Course Catalog

All Courses & Roadmaps

Every course includes a full module index. Expand any course to see exactly what you'll learn, step by step.

🤖

AI & Machine Learning

2 courses
🤖

AI & Machine Learning Fundamentals

Beginner 12 weeks 8 modules

A complete, ground-up introduction to Artificial Intelligence and Machine Learning. You will go from Python basics through neural networks to building and evaluating real ML models.

Course Roadmap — 8 Modules Enquire About This Course →
01

Introduction to Artificial Intelligence

  • What is AI, ML, and Deep Learning
  • History and evolution of AI
  • Real-world applications across industries
  • Setting up your Python environment
02

Python for AI

  • Python essentials — variables, loops, functions, OOP
  • NumPy — arrays, broadcasting, linear algebra basics
  • Pandas — DataFrames, data manipulation, merging
  • Matplotlib & Seaborn — data visualization fundamentals
03

Data Preprocessing & Feature Engineering

  • Handling missing values and outliers
  • Feature scaling — normalization vs. standardization
  • Encoding categorical variables
  • Train/validation/test split strategies
04

Supervised Learning

  • Linear Regression — intuition and math
  • Logistic Regression and classification
  • Decision Trees and Random Forests
  • K-Nearest Neighbours and Support Vector Machines
05

Unsupervised Learning

  • K-Means and hierarchical clustering
  • DBSCAN — density-based clustering
  • PCA — dimensionality reduction
  • Anomaly detection techniques
06

Neural Networks & Deep Learning Basics

  • Perceptrons and multi-layer networks
  • Activation functions — ReLU, Sigmoid, Tanh
  • Backpropagation and gradient descent
  • Introduction to TensorFlow / Keras
07

Model Evaluation & Improvement

  • Metrics — accuracy, precision, recall, F1, AUC-ROC
  • Cross-validation and hyperparameter tuning
  • Overfitting, underfitting, and regularization
  • Bias-variance trade-off
08

Capstone — End-to-End ML Pipeline

  • Problem definition and dataset selection
  • Full EDA, preprocessing, and model selection
  • Model training, evaluation, and optimisation
  • Deployment basics with Flask / FastAPI
✨

Prompt Engineering & AI Tools

Beginner 6 weeks 5 modules

Learn to communicate effectively with large language models, design reliable prompt workflows, and leverage modern AI tools to automate real business tasks.

Course Roadmap — 5 Modules Enquire About This Course →
01

How Large Language Models Work

  • Tokens, context windows, and sampling
  • GPT, Claude, and Gemini — architecture overview
  • System prompts vs. user prompts
  • Understanding model limitations and hallucinations
02

Prompt Design Patterns

  • Zero-shot and few-shot prompting
  • Chain-of-thought and step-by-step reasoning
  • Role prompting and persona assignment
  • Output formatting — JSON, markdown, structured data
03

Advanced Techniques

  • Retrieval-Augmented Generation (RAG) basics
  • Prompt chaining and multi-step pipelines
  • Evaluating and iterating on prompts systematically
  • Temperature, top-p, and parameter tuning
04

AI Productivity Tools

  • ChatGPT, Claude, and Gemini — practical deep dives
  • GitHub Copilot and Cursor for coding workflows
  • Midjourney and image generation tools
  • Notion AI, Zapier AI, and document automation
05

Real-World Applications & Automation

  • Content creation and SEO workflows
  • Customer support and email automation
  • Data extraction and summarisation pipelines
  • Capstone: Build a personal AI assistant workflow
🐍

Programming & Data

1 course
🐍

Python for Data Science

Beginner 8 weeks 6 modules

A focused, hands-on Python course for aspiring data professionals — from core language skills to exploratory data analysis on real datasets.

Course Roadmap — 6 Modules Enquire About This Course →
01

Python Essentials

  • Variables, data types, and operators
  • Control flow — if/else, for/while loops
  • Functions, scope, and lambda expressions
  • Object-Oriented Programming — classes and inheritance
  • File I/O — reading and writing CSV, JSON, text
02

NumPy & Pandas

  • NumPy arrays, indexing, and broadcasting
  • Pandas Series and DataFrames
  • Data selection, filtering, and transformation
  • Merging, joining, and reshaping data
  • Handling missing values and duplicates
03

Data Visualization

  • Matplotlib — line, bar, scatter, histogram plots
  • Seaborn — heatmaps, pair plots, violin plots
  • Plotly — interactive charts and dashboards
  • Choosing the right chart for your data
04

Statistics for Data Science

  • Descriptive statistics — mean, median, mode, variance
  • Probability fundamentals and distributions
  • Hypothesis testing — t-tests, chi-square
  • Correlation and covariance
05

SQL for Data Analysts

  • SELECT, WHERE, ORDER BY, and LIMIT
  • Joins — INNER, LEFT, RIGHT, FULL OUTER
  • Aggregations — GROUP BY, HAVING, COUNT, SUM
  • Window functions — ROW_NUMBER, RANK, LAG, LEAD
  • Subqueries and CTEs
06

Capstone — Exploratory Data Analysis

  • Selecting a real-world dataset
  • Full data cleaning and preprocessing pipeline
  • Hypothesis generation and statistical testing
  • Storytelling with visualisations
  • Presenting findings as a professional report
🌐

Web Development

1 course
🌐

Full Stack Web Development

Intermediate 16 weeks 10 modules

The most comprehensive web development course — from HTML foundations through React, Node.js, databases, authentication, deployment, and interview preparation.

