Course overview
• Thumbnail
Build Intelligent Systems
Learn the fundamentals of Artificial Intelligence and Machine Learning and discover how intelligent systems are designed, trained, and used in real-world applications. This course is designed to give beginners and aspiring developers a strong foundation in modern AI technologies.
You will learn important concepts such as Artificial Intelligence, Machine Learning, supervised and unsupervised learning, datasets, data preprocessing, model training, classification, regression, clustering, and model evaluation. You will also explore how Python and popular machine learning libraries can be used to build practical AI solutions.
Through hands-on examples and practical projects, you will understand how machines learn from data and how AI can be applied to solve real-world problems. The course gradually takes you from basic concepts to building simple intelligent models.
By the end of this course, you will have a solid understanding of AI and Machine Learning fundamentals and be ready to continue learning advanced topics such as Deep Learning, Neural Networks, Computer Vision, Natural Language Processing, and Generative AI.
You will learn important concepts such as Artificial Intelligence, Machine Learning, supervised and unsupervised learning, datasets, data preprocessing, model training, classification, regression, clustering, and model evaluation. You will also explore how Python and popular machine learning libraries can be used to build practical AI solutions.
Through hands-on examples and practical projects, you will understand how machines learn from data and how AI can be applied to solve real-world problems. The course gradually takes you from basic concepts to building simple intelligent models.
By the end of this course, you will have a solid understanding of AI and Machine Learning fundamentals and be ready to continue learning advanced topics such as Deep Learning, Neural Networks, Computer Vision, Natural Language Processing, and Generative AI.
Mode
Record Videos
Duration
1h 38m · 12 lessons
Level
Beginner to Pro
Language
Urdu / Hindi
What You Will Learn
- Separate AI, ML, and deep learning in plain language
- Describe the ML workflow and why data quality matters
- Tell supervised from unsupervised problems
- Explain simple models and honest evaluation
- Know the Python libraries you will meet next
- Use AI responsibly (privacy, bias, verification)
Course Curriculum
Module 1: AI Landscape
-
AI vs Machine