How to Get Into AI Course

Lesson 1 • What AI Actually Means

Demystify AI by distinguishing it from machine learning, deep learning, and data science. Establishes shared vocabulary used throughout the course.

Lesson 2 • How AI Projects Are Built

Trace the lifecycle of an AI project from problem definition to deployment. Grounds learners in how teams and roles interact in practice.

Lesson 3 • The AI Industry Ecosystem

Map the sectors, company types, and stakeholders that make up the AI industry. Helps learners identify where they can realistically enter.

Lesson 1 • Matching Your Background to AI Roles

Use a structured framework to map existing skills and experience to viable AI entry points. Produces a personalised shortlist of target roles.

Lesson 2 • Understanding Role Seniority and Growth

Outline career progression from junior to senior and staff levels within AI roles. Sets realistic expectations for timelines and promotion criteria.

Lesson 3 • Technical AI Roles Explained

Break down roles such as ML engineer, data scientist, and AI researcher by daily tasks and skill requirements. Clarifies distinctions often confused by newcomers.

Lesson 4 • Non-Technical AI Roles

Explore product, strategy, ethics, and operations roles that require AI fluency but not coding. Expands the career map for non-engineers.

Lesson 1 • Creating a Personalised Learning Plan

Design a structured, time-bound plan to close identified skill gaps using free and paid resources. Converts gap analysis into actionable weekly milestones.

Lesson 2 • Data Literacy and Handling

Develop skills in understanding, cleaning, and exploring datasets as a prerequisite for modelling. Directly supports the data preparation phase of AI projects.

Lesson 3 • Mathematics Essentials for AI

Identify the specific math concepts—linear algebra, calculus, statistics—that underpin AI models. Focuses on practical relevance, not academic depth.

Lesson 4 • Introduction to Machine Learning Models

Build conceptual and practical understanding of supervised and unsupervised learning algorithms. Prepares learners for hands-on model building in later chapters.

Lesson 1 • Using Public Datasets and Competitions

Leverage open datasets and ML competition platforms to build skills and gain credibility. Connects competitive participation to resume and portfolio value.

Lesson 2 • Contributing to Open-Source AI Projects

Participate in open-source AI repositories to gain collaborative coding experience and visibility. Builds professional credibility through public contribution history.

Lesson 3 • Building and Deploying Simple AI Apps

Create and deploy lightweight AI-powered applications to demonstrate end-to-end capability. Produces tangible, shareable portfolio artefacts beyond notebooks.

Lesson 4 • Structuring a Strong AI Portfolio

Organise and present projects in a portfolio that communicates skill and impact to hiring managers. Applies storytelling principles to technical work.

Lesson 1 • Building an AI-Focused Online Presence

Optimise LinkedIn and GitHub profiles to signal AI expertise and attract recruiter attention. Establishes a credible digital footprint before active outreach.

Lesson 2 • Leveraging AI Conferences and Events

Maximise value from AI conferences, workshops, and hackathons as networking and learning venues. Teaches preparation and follow-up tactics for event-based networking.

Lesson 3 • Working with Recruiters and Referrals

Navigate relationships with technical recruiters and leverage employee referrals to bypass competitive application queues. Increases interview conversion rates significantly.

Lesson 1 • Optimising Your Resume for AI Roles

Rewrite and tailor your resume to highlight AI-relevant skills, projects, and impact metrics. Addresses applicant tracking system optimisation and recruiter readability.

Lesson 2 • Writing Effective AI Cover Letters

Craft targeted cover letters that connect your background to specific AI role requirements. Teaches differentiation through narrative rather than resume repetition.

Lesson 3 • Researching AI Job Markets

Analyse job postings, salary data, and hiring trends to identify high-opportunity AI roles and companies. Grounds the search in real market intelligence.

Lesson 4 • Managing the Application Pipeline

Implement a tracking system to manage applications, follow-ups, and deadlines efficiently. Prevents missed opportunities and maintains momentum throughout the search.

Lesson 1 • Understanding the AI Hiring Pipeline

Map the typical stages of AI hiring—screen, technical, system design, behavioural—and their evaluation criteria. Sets expectations and preparation priorities.

Lesson 2 • Presenting Portfolio Work in Interviews

Communicate your projects clearly and confidently to both technical and non-technical interviewers. Turns portfolio artefacts into compelling interview narratives.

Lesson 3 • Behavioural and Situational Interviews

Prepare structured responses to behavioural questions using AI-specific examples from your experience. Connects soft skills to technical role requirements.

Lesson 4 • Tackling ML System Design Questions

Develop a structured approach to open-ended ML system design problems asked in senior and mid-level interviews. Teaches frameworks for scoping and communicating solutions.

Lesson 1 • Evaluating AI Job Offers

Assess total compensation, growth potential, team quality, and culture fit across competing offers. Provides a multi-factor framework beyond base salary comparison.

Lesson 2 • Sustaining Long-Term Career Growth

Establish habits for continuous learning, skill updating, and career advancement in a fast-moving field. Ensures the career launch becomes a lasting trajectory.

Lesson 3 • Negotiating Compensation Effectively

Apply evidence-based negotiation tactics to maximise compensation without damaging offer relationships. Covers salary, equity, and non-monetary benefits.