Course Roadmap — 10 Modules Enquire About This Course →
01

HTML & CSS Mastery

  • Semantic HTML5 and accessibility
  • Flexbox — layouts, alignment, responsive patterns
  • CSS Grid — advanced two-dimensional layouts
  • CSS variables, custom properties, and animations
  • Mobile-first responsive design
02

JavaScript Core

  • DOM manipulation and events
  • ES6+ — arrow functions, destructuring, spread, modules
  • Async JavaScript — Promises, async/await, Fetch API
  • Error handling and debugging
03

React.js

  • Components, props, and JSX
  • Hooks — useState, useEffect, useContext, useReducer
  • React Router — SPA navigation
  • State management — Context API and Zustand
  • Performance — lazy loading, memoisation
04

Node.js & Express

  • Node.js architecture and event loop
  • Building REST APIs with Express
  • Middleware — auth, validation, error handling
  • File uploads and static file serving
05

Databases — SQL & NoSQL

  • PostgreSQL — schema design, queries, indexes
  • MongoDB — documents, aggregation pipelines
  • ORMs — Prisma and Mongoose
  • Database relationships and normalisation
06

Authentication & Security

  • JWT — generation, verification, refresh tokens
  • OAuth 2.0 — Google, GitHub login
  • Session management and cookies
  • Security best practices — CORS, CSRF, XSS, rate limiting
07

Git & Developer Tooling

  • Git internals — commits, branches, merging, rebasing
  • GitHub — PRs, code reviews, issue tracking
  • Linux CLI essentials for developers
  • Environment management — .env, dotfiles
08

Deployment & DevOps Basics

  • Deploying frontends — Vercel, Netlify
  • Deploying backends — Railway, Render, EC2
  • Docker basics for web apps
  • CI/CD with GitHub Actions
09

Full Stack Capstone Project

  • Planning — requirements, wireframes, ERD
  • Building — multi-page app with auth, DB, REST API
  • Testing — unit and integration tests
  • Deploying to production with CI/CD
10

Interview & Career Preparation

  • System design basics for junior roles
  • Common technical interview questions
  • Building a portfolio and GitHub profile
  • Resume, LinkedIn, and job search strategy
☁️

Cloud & Infrastructure

2 courses
☁️

Cloud Computing with AWS

Intermediate 10 weeks 7 modules

Master core AWS services — compute, storage, networking, databases, security, and monitoring — and finish with certification-level knowledge and a deployed production application.

Course Roadmap — 7 Modules Enquire About This Course →
01

Cloud Fundamentals

  • What is cloud computing — IaaS, PaaS, SaaS
  • AWS Global Infrastructure — regions, AZs, edge locations
  • AWS console, CLI, and SDK setup
  • Pricing models — on-demand, reserved, spot
02

Compute

  • EC2 — instance types, AMIs, user data, key pairs
  • Auto Scaling Groups and Launch Templates
  • Elastic Load Balancers — ALB, NLB
  • Lambda — serverless functions and triggers
03

Storage & Databases

  • S3 — buckets, versioning, lifecycle policies, static hosting
  • EBS and EFS — block and file storage
  • RDS — managed relational databases, read replicas
  • DynamoDB — NoSQL, partition keys, GSIs
  • ElastiCache — Redis and Memcached caching
04

Networking

  • VPC — subnets, route tables, internet gateways
  • Security Groups and NACLs
  • VPC Peering and Transit Gateways
  • CloudFront — CDN and edge caching
  • Route 53 — DNS management and routing policies
05

IAM & Security

  • IAM users, groups, roles, and policies
  • Least-privilege principle and permissions boundaries
  • MFA, credential rotation, and access keys
  • AWS Secrets Manager and Parameter Store
06

Monitoring, Logging & Cost

  • CloudWatch — metrics, dashboards, alarms, log groups
  • CloudTrail — API audit logging
  • AWS Cost Explorer and Budgets
  • Trusted Advisor and Well-Architected Framework
07

Capstone & Certification Prep

  • Deploy a full three-tier application on AWS
  • Architecture review and cost optimisation
  • AWS Solutions Architect Associate exam tips
  • Practice questions and exam strategy
🔎

AWS OpenSearch Service

Intermediate 8 weeks 7 modules

Master AWS OpenSearch Service — the managed, scalable search and analytics engine built on OpenSearch. From domain setup and indexing through vector search, dashboards, and production operations.

