Telicon Academy™ · 20 weeks · 80 lessons
AI/ML Modeling Bootcamp
A 20-week, cohort-based, project-intensive bootcamp in applied AI and machine learning: Python, the maths behind models, classical ML, deep learning, NLP, LLMs, MLOps and responsible AI, finished with a deployed capstone defended before an expert panel.
Telicon Academy™ programs are vendor-aligned where relevant but are not official SAP, Oracle, Microsoft or AWS certifications and do not imply vendor endorsement unless separately authorized.
Who it's for
Working professionals and career changers who want to build, evaluate and deploy AI/ML models — analysts, engineers, scientists and technical product people.
Prerequisites
- High-school algebra and comfort with numbers
- Some programming exposure (any language) is helpful
- About 15–20 hours per week for 20 weeks
- A laptop that can run Python and Jupyter
You'll be able to
- Frame AI/ML problems with clear success criteria
- Prepare data and engineer features without leakage
- Train, tune and compare classical and deep learning models
- Deploy a model behind an API with monitoring and a model card
- Assess fairness, explainability, drift and model risk
20-week curriculum
Week 1
AI/ML Foundations & Problem Framing
AI vs ML vs deep learning · Supervised, unsupervised and reinforcement learning · Framing use cases and success criteria · Responsible AI and data ethics
Week 2
Python for Machine Learning
Python essentials for modeling · NumPy arrays and vectorization · pandas for modeling data · Reproducible notebooks and environments
Week 3
Math for Machine Learning I
Vectors, matrices and linear algebra · Functions and optimization intuition · Gradients and gradient descent · Numerical stability basics
Week 4
Math for Machine Learning II
Probability for ML · Common distributions · Descriptive and inferential statistics · Bayesian intuition
Week 5
Data Preparation & Feature Engineering
Data quality assessment · Handling missing values · Encoding categorical data · Scaling, transformations and feature construction
Week 6
Exploratory Data Analysis & Visualization
The EDA workflow · Distributions and relationships · Visualization for modeling · Detecting leakage and bias
Week 7
Regression Modeling
Linear regression · Regularization: ridge and lasso · Regression assumptions · Regression metrics and diagnostics
Week 8
Classification Modeling
Logistic regression · Decision boundaries · Handling class imbalance · Classification metrics
Week 9
Trees & Ensemble Methods
Decision trees · Random forests · Gradient boosting · Feature importance and tuning
Week 10
Midpoint Applied Modeling Project
Midpoint: problem framing and data plan · Midpoint: cleaning and feature engineering · Midpoint: model comparison and evaluation · Midpoint: executive presentation
Week 11
Unsupervised Learning
Clustering with k-means and beyond · Dimensionality reduction · Principal component analysis · Segmentation and anomaly detection
Week 12
Time Series & Forecasting
Working with temporal data · Trend and seasonality · Feature engineering for time series · Baseline and ML forecasting models
Week 13
Neural Networks & Deep Learning Foundations
Perceptrons and multilayer networks · Activation and loss functions · Backpropagation intuition · Training dynamics
Week 14
Computer Vision Modeling
Image data pipelines · CNN fundamentals · Transfer learning · Image classification and evaluation
Week 15
Natural Language Processing
Text preprocessing · Vectorization and embeddings · Sequence modeling overview · Text classification and evaluation
Week 16
Transformers, LLMs & Generative AI
Transformer architecture concepts · Prompting and retrieval patterns · Fine-tuning concepts · Evaluating generative systems safely
Week 17
Model Optimization & Experimentation
Hyperparameter tuning · Cross-validation strategies · Experiment tracking and reproducibility · Model selection
Week 18
MLOps & Model Deployment
Packaging models · Serving models through APIs · Containers and CI/CD concepts · Monitoring and rollback
Week 19
Model Risk, Governance & Responsible AI
Fairness assessment · Explainability methods · Drift, privacy and validation · Model cards and governance
Week 20
Capstone Defense & Portfolio Launch
Capstone completion sprint · Technical documentation · Deployment demo and panel defense · Portfolio and career packaging
Projects
Week 5 · Applied project
Data Quality & Feature Engineering Audit
Audit a messy dataset, fix quality issues and build a leakage-free feature pipeline.
Week 7 · Applied project
Regression Modeling Business Case
Predict a business quantity and explain drivers, error and value.
Week 8 · Applied project
Classification / Risk Scoring Model
Build and calibrate a risk score with imbalance handling and threshold choice.
Week 10 · Mid-program project
Midpoint Applied Modeling Project
End-to-end tabular ML engagement: frame, clean, engineer, compare, evaluate, explain and present.
Week 11 · Applied project
Customer or Operational Segmentation
Segment customers or operations and turn clusters into actions.
Week 12 · Applied project
Time-Series Forecasting Project
Forecast demand against baselines and communicate uncertainty.
Week 15 · Applied project
NLP or Computer Vision Applied Model
Train and evaluate a text or image model using transfer learning.
Week 20 · Capstone · expert panel
Deployed AI/ML Capstone
Design, build, evaluate, document and deploy an AI/ML solution, then defend it before an expert panel.
Admissions readiness check
- Quantitative reasoning
- Basic statistics
- Logic and analytical reasoning
- Data interpretation
- Programming aptitude
- Problem solving
- Learning readiness
Advisory only; staff make the final decision.
Trainer expectations
Hands-on Python, statistics and ML with scikit-learn, notebook and data workflows, a deep learning framework, model evaluation, deployment/MLOps and responsible AI, backed by a real project portfolio. Industry certifications are optional; experience may substitute.
Career directions
- Junior ML Analyst
- Applied AI Developer
- Data Science Associate
- ML Engineering Associate
Not a job guarantee.
Learning and tools
Guided lessons, applied practice and a portfolio of work aligned to this pathway.
Tools and technologies
- Python
- Jupyter notebooks
- pandas and NumPy
- scikit-learn
- matplotlib plotting
- Git and GitHub workflow
- One deep learning framework (e.g. PyTorch)
- API serving (e.g. FastAPI)
- Containers (e.g. Docker)
- Experiment tracking (e.g. MLflow)
- Cloud and deployment concepts
What you'll build
- Six portfolio projects on realistic, made-up datasets
- An end-to-end tabular ML engagement at Week 10
- A deployed capstone with API/demo, monitoring plan and model card
- A public-ready portfolio and technical report
Expert review and certificate
The capstone is reviewed by an assigned expert panel. Panel assignments depend on verified reviewer availability; no staff credentials are implied by a seat description.
Panel perspectives
- ML / Data Science Technical Reviewer
- Industry AI Practitioner
- MLOps / Platform Reviewer
- Responsible AI / Model Risk Reviewer
- Business / Product Reviewer
Completion requirements
- All required lessons complete
- Weekly labs submitted and passed
- Quizzes passed
- Six portfolio projects completed
- Week 10 midpoint project passed
- Final written/technical assessment passed
- Capstone submitted
- Expert panel approval (at least 3 independent reviews), including any required revisions
Trainer roles
- Lead AI/ML Instructor
- Applied Machine Learning / Data Science Instructor
- MLOps / Deployment Instructor
- Teaching Assistant / Learner Success Coach
- Guest AI Practitioner (optional)
Start dates, financial options and admission decisions are confirmed by staff. A certificate does not guarantee employment.