25%
Market Growth (2020–2030)
AI adoption accelerating
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Become industry-ready with Statistics, Supervised & Unsupervised ML, Feature Engineering, Model Evaluation, and Deployments. Build portfolio projects and earn a QR-verified certificate.
Curriculum includes Python (Pandas/NumPy), EDA & Data Visualization, Scikit-learn, XGBoost, basic Deep Learning, ML pipelines, MLOps fundamentals, and CI/CD best practices.
Data Science fuels products, operations, and strategy. Build Python/SQL/ML skills with mentor-led projects and recruiter-ready outcomes.
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Market Growth (2020–2030)
Job Vacancies in India
Average Fresher Salary
Job Satisfaction
India’s Global Market Share
Master dashboards, experimentation, and ML pipelines—target roles like Data Analyst, Data Scientist, and ML Engineer.
*Figures are indicative and vary by location, skills, and industry.
Go end-to-end from data processing to production deployment. Build a job-ready portfolio in Python, scikit-learn, TensorFlow and cloud MLOps with measurable impact.
Build reliable pipelines with pandas, Polars & SQL. Encode, scale, and select features with reproducible notebooks.
Model selection with scikit-learn, cross-validation, hyper-parameter tuning, and leakage-free evaluation.
Neural networks, CNNs/RNNs, transfer learning and modern tooling with TensorFlow/Keras & PyTorch basics.
Package models with FastAPI, containerize with Docker, add CI/CD, and deploy to cloud with monitoring.
Track performance, data drift and cost; add alerts and retraining triggers for production reliability.
Bias checks, documentation, and governance so models are ethical, explainable, and audit-ready.
From EDA & feature engineering to model development and MLOps, this masterclass focuses on deployable skills. You’ll ship APIs, dashboards, and reproducible experiments that translate to interviews and on-the-job success.
Keywords: data science training, machine learning course, deep learning with TensorFlow, feature engineering, model deployment, MLOps pipeline, ML monitoring, cloud AI solutions, Python data analysis.
An industry-aligned pathway from core data science to deep learning, big data, cloud deployments, and MLOps — ending with a portfolio-ready capstone.
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Master data wrangling with Pandas, exploratory data analysis, visualization (Matplotlib/Seaborn), and basics of ML.
Build and optimize models using regularization, feature engineering, ensembles (RF, GBM, XGBoost), and hyperparameter tuning.
Train CNNs/RNNs/Transformers fundamentals for images and text; use Keras/PyTorch with callbacks, checkpoints, and metrics.
Tokenization, embeddings, classical NLP, sequence models, ARIMA/Prophet/ETS, feature lags/rolling stats, and forecasting.
Parallel data processing with Spark, data lakes/warehouses, and deployments to AWS/GCP (API, serverless, containers).
CI/CD for ML, model packaging, tracking, evaluation, monitoring; ship a portfolio-grade capstone with docs & demo.
*Module order may vary slightly by cohort and instructor discretion to maximize learning outcomes.
Apply advanced techniques to solve real business challenges. Build a recruiter-ready portfolio with clean code, clear storytelling, and measurable impact.
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Forecast equipment failures from sensor streams to reduce downtime and maintenance cost.
Mine customer feedback to quantify sentiment and surface actionable themes at scale.
Build robust time-series forecasts for equities with backtesting and risk-aware metrics.
Predict churn and prioritize campaigns using uplift models for incremental impact.
Detect low-quality or defective product images to automate content standards.
Deliver personalized recommendations with evaluation beyond accuracy (coverage, novelty).
These industry-aligned projects emphasize reproducible pipelines, evaluation, and clear communication—ideal for Data Scientist, ML Engineer, and Analytics roles.
*Scope may vary by dataset, domain, and pace.
Authentic reviews from our Advanced Data Science & Machine Learning cohort—highlighting MLOps, model deployment, drift monitoring, and job outcomes.
“This masterclass is the best for advancing in DS & ML. The evaluation mindset and reproducible pipelines helped me ship confidently.”
“Projects were challenging and rewarding—end-to-end ML with MLflow, feature stores, and clear READMEs made my portfolio stand out.”
“The 200-hour program was worth it. Deployed a FastAPI model on cloud with monitoring and got offers quickly.”
