Smart Learning
Deeper & More
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• SQL100: How data lives in tables, why databases beat raw files, and how keys link info.
• SQL200: Filter and sort rows; use simple rules and labels to clean results.
• SQL300: Subqueries to answer deeper questions; summarize correctly with groups.
• SQL400: Combine tables the right way (inner/left/full); avoid duplicate-row surprises.
• SQL500: Make queries faster with indexes/partitions; read an EXPLAIN like a pro.
• SQL600: Create/update tables safely; Spark tables, Delta Lake, and lakehouses.

• PY100: Write and run simple programs; basic types and input/output.
• PY200: Tables in Python (Pandas); select, filter, and summarize like SQL.
• PY300: If/else, loops; clean short code with list/dict tricks.
• PY400: Clear charts that tell a story; compare groups and trends.
• PY500: Functions; use small helpers and handle errors nicely.
• PY600: Read/write files, parse JSON, call web APIs, scrape pages politely.
• PY700: Think in objects (reusable parts) to keep code organized.
• PY800: Connect Python to databases, run queries, save results safely.
• PY900: Feature engineering with Pandas; windows, categories; intro to PySpark.
• PY1000: Build a simple regression and check if the story makes sense.

• STAT100: Averages and spread; spot skew and tell the shape of data.
• STAT200: Probability, common patterns; compare groups fairly.
• STAT300: Hypothesis tests, p-values, CLT, and independence checks.
• STAT400: Correlation vs causation; simple regression and common misreads.
• STAT500: p-values in context; time-series basics; panel/mixed models overview.
• STAT600: ANOVA, non-parametric tests, and experiment planning.

• EXL100: Pivot tables, lookups, and charts that highlight the "so what."
• BA100: What a Business Analyst does; funnels, metrics, anomalies, A/B basics.
• BA200: Customer journeys, LTV estimation, marketing terms that matter.
• BA300: North-Star metrics, multivariate tests, avoiding peeking mistakes.
• BA400: Forecast run-rate, simple time-series, spotting unusual behavior.

• DS100: What a data scientist does day-to-day; how teams work together.
• DS200: Clean repos, environments, and short sprints with clarity.
• MLOPS101: Pipelines that move data reliably; quick UIs to show results.
• MLOPS200: Cloud basics for ML (compute, storage, IAM) and cost awareness.
• MLOPS300: Package models in Docker, serve via FastAPI, add health checks.
• MLOPS400: Track experiments (MLflow), promote versions, keep history tidy.

• MLM100: Gentle intro to functions, gradients, and why models climb to better answers.
• MLM200: Iterative methods, cost functions, and smarter gradient steps.
• MLM300: Matrices and vectors explained; distances and rotations with pictures.
• MLM400: Math behind decision trees (entropy, splits) and friends.

• ML100: Regularization and gradient descent in practice.
• ML200: Split data right; fair metrics and class imbalance.
• ML300: Clustering without labels — K-Means, DBSCAN; cluster quality.
• ML400: Dimensionality reduction (PCA); fix linear regression issues.
• ML500: Logistic Regression and Decision Trees for classification.
• ML600: Boosting and bagging; Random Forests without math overload.
• ML700: SVM, k-NN; explain results with SHAP/LIME.
• ML800: Ship a mini ML app with Flask/Streamlit + monitoring.
• ML900: Hyperparameter tuning and cross-validation.
• ML1000: Capstone: end-to-end churn project with demo and docs.

• DL100: Neural networks: layers, activations, and training basics.
• DL200: Backpropagation and how to keep learning stable.
• DL300: Right loss and optimizer; avoiding over/under-fitting.
• DL400: Train with Keras/TensorFlow/PyTorch; save checkpoints like a pro.
• DL500: CNNs for images: convolution explained and regularization.
• DL600: Transfer learning: fine-tune pretrained models safely.
• DL700: Sequences: LSTM/GRU for text and time-series.
• DL800: Autoencoders & embeddings; image/text mini-project.
• DL900: Modern CNNs (ResNet/Inception) and model explainability.
• DL1000: Capstone: end-to-end deep learning project with live demo.

• AI100: Image processing with OpenCV; intro to multimodal ideas.
• AI200: Object detection and classification; accuracy metrics.
• AI300: Text understanding (intent/NER) and text-to-speech.
• AI400: Embeddings and BERT in simple words; fine-tune a small model.
• AI500: Sentiment analysis project from baseline to modern models.
• AI600: Hugging Face & open LLMs; lightweight fine-tuning and serving.
• AI700: RAG: search + AI answers that show sources and stay grounded.
• AI800: Agentic AI with code: tools, memory, teamwork, guardrails.
• AI900: Agentic AI without code: n8n/Zapier/UiPath flows with approvals.
• AI1000: Grand capstone: finalize, evaluate, document, and present.

• SS100: Communicate clearly, present work well, craft impact-first resumes.
• SS200: Plan capstones, form teams, set milestones, get CI running.
• SS300: Mock interview for Business Analyst: SQL/Excel/stats/product.
• SS400: Mock interview for Data Scientist: coding, ML/stats, SQL, design.
• SS500: Mock interview for ML/AI Engineer: coding, RAG/serving, MLOps.
• SS600: Interview prep + salary negotiations with scripts and examples.
• SS700: Buffer session: Q&A, doubt-clearing, recap, readiness plans.
• SS800: Cohort wrap-up, community, next steps, graduation checklist.

If you're interested in AI/ML and have basic math skills; or you're looking to gain practical stock market knowledge, these programs are designed for you. Our structured approach guides you step-by-step to build real-world competency.

We focus on building true job readiness: practical skills, hands-on projects, resume guidance, and interview preparation. We position you for growth without making false employment guarantees.

The AI-Ready Engineer program is currently priced at ₹29,999 (50% off ₹60,000). The Stock Market course is ₹10,000. Flexible payment options may be available upon discussion with our advisory team.

Basic programming familiarity helps but is not mandatory. We start with foundational Python modules so dedicated learners can keep up and succeed.

The Stock Market program is open to everyone! No prior finance degree or technical background is required.

You receive full access throughout the cohort duration plus extended access to session recordings and materials to reinforce your learning.

Absolutely! All projects built during the program can be showcased on your resume, LinkedIn, and GitHub portfolio.