Packard Enterprise
Really Changed My Career!
I moved from reporting into a data science role within months. The statistics and ML modules were taught with real datasets, not toy examples.
Dilip Mehta
PhonePe
Absolutely Game-Changing!
The MLOps module was the differentiator in my interviews — being able to talk about MLflow, Docker and FastAPI serving set me apart.
Suraj Sharma
Concentrix
Amazing Learning Experience!
The mentor support was exceptional. I now build and evaluate models in production at work with confidence.
Neha Kulkarni
Wipro
From QA to Data Analyst
SQL and Excel in the first two months got me interview-ready fast, and the Pandas work took me the rest of the way.
Rahul Verma
Tech Mahindra
Worth Every Rupee
Live classes, real notebooks and a capstone with mentor review — far ahead of any self-paced course I tried before.
Sneha Iyer
Infosys
Landed a 16 LPA Offer
The Generative AI and RAG module came up in every single interview I attended. Highly recommend for career switchers.
Amit Patel
Capgemini
Best Mentors in the Industry
Doubt-clearing sessions kept me on track even with a full-time job. The deep learning module was surprisingly approachable.
Pooja Nair
Cognizant
Career Transformation
I went from support engineer to ML Engineer. The XGBoost and model-tuning sessions were incredibly detailed.
Vikram Singh
Oracle
Hands-On and Practical
Every concept was backed by a notebook. My churn predictor and CNN classifier are both in my portfolio now.
Divya Menon
Accenture
Highly Structured Program
The 10-module roadmap is well-paced. Resume and LinkedIn support helped me get shortlisted quickly.
Karthik Reddy
IBM
Great ROI on Learning
The agentic AI and function-calling sessions gave me an edge. Secured a role with a 45% hike within weeks.
Ananya Ghosh
Deloitte
Supportive Community
The peer community kept me motivated through the capstone. The placement team is genuinely committed.
Rohan Desai
HCLTech
Cleared Interviews Confidently
Mock interviews mirrored real hiring rounds — SQL, case study and model design, all covered.
Meera Joshi
LTIMindtree
Loved the Capstone
I picked retail demand forecasting and it became the centrepiece of every interview conversation.
Sandeep Kumar
Mphasis
From Fresher to Hired
As a fresher, the guaranteed opportunity policy gave me confidence. I now work as a Data Analyst.
Everything You Need to Go From Learner to Hired
This isn't just another online course. It's a complete, job-first program engineered to get you certified, skilled, and placed — with the mentorship and hiring network to back it up.
Demo class included · No payment to apply · Talk to an advisor in 30 mins
Earn an industry-recognized data science certification
150+ instructor-led hours across 10 structured modules
Build a portfolio of 10 hands-on projects plus a domain capstone
Master MLOps and Generative AI — not just notebooks
1-on-1 resume, LinkedIn & mock-interview prep
100% Job Opportunity Promise — or your career fee is refunded
FAQs — Fees, Syllabus, Classes & Certification
Straight answers on fees, syllabus, batch timings, placement assistance and career support.
Data science course fees start from ₹66,795 + GST for the full 150+ hour program. No-cost EMI is available from ₹8,888/month through Bajaj Finserv, ShopSe and Northern Arc, so the data scientist course fees can be spread across the 6-month duration.
Yes. PaperLive periodically offers early-bird pricing and merit-based scholarships that reduce the data scientist course fees by up to 35%. Reach out to the admissions team to check current offers before your batch closes.
PaperLive backs this program with a 100% Job Opportunity Guarantee — if you complete it and don't secure an opportunity, your career fee is fully refunded.
It is a full AI and data science course. Alongside Python, SQL and statistics, roughly half the 150+ hours go to machine learning, deep learning, computer vision, MLOps and Generative AI — so you leave able to build and deploy models, not just report on data.
Yes. Every session is live and fully online, so learners from Bengaluru to Guwahati attend the same batch. All you need is a laptop with 8GB RAM and a stable connection — lab work runs in Google Colab, and recordings are posted within 24 hours.
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