Beginner AI Program
Two months to a working foundation in AI: Python, mathematics and statistics, data analysis and visualization, and basic prompt engineering.
Eight weeks, three phases
The Starter plan follows phases 1 to 3 of the 24-week roadmap, plus basic prompt engineering. Each phase closes with an assignment or a project.
Phase 1 • Introduction to AI and Python
AI fundamentals — what is artificial intelligence, history of AI, types of AI, machine learning vs deep learning, AI applications across industries, ethics and responsible AI
Python programming — variables and data types, loops and conditions, functions, lists, tuples and dictionaries, object-oriented programming, file handling, exception handling
Phase 2 • Mathematics and statistics for AI
Mathematics — linear algebra basics, matrices and vectors, probability, statistics, mean, median and mode, standard deviation, correlation
Data preprocessing — data cleaning, missing values, data transformation, feature scaling
Phase 3 • Data analysis and visualization
Python libraries — NumPy, Pandas, Matplotlib, Seaborn
Data analysis — DataFrames, data filtering, grouping, merging datasets, visualization techniques
Basic machine learning and prompt engineering
An introduction to supervised and unsupervised learning, and basic prompt engineering with tools such as ChatGPT, Gemini and Claude. Deep learning, generative AI depth, NLP and computer vision are covered in the Professional and Career Pro plans.
Phase 1 • Introduction to AI and Python
AI fundamentals — types of AI, ML vs deep learning, applications across industries, ethics and responsible AI
Python programming — data types, loops, functions, collections, OOP, file and exception handling
Phase 2 • Mathematics and statistics for AI
Mathematics — linear algebra, matrices and vectors, probability, statistics, standard deviation, correlation
Data preprocessing — cleaning, missing values, transformation, feature scaling
Phase 3 • Data analysis and visualization
Libraries — NumPy, Pandas, Matplotlib, Seaborn
Analysis — DataFrames, filtering, grouping, merging datasets, visualization techniques
Phase 4 • Machine learning
Fundamentals & Evaluation — supervised, unsupervised and reinforcement learning, accuracy, precision, recall, F1 score, confusion matrix
Algorithms — linear and logistic regression, decision trees, random forests, K-nearest neighbors, Naive Bayes, K-means clustering
Deep learning and generative AI
Phase 5 • Deep learning
Neural networks — perceptrons, activation functions, forward propagation, backpropagation, TensorFlow, Keras, PyTorch
Models — Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)
Phase 6 • Generative AI and LLMs (basics)
Fundamentals — what is generative AI, large language models, prompt engineering, fine-tuning concepts, embeddings
Tools — ChatGPT, Gemini, Claude, LangChain, Hugging Face
Phase 7 • NLP and computer vision (basics)
NLP — text preprocessing, tokenization, sentiment analysis, named entity recognition
Computer vision — image processing, object detection, face recognition, OCR
Phase 8 • API development and MLOps (basics)
Deployment — Flask and FastAPI, REST APIs, Docker basics
MLOps introduction — model versioning, monitoring, model optimization
Phase 9 • AI ethics and security • Capstones
Responsible AI, AI bias, data privacy, AI governance and security considerations, followed by two capstone projects and a one-month internship.
AI Chatbot
Conversational interface • knowledge base integration
Recommendation System
Product recommendations • user behavior analysis
Phase 1 • Introduction to AI and Python
AI fundamentals — what is AI, history, types of AI, ML vs deep learning, industry applications, ethics and responsible AI
Python programming — data types, loops and conditions, functions, collections, OOP, file handling, exception handling
Phase 2 • Mathematics and statistics for AI
Mathematics — linear algebra, matrices and vectors, probability, statistics, mean, median and mode, standard deviation, correlation
Data preprocessing — data cleaning, missing values, transformation, feature scaling
Phase 3 • Data analysis and visualization
Libraries — NumPy, Pandas, Matplotlib, Seaborn
Analysis — DataFrames, filtering, grouping, merging datasets, visualization techniques
Phase 4 • Machine learning
Fundamentals & Evaluation — supervised, unsupervised and reinforcement learning, accuracy, precision, recall, F1 score, confusion matrix
Algorithms — linear and logistic regression, decision trees, random forests, K-nearest neighbors, Naive Bayes, K-means clustering
Phase 5 • Deep learning
Neural networks — perceptrons, activation functions, forward propagation, backpropagation, TensorFlow, Keras, PyTorch
Models — Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)
From models to production
Phase 6 • Generative AI and LLMs
Fundamentals & Tools — generative AI, LLMs, prompt engineering, fine-tuning, embeddings, vector databases, ChatGPT, Gemini, Claude, LangChain, Hugging Face
Retrieval-Augmented Generation — document processing, vector databases, semantic search, AI chatbots
Phase 7 • Computer vision and NLP
NLP — text preprocessing, tokenization, sentiment analysis, named entity recognition
Computer vision — image processing, object detection, face recognition, OCR
Phase 8 • AI deployment and MLOps
Deployment & Cloud — Flask and FastAPI, REST APIs, Docker basics, AWS, Google Cloud, Azure, Hugging Face Spaces
MLOps — model versioning, model monitoring, CI/CD pipelines, model optimization
Phase 9 • AI ethics and security
Responsible AI • AI bias • data privacy • AI governance • security considerations
Phase 10 • Capstone projects
Students must complete at least three major projects; the Career Pro plan delivers four industry capstones.
AI Chatbot
Conversational interface • knowledge base • RAG implementation
Recommendation System
Product recommendations • user behavior analysis
AI Content Generator
Blog generation • summarization • translation
Support beyond the classroom
Every cohort member gets personalized portfolio reviews, technical mock interviews, and access to our network of 120+ hiring partners.
Resume building
A reviewed, AI-focused resume built around the projects completed in the program.
Two mock interviews
Technical and behavioural rounds with written feedback after each session.
Basic placement assistance
Access to the hiring partner list and referrals for entry-level data roles.
Industry certification
Issued on completion of all ten assignments and both mini projects.
GitHub portfolio
Seven reviewed repositories with documented notebooks and READMEs.
Resume & LinkedIn
A rewritten resume plus LinkedIn optimization for AI and data roles.
Five mock interviews
Machine learning theory, coding and system design rounds with feedback.
Priority placement
Referrals ahead of Starter candidates, plus limited 1-on-1 mentorship.
Two-month internship
Live team placement with weekly review, ending in a shipped feature and a written report.
Four industry capstones
Chatbot with RAG, recommendation system, content generator, and one chosen additional project.
Ten mock interviews
Theory, coding, system design and behavioural rounds, each with written feedback.
Premium placement
Unlimited 1-on-1 mentorship, resume and LinkedIn optimization, priority referrals.