Beginner AI Course

Through a combination of theory and hands-on exercises, students will learn how AI works, explore practical Python programming for AI, and gain foundational skills to build simple AI models and applications.

(Installment Options Available)

Duration: 3 Months

35,000 PKR

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Starting Date

30 Oct, 2025

Introduction

This course introduces the fundamentals of Artificial Intelligence, covering basic concepts, types of AI, and essential mathematics and programming skills. Learners will explore machine learning, neural networks, and generative AI through hands-on exercises, simple projects, and practical Python examples, building a foundation to create and evaluate AI models confidently.

Sr. No.	Contents
1	Introduction to AI & Python Basics What is AI, what it can / cannot do Types: narrow AI, general AI (basic intro) Overview: Machine Learning vs Deep Learning vs AI apps Python refresher: data types, basic syntax, working with Python REPL / notebooks
2	Data Handling & Preprocessing Libraries: NumPy & pandas Loading datasets; CSV / JSON Cleaning data: missing values, basic cleaning Exploratory data analysis: summary statistics, visualizations (matplotlib / seaborn)
3	Core Math & Statistics for AI Basic statistics: mean, median, mode, variance, standard deviation Probability fundamentals Linear algebra basics: vectors, matrices, dot product Why this math matters in ML
4	Supervised Learning What is supervised learning Regression: linear regression, cost/loss, fitting model Classification: logistic regression, evaluation metrics (accuracy, precision, recall, F1)
5	Unsupervised Learning Clustering (K-means etc.) Dimensionality reduction (PCA basic idea) Use cases
6	Model Evaluation & Overfitting Train/test split, cross-validation Bias vs variance Overfitting, underfitting, regularization basics
7	Neural Networks & Deep Learning Intro What is a neural network; perceptron; activation functions Basic feedforward network Loss functions, optimizers Training via backpropagation (with libraries e.g. Keras / PyTorch)
8	Practical ML Project Putting together what’s learned: choose a simple dataset (e.g. house prices / Iris / MNIST) Build, train, evaluate a model Report: dataset used, preprocessing, model choice, evaluation
9	Introduction to Generative AI What is generative AI; how is it different Examples of generative AI: text generation, image generation, audio, etc. Basic architectures: what a model needs to generate rather than just classify
10	Language Models & Prompt Engineering Large Language Models (LLMs) basics: what they are, how they are trained / fine-tuned Tokenization, embeddings Prompt engineering: making prompts, few-shot / zero-shot, controlling output
11	Generative Models: Hands-on Tools Using APIs / tools: e.g. OpenAI API, Hugging Face models Simple projects: text completion, summarization, maybe image generation (via DALLE or similar) Discussion: limitations, biases, risks (hallucinations etc.)
12	Final Project & Wrap Up Final project: pick one of generative AI applications (e.g. chatbot, summarizer, content generator, image + text combined) Deployment / demonstration of project

Skill Level	Avg Monthly Salary
Junior	75k-100k
Mid-Level	100k - 170k
Advanced	250k- 450k
Freelancer	Earn in millions