Course Roadmap — 7 Modules Enquire About This Course →
01

Search Fundamentals & OpenSearch Architecture

  • What is OpenSearch — history, Elasticsearch vs OpenSearch
  • Inverted index, shards, replicas — how search works internally
  • AWS OpenSearch Service — managed architecture and domain concepts
  • Deployment options — VPC vs public access, instance types, storage
  • OpenSearch vs AWS Kendra — when to use which
02

Domain Setup & Configuration

  • Creating and configuring an OpenSearch domain via console & CLI
  • Node types — dedicated master, data, UltraWarm, cold storage
  • EBS volume configuration and storage tiers
  • VPC configuration — subnets, security groups, access policies
  • Multi-AZ deployment and cross-zone replication
03

Indexing, Mappings & Analyzers

  • Creating indices — settings and mappings
  • Data types — text, keyword, date, geo_point, dense_vector
  • Dynamic vs explicit mappings and mapping best practices
  • Custom analyzers — tokenizers, token filters, char filters
  • Bulk indexing with the REST API and Python client
  • Index aliases, templates, and data streams
04

Query DSL & Full-Text Search

  • Match, term, range, and wildcard queries
  • Bool queries — must, should, must_not, filter
  • Multi-match, phrase match, and fuzzy search
  • Relevance scoring — TF-IDF and BM25
  • Pagination — from/size, search_after, scroll API
  • Sorting, highlighting, and source filtering
05

Aggregations & OpenSearch Dashboards

  • Metric aggregations — avg, sum, min, max, cardinality
  • Bucket aggregations — terms, date histogram, range
  • Pipeline aggregations — moving average, derivative
  • OpenSearch Dashboards — connecting to your domain
  • Building visualisations — bar charts, pie charts, time series
  • Creating dashboards and setting up alerts
06

Vector Search & Semantic Search

  • k-NN plugin — approximate nearest neighbour search
  • Indexing dense vector embeddings
  • Generating embeddings with Amazon Bedrock / SageMaker
  • Hybrid search — combining lexical and semantic scoring
  • Neural search plugin and ML inference pipeline
  • Building a RAG pipeline with OpenSearch as the vector store
07

Security, Monitoring & Production Operations

  • Fine-grained access control — roles, role mappings, backend roles
  • Encryption at rest and in transit — KMS integration
  • Automated snapshots and manual snapshot management (S3)
  • CloudWatch metrics — cluster health, JVM pressure, search latency
  • Index State Management (ISM) — lifecycle policies, rollover, deletion
  • Scaling — adding nodes, UltraWarm for cold data, cost optimisation
⚙️

DevOps & Automation

1 course
⚙️

DevOps & CI/CD Pipelines

Advanced 12 weeks 8 modules

The complete DevOps engineering curriculum — Linux, Docker, Kubernetes, CI/CD, Infrastructure as Code, and full observability, culminating in deploying a microservices app end-to-end.

Course Roadmap — 8 Modules Enquire About This Course →
01

Linux & Shell Scripting

  • Linux filesystem, permissions, and user management
  • Essential commands — grep, awk, sed, find, curl
  • Bash scripting — variables, loops, functions, conditionals
  • Process management and cron jobs
  • SSH, SCP, and remote server administration
02

Advanced Version Control

  • Git internals — objects, refs, and the index
  • Branching strategies — Git Flow, trunk-based development
  • Pull request workflows and code review best practices
  • Rebasing, cherry-picking, and conflict resolution
03

Docker & Containerisation

  • Container fundamentals vs. virtual machines
  • Dockerfile — layers, multi-stage builds, caching
  • Volumes, networking, and container communication
  • Docker Compose — multi-service applications
  • Docker Hub and private registries
04

Kubernetes

  • Kubernetes architecture — control plane and worker nodes
  • Pods, Deployments, ReplicaSets, and StatefulSets
  • Services — ClusterIP, NodePort, LoadBalancer
  • Ingress controllers and TLS termination
  • ConfigMaps, Secrets, and resource limits
  • Helm charts and package management
05

CI/CD Pipelines

  • GitHub Actions — workflows, triggers, runners, secrets
  • Jenkins — pipelines, shared libraries, agents
  • Pipeline stages — build, test, security scan, deploy
  • Automated testing integration and coverage gates
  • Blue-green and canary deployment strategies
06

Infrastructure as Code

  • Terraform — providers, resources, state, modules
  • Terraform Cloud and remote backends
  • AWS CloudFormation basics
  • Ansible — playbooks, roles, and inventory management
07

Monitoring, Logging & Observability

  • Prometheus — metrics collection and PromQL
  • Grafana — dashboards and alerting rules
  • ELK Stack — Elasticsearch, Logstash, Kibana
  • Distributed tracing — Jaeger and OpenTelemetry
  • SLIs, SLOs, and on-call best practices
08

Capstone — Microservices Production Deployment

  • Architecting a microservices application
  • Containerising all services with Docker
  • Deploying to Kubernetes with Helm
  • Full CI/CD pipeline with automated tests and rollback
  • Monitoring, alerting, and post-deployment verification

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