Tibixa
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Intermediate · free

Applied AI for Developers and IT Professionals

From Python basics to production LLM apps, with an optional GeoAI specialization.

For working developers and IT professionals (software, web, database, sysadmin, support and data analyst roles) with intermediate programming or IT experience and no prior machine learning or advanced math background.

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Foundations for AI

Start here

1 lesson
  1. Welcome to the course: what you will learn Welcome 6 min

Module 1: Python for AI

8 lessons · 2 available
  1. Track overview: Foundations for AI Introduction 5 min
  2. Setting up your AI workstation (conda, VS Code, Jupyter) Lesson 1 9 min quiz 3 exercises
  3. Python refresher for data work Lesson 2 coming soon
  4. NumPy: arrays, broadcasting, vectorization Lesson 3 coming soon
  5. Pandas: loading, cleaning, joining tabular data Lesson 4 coming soon
  6. Working with real data: CSV, JSON, Excel, SQL and web APIs Lesson 5 coming soon
  7. Visualization with Matplotlib and Plotly Lesson 6 coming soon
  8. Project: exploring an online store's sales dataset Lesson 7 coming soon

Module 2: Math Essentials for ML

6 lessons · coming soon
  1. Vectors and matrices: the language of data Lesson 1 coming soon
  2. Dot products, norms and distance Lesson 2 coming soon
  3. Derivatives and gradients, intuitively Lesson 3 coming soon
  4. Probability basics: distributions, expectation Lesson 4 coming soon
  5. Bayes' rule and conditional probability Lesson 5 coming soon
  6. Statistics you actually need: mean, variance, correlation, p-values Lesson 6 coming soon

Module 3: Data Handling and Preparation

6 lessons · coming soon
  1. Data types, missing values and outliers Lesson 1 coming soon
  2. Feature scaling and encoding Lesson 2 coming soon
  3. Train, validation and test splits Lesson 3 coming soon
  4. Data leakage and other silent pitfalls Lesson 4 coming soon
  5. Building a reproducible data pipeline Lesson 5 coming soon
  6. Project: preparing a customer churn dataset for modelling Lesson 6 coming soon

Classical Machine Learning

Module 4: Supervised Learning

8 lessons · coming soon
  1. Track overview: Classical Machine Learning Introduction coming soon
  2. What is machine learning: a working definition Lesson 1 coming soon
  3. Linear regression from scratch Lesson 2 coming soon
  4. Logistic regression and classification Lesson 3 coming soon
  5. Decision trees and random forests Lesson 4 coming soon
  6. Gradient boosting (XGBoost, LightGBM) Lesson 5 coming soon
  7. K-nearest neighbours and support vector machines Lesson 6 coming soon
  8. Project: predicting house prices Lesson 7 coming soon

Module 5: Model Evaluation and Tuning

6 lessons · coming soon
  1. Overfitting, underfitting and bias-variance Lesson 1 coming soon
  2. Metrics: accuracy, precision, recall, F1, ROC-AUC Lesson 2 coming soon
  3. Regression metrics: MAE, RMSE, R2 Lesson 3 coming soon
  4. Cross-validation: k-fold, stratified and time-based splits Lesson 4 coming soon
  5. Hyperparameter tuning: grid, random, Bayesian Lesson 5 coming soon
  6. Explaining models: feature importance and SHAP Lesson 6 coming soon

Module 6: Unsupervised Learning

5 lessons · coming soon
  1. Clustering: K-means and DBSCAN Lesson 1 coming soon
  2. Hierarchical clustering Lesson 2 coming soon
  3. Dimensionality reduction: PCA, t-SNE, UMAP Lesson 3 coming soon
  4. Anomaly detection Lesson 4 coming soon
  5. Project: customer segmentation from purchase behaviour Lesson 5 coming soon

Deep Learning

Module 7: Neural Network Fundamentals

8 lessons · coming soon
  1. Track overview: Deep Learning Introduction coming soon
  2. From linear models to neurons Lesson 1 coming soon
  3. Activation functions and layers Lesson 2 coming soon
  4. Loss functions and gradient descent Lesson 3 coming soon
  5. Backpropagation explained Lesson 4 coming soon
  6. PyTorch basics: tensors, autograd, modules Lesson 5 coming soon
  7. Training loop, batches, epochs Lesson 6 coming soon
  8. Project: your first neural network on tabular data Lesson 7 coming soon

Module 8: Computer Vision with CNNs

7 lessons · coming soon
  1. Convolutions and pooling Lesson 1 coming soon
  2. Building a CNN in PyTorch Lesson 2 coming soon
  3. Transfer learning with pretrained backbones Lesson 3 coming soon
  4. Data augmentation Lesson 4 coming soon
  5. Object detection: YOLO and Faster R-CNN Lesson 5 coming soon
  6. Semantic segmentation: U-Net Lesson 6 coming soon
  7. Project: classifying product photos with transfer learning Lesson 7 coming soon

Module 9: Sequences and Training at Scale

6 lessons · coming soon
  1. RNNs, LSTMs and where they still matter Lesson 1 coming soon
  2. Time series forecasting with deep learning Lesson 2 coming soon
  3. Regularization: dropout, batch norm, weight decay Lesson 3 coming soon
  4. Learning rate schedules and optimizers Lesson 4 coming soon
  5. GPU training, mixed precision, checkpoints Lesson 5 coming soon
  6. Debugging a training run Lesson 6 coming soon

LLMs and Generative AI

Module 10: Transformers and LLM Foundations

7 lessons · coming soon
  1. Track overview: LLMs and Generative AI Introduction coming soon
  2. Tokens, embeddings and attention Lesson 1 coming soon
  3. The transformer architecture Lesson 2 coming soon
  4. How LLMs are trained: pretraining, SFT, RLHF Lesson 3 coming soon
  5. Model landscape: open vs closed, sizes, context windows Lesson 4 coming soon
  6. Calling an LLM API: parameters that matter Lesson 5 coming soon
  7. Prompt engineering patterns for developers Lesson 6 coming soon

Module 11: Retrieval-Augmented Generation

6 lessons · coming soon
  1. Embeddings and vector similarity Lesson 1 coming soon
  2. Vector databases: pgvector, Chroma, FAISS Lesson 2 coming soon
  3. Chunking strategies Lesson 3 coming soon
  4. Building a RAG pipeline end to end Lesson 4 coming soon
  5. Evaluating RAG: faithfulness, relevance Lesson 5 coming soon
  6. Project: chat with your company's internal documentation Lesson 6 coming soon

Module 12: Agents and Tool Use

6 lessons · coming soon
  1. Function calling and structured outputs Lesson 1 coming soon
  2. Agent loops: plan, act, observe Lesson 2 coming soon
  3. Model Context Protocol (MCP) Lesson 3 coming soon
  4. Multi-agent patterns and when to avoid them Lesson 4 coming soon
  5. Guardrails, safety and cost control Lesson 5 coming soon
  6. Project: an IT helpdesk agent that queries a database and a REST API Lesson 6 coming soon

Module 13: Fine-tuning and Custom Models

5 lessons · coming soon
  1. When to fine-tune vs prompt vs RAG Lesson 1 coming soon
  2. LoRA and parameter-efficient fine-tuning Lesson 2 coming soon
  3. Preparing a fine-tuning dataset Lesson 3 coming soon
  4. Running a fine-tune with Hugging Face Lesson 4 coming soon
  5. Evaluating and deploying a fine-tuned model Lesson 5 coming soon

MLOps and AI Engineering

Module 14: From Notebook to Service

7 lessons · coming soon
  1. Track overview: MLOps and AI Engineering Introduction coming soon
  2. Project structure and packaging Lesson 1 coming soon
  3. Serving a model with FastAPI Lesson 2 coming soon
  4. Containerizing with Docker Lesson 3 coming soon
  5. Model registries and versioning (MLflow) Lesson 4 coming soon
  6. CI/CD for ML projects Lesson 5 coming soon
  7. Project: deploy the house-price model as an API Lesson 6 coming soon

Module 15: Data and Model Operations

6 lessons · coming soon
  1. Experiment tracking Lesson 1 coming soon
  2. Data versioning (DVC) and feature stores Lesson 2 coming soon
  3. Monitoring drift and performance Lesson 3 coming soon
  4. Scheduling pipelines with Airflow or Prefect Lesson 4 coming soon
  5. Cloud options: AWS, Azure, GCP, on-prem Lesson 5 coming soon
  6. Cost, latency and scaling trade-offs Lesson 6 coming soon

Module 16: LLM Application Engineering

6 lessons · coming soon
  1. LLM app architecture patterns Lesson 1 coming soon
  2. Caching, batching and streaming Lesson 2 coming soon
  3. Observability for LLM apps Lesson 3 coming soon
  4. Evaluation pipelines and regression tests Lesson 4 coming soon
  5. Security: prompt injection and data leakage Lesson 5 coming soon
  6. Project: production RAG service with monitoring Lesson 6 coming soon

GeoAI Specialization

Module 17: Spatial Machine Learning

8 lessons · coming soon
  1. Track overview: GeoAI Specialization Introduction coming soon
  2. GIS primer for AI developers: coordinates, projections, vector and raster Lesson 1 coming soon
  3. GeoPandas and Rasterio: spatial data in Python Lesson 2 coming soon
  4. Spatial autocorrelation and why it breaks standard ML Lesson 3 coming soon
  5. Spatial features: distance, density, neighbourhood stats Lesson 4 coming soon
  6. Geographically weighted models Lesson 5 coming soon
  7. Spatial interpolation with ML Lesson 6 coming soon
  8. Project: predicting service demand across districts Lesson 7 coming soon

Module 18: Imagery and Remote Sensing AI

7 lessons · coming soon
  1. Satellite and aerial data sources Lesson 1 coming soon
  2. Preprocessing rasters for deep learning Lesson 2 coming soon
  3. TorchGeo and Esri pretrained models Lesson 3 coming soon
  4. Land cover classification Lesson 4 coming soon
  5. Change detection Lesson 5 coming soon
  6. Object detection on imagery: vehicles, roofs, encroachments Lesson 6 coming soon
  7. Project: detecting unauthorized construction between two dates Lesson 7 coming soon

Module 19: LLMs for GIS Workflows

5 lessons · coming soon
  1. Text-to-SQL and text-to-query over spatial databases Lesson 1 coming soon
  2. LLM agents for ArcGIS and QGIS Lesson 2 coming soon
  3. Extracting locations and entities from documents Lesson 3 coming soon
  4. Automating cadastral and metadata QC with LLMs Lesson 4 coming soon
  5. Project: natural language assistant for a municipal geoportal Lesson 5 coming soon

Module 20: GeoAI in Production

5 lessons · coming soon
  1. Deploying models inside ArcGIS Enterprise Lesson 1 coming soon
  2. Raster inference at scale Lesson 2 coming soon
  3. Integrating AI outputs into web maps and dashboards Lesson 3 coming soon
  4. Governance, accuracy reporting and ethics Lesson 4 coming soon
  5. Capstone: end-to-end GeoAI project Lesson 5 coming soon