CommandStack
Training: From Zero→Hero
Advanced Track · ML + MLOps

7‑Week AI Development Bootcamp

Capstone: Stock Price Movement Predictor — build an end‑to‑end platform (data pipeline → features → ML & DL models → backtesting → FastAPI + Streamlit deployment) for 15+ blue‑chip tickers.

Apply Now See Curriculum
Cadence4 days/week4 hrs/day · 112 hrs total
ModelsLR · RF · XGB · LSTMCompare & select
DeliveryFastAPI + StreamlitDocker · CI/CD
OutcomesBacktests & ReportsPortfolio‑ready

What you’ll master

Python 3.11 pandas / NumPy scikit‑learn XGBoost TensorFlow/Keras (LSTM/CNN) yfinance / Alpha Vantage TA indicators (RSI/MACD/SMA) Optuna / GridSearch Backtesting (sliding window) FastAPI Streamlit Docker GitHub Actions OpenAPI Metrics: ROC/F1/Sharpe Secrets & API keys

Predict UP/DOWN for next day; extend to any ticker; compare classical ML with DL; ship to the cloud.

Who is this for?

Developers and analysts who want to build production‑style ML systems, not just notebooks. Expect strict pace and code reviews.

  • Comfortable with Python and basic statistics
  • Some pandas/NumPy familiarity
  • Ready to deploy APIs and dashboards

Outcomes

  • Automated data pipeline for OHLCV
  • Feature engineering (TA indicators)
  • ML models: Logistic, RF, XGBoost
  • DL models: LSTM, 1D‑CNN
  • Backtesting with sliding windows
  • FastAPI /predict + Streamlit UI
  • CI/CD with secrets management

Curriculum at a Glance (7 Weeks)

Week 1 — Kickoff & Data Foundations

  • Data sources (Yahoo/Alpha Vantage)
  • Automate downloads with yfinance
  • Cleaning, normalization & EDA visuals

Week 2 — Feature Engineering

  • Indicators: SMA/EMA/RSI/MACD/Bollinger
  • Build feature matrix & labels (1=up, 0=down)
  • Reusable scikit pipelines + unit tests

Week 3 — Classical ML Models

  • Logistic Regression baseline
  • Random Forest & XGBoost
  • Compare models; strengths/weaknesses

Week 4 — Deep Learning for Stocks

  • LSTM sequence model
  • 1D‑CNN alternative
  • Evaluation: ROC, F1, confusion matrix

Week 5 — Optimization & Backtesting

  • Hyperparameter tuning (Optuna/Grid)
  • Sliding‑window validation & Sharpe
  • Report: win rate & risk metrics

Week 6 — Deployment & Dashboard

  • FastAPI /predict with OpenAPI docs
  • Streamlit dashboard (multi‑ticker)
  • CI/CD: Docker + GitHub Actions; secure keys

Week 7 — Capstone Defense

  • Full demo: select stock → fetch → predict
  • Validation & backtest review
  • Architecture diagrams & README

Capstone — Stock Movement Predictor

Predict whether selected stocks will go UP or DOWN the next day, starting with 15 blue‑chip tickers and extendable to any symbol.

Feature Pipeline ML vs DL Benchmark Backtesting FastAPI + Streamlit Docker CI/CD

Daily Flow (4 hrs)

  • Kick‑off & goal (15m)
  • Concepts + live demo (90m)
  • Break (15m)
  • Guided lab/project (90m)
  • Debrief & verification (30m)

Everything built live — no homework required.

Upcoming Cohort

Duration: 7 weeks · Schedule: 4 days/week × 4 hrs/day (Evenings, America/Chicago)
Exact start date/time will be finalized with the batch.

Format: Live online, recordings provided. Strict daily labs. Weekly milestones.

Tuition

$ 300per seat

Early‑bird and group pricing available. Limited seats to maintain quality.

Speak to admissions for EMI plans & scholarships.

Admissions & Placement Support

  • Pre‑work + baseline assessment
  • 1:1 resume + LinkedIn revamp
  • Interview prep & mock panels
  • Portfolio review and live demo coaching

Ready to build AI?

Apply now to reserve your seat. Include your background and goals—expect a short call to confirm fit.

Apply Now Talk to Admissions

Company team? Ask about a private cohort tailored to your data stack.