“Strong focus on MLOps, drift detection, and cost control. Exactly what hiring managers asked about.”
“Clear coverage of classical ML to deep learning—solid baselines, fair comparisons, and great storytelling in dashboards.”
“Mock interviews + DSA warm-ups + system design primer gave me confidence. I recommend it for serious career switchers.”
“This masterclass is the best for advancing in DS & ML. The evaluation mindset and reproducible pipelines helped me ship confidently.”
“Projects were challenging and rewarding—end-to-end ML with MLflow, feature stores, and clear READMEs made my portfolio stand out.”
“The 200-hour program was worth it. Deployed a FastAPI model on cloud with monitoring and got offers quickly.”
“Strong focus on MLOps, drift detection, and cost control. Exactly what hiring managers asked about.”
“Clear coverage of classical ML to deep learning—solid baselines, fair comparisons, and great storytelling in dashboards.”
“Mock interviews + DSA warm-ups + system design primer gave me confidence. I recommend it for serious career switchers.”
Read independent reviews of our Data Science and ML masterclass. Alumni highlight MLOps, deployment, monitoring, and job placements.
High-growth careers across data science, machine learning engineering, analytics, and AI product innovation.
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*Logos are illustrative of hiring potential. Openings vary by location, skills, and experience.
Whether you’re starting out or leveling up, this masterclass maps directly to job-ready skills in Data Science and Machine Learning.
Build industry-ready DS & ML skills and create a standout portfolio for high-growth roles.
Upskill to lead AI/ML projects, automate workflows, and accelerate career growth.
Transition to Data Scientist roles with modeling, MLOps, and experiment design.
Break into the booming AI industry with guided projects and interview prep.
This course is ideal for students, fresh graduates, working professionals, data analysts, and career switchers who want to learn data science and machine learning.
The industry-trusted stack for Data Science, Machine Learning, and Data Engineering. Learn by building, shipping, and iterating.
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Scripting, data wrangling, and model glue.
CoreND arrays, vectorized compute, linear algebra.
CoreTabular analytics, joins, groupbys, I/O.
DataWarehouse queries & data modeling.
DataDistributed compute for large datasets.
DataOrchestrate reliable ETL/ELT pipelines.
DataClassical ML, metrics, pipelines.
MLDeep learning & fine-tuning.
MLDL production & TF-Serving.
MLVisual analytics & reports.
DataS3 • EC2 • Lambda • SageMaker.
CloudVersioning, CI/CD, and deploy.
CloudFollow these four proven steps to go from learner to job-ready Data Science professional with a portfolio recruiters trust.
Statistics, EDA, feature engineering, supervised/unsupervised ML, and real-world case studies to build strong foundations.
End-to-end projects with clean notebooks, APIs, dashboards, and cloud demos. Document with READMEs, evals, and reports.
ATS-optimized resume, analytics storytelling, system design for ML, model packaging, tracking, and deployment checklists.
Target roles like Data Scientist, ML Engineer, Applied AI Engineer, or Analytics Specialist (₹9–18 LPA based on role & city).
Learn from anywhere. Your journey to a Data Science career starts here.
Everything you need to know about our Advanced Data Science & Machine Learning Masterclass—admissions, curriculum, schedule, portfolio, and placement support.
Basic Python is helpful but not mandatory. We start with foundations and ramp up to ML, DL, and deployment with guided, hands-on projects.
The masterclass runs for ~200 hours, including live sessions, labs, capstone projects, and interview preparation.
Yes. You’ll get resume revamps, ATS keyword mapping, mock interviews, portfolio reviews, and targeted referrals through our network.
Python, pandas/Polars, scikit-learn, TensorFlow/Keras, MLflow/DVC, FastAPI, Docker, and cloud patterns on AWS/GCP/Azure.
Absolutely. Each module ends with a deployable artifact—APIs, dashboards, notebooks, and experiment reports—to showcase in interviews.
Yes. We offer flexible schedules, mentor support, and recorded sessions so you can learn at your pace without missing milestones.
Find answers about prerequisites, duration, job assistance, tools covered, portfolio outcomes, and flexible schedules for the Data Science & Machine Learning program.
Enroll today and get 100% job assistance, mentor-led projects, and a verifiable global certificate.
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