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Artificial Intelligence & Machine Learning Masterclass

Learn to build intelligent AI-powered applications using Python, OpenAI APIs, and machine learning frameworks. From prompt engineering to neural networks — become an AI practitioner ready for the future.

4.9 (1,120 reviews)
| 120+ enrolled students | By Tooba Kashif
Artificial Intelligence

Course Overview

Artificial Intelligence is reshaping every industry — from healthcare and finance to marketing and software development. This hands-on course provides a practical pathway into the world of AI and Machine Learning. You will learn Python programming for AI, work with industry-leading frameworks like TensorFlow and scikit-learn, master prompt engineering with the OpenAI ChatGPT API, and build real-world AI-powered applications from scratch.

This is not a theoretical course. Every module culminates in a practical project. By the end of the program, you will have built an AI chatbot, an image classification system, a recommendation engine, and a full AI-integrated web application — all ready to showcase in your portfolio. No prior AI experience is required; just a willingness to learn.

What You'll Learn

Master Python programming specifically for AI & data science.
Build machine learning models with scikit-learn & TensorFlow.
Engineer advanced prompts using ChatGPT & OpenAI API.
Develop custom AI chatbots and voice assistants.
Implement deep learning & neural network architectures.
Build computer vision apps with image classification models.
Integrate AI models into live web & mobile applications.
Automate real-world workflows using AI agents & tools.

Course Curriculum

Get a fast-paced introduction to Python with a focus on AI-relevant libraries: NumPy, Pandas, and Matplotlib. Learn data structures, list comprehensions, and how to manipulate datasets efficiently.
  • Python environment setup & Jupyter notebooks 14:20
  • NumPy arrays & Pandas DataFrames 22:40
  • Data cleaning & visualization project 30:00

Understand supervised and unsupervised learning. Build regression, classification, and clustering models using scikit-learn. Learn model evaluation, cross-validation, and hyperparameter tuning.
  • Linear & logistic regression models 26:15
  • Decision trees, Random Forest & SVM 31:50
  • Build a spam email classifier 35:00

Master the art of communicating with large language models. Learn prompt chaining, few-shot learning, system roles, and how to integrate the OpenAI GPT-4 API into your own applications.
  • OpenAI API setup & authentication 18:30
  • Advanced prompting: Chain-of-thought & RAG 24:00
  • Build a custom AI assistant chatbot 40:00

Build multi-layer neural networks using TensorFlow and Keras. Understand backpropagation, activation functions, dropout layers, and how to prevent overfitting in deep learning models.
  • TensorFlow & Keras architecture overview 22:10
  • Training & optimizing deep neural networks 35:40
  • Build a handwriting digit classifier (MNIST) 28:00

Use Convolutional Neural Networks (CNNs) to process and classify images. Explore transfer learning with pre-trained models (ResNet, MobileNet) and build your own image recognition app.
  • CNN architecture & feature extraction 29:30
  • Transfer learning with ResNet & MobileNet 23:15
  • Build a real-time object detection system 45:00

Deploy your AI models as real web APIs using Flask and FastAPI. Integrate them into front-end applications, host on cloud platforms (Render, HuggingFace Spaces), and build end-to-end AI products.
  • Wrapping ML models into REST APIs 21:40
  • Deploying AI apps on cloud platforms 18:20
  • Capstone: Full AI-powered web application 60:00

Prerequisites

Basic Programming Knowledge: Some familiarity with any programming language (even if it's just HTML/CSS) will be helpful, though complete beginners can also follow along with extra effort.

Mathematics Basics: A high-school level understanding of algebra and statistics is sufficient. We will cover the math concepts needed as we go.

Laptop & Internet: A computer with at least 8GB RAM is recommended for running machine learning models locally. Cloud-based alternatives (Google Colab) are also provided free of charge.

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Tooba Kashif
AI & ML Engineer

Tooba is an experienced AI engineer and curriculum designer with over 4 years of professional experience building machine learning pipelines, deploying AI models at scale, and teaching practical AI skills to students across all backgrounds.