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From classic AI ideas to large language models: learn how they work, how to build with them and how to use them responsibly.
Free, no-obligation chat · Affordable plans that fit your budget · Start this week
About this course
Artificial intelligence has moved from research papers into everyday tools. Learners often find the field confusing: where classic search and logic end and machine learning begins, what a language model actually does with your prompt, why it sometimes states wrong facts with confidence, and how to build something dependable on top of it.
This course has two parts. Part one covers AI foundations: intelligent agents, search, knowledge and logic, probability, and a machine learning overview. Part two covers generative AI: how large language models work, prompt design, embeddings and vector search, retrieval-augmented generation, building applications through APIs with Python, evaluation, safety and responsible use.
Learning is through affordable 1:1 mentoring, where a mentor explains each idea with small, hands-on examples and reviews your code with you. Message us on WhatsApp or email, or fill in the short form, for a free, no-obligation chat. We will suggest a plan that fits your budget and flexible timings, and you can start this week.
Outcomes
Detailed syllabus
Every module lists its topics and what you will be able to do. Your mentor plans sessions around them and your own goals.
The goals, history and branches of AI, and how it differs from ordinary software.
By the end of this module you can
Covered in 1:1 mentoring sessions
How an AI program explores possibilities to reach a goal.
By the end of this module you can
Covered in 1:1 mentoring sessions
Representing facts and drawing conclusions from them.
By the end of this module you can
Covered in 1:1 mentoring sessions
Making decisions when information is incomplete.
By the end of this module you can
Covered in 1:1 mentoring sessions
How programs learn patterns from data, enough to understand modern AI.
By the end of this module you can
Covered in 1:1 mentoring sessions
The ideas inside large language models, without the heavy maths.
By the end of this module you can
Covered in 1:1 mentoring sessions
Getting reliable results by writing clear instructions.
By the end of this module you can
Covered in 1:1 mentoring sessions
Turning text into vectors so that meaning can be searched.
By the end of this module you can
Covered in 1:1 mentoring sessions
Grounding a language model's answers in your own data.
By the end of this module you can
Covered in 1:1 mentoring sessions
Calling model services from Python and shaping a dependable application.
By the end of this module you can
Covered in 1:1 mentoring sessions
Knowing whether your GenAI system is actually good.
By the end of this module you can
Covered in 1:1 mentoring sessions
Using AI carefully and fairly.
By the end of this module you can
Covered in 1:1 mentoring sessions
Building and presenting your own AI application.
By the end of this module you can
Covered in 1:1 mentoring sessions
Ways to learn
A mentor who knows this course plans sessions around your goal, explains each idea with fresh examples and checks your understanding. Personal attention, flexible timings in your time zone and affordable plans that fit your budget.
Talk to our teamHow it works
Four simple steps to start Artificial Intelligence and Generative AI with a mentor. A real person replies to you; there is no booking system.
Message us on WhatsApp or email, or fill in the short form. The first chat is free, with no obligation.
We listen to your goal, level and deadlines, then suggest a plan that fits your budget and timings.
Online 1:1 sessions with a mentor who knows your course, explains clearly and checks your understanding.
Keep going between sessions with practice, and watch your confidence grow week by week.
Learn by doing
Implement BFS and A* on a grid and compare how many cells each one explores.
Build an unbeatable game player using minimax with alpha-beta pruning.
Embed a folder of documents and return the most relevant passages for any question.
Build a RAG application that answers from your own PDFs or notes and shows its sources.
Create a small test set, score different prompts and record which version works best and why.
Your progress
Milestones from the start, middle and end of the syllabus.
Describe the main branches of AI and how they relate
Early on
Write structured prompts that return predictable output
Halfway
Deliver a working GenAI application with a documented evaluation
By the end
FAQs
Free, no-obligation chat
Tell us what you need. Our support team replies within one working day, by email or WhatsApp, whichever you prefer, and arranges your mentor.
Prefer to talk first?
Message the support team directly. We usually reply the same day.
Our mentors teach and explain. They never complete graded work, tests or assessments for students.
Keep going
Regression, classification, ensembles, SVM, clustering, PCA and evaluation.
Neural networks, backpropagation, CNNs, RNNs, LSTMs, transformers and PyTorch.
NumPy, pandas, statistics, EDA and first models with scikit-learn.
Core Python from zero to functions, collections, files, OOP and algorithms.
Asymptotics, recurrences, divide and conquer, greedy, DP, graphs, network flow and NP-completeness.
Logic, proofs, sets, relations, counting, probability, graphs and recurrences.
Last reviewed
Message us on WhatsApp or email, or fill in the short form. Our team replies with a plan that fits your goal and budget, and you can start this week.
Talk to us about a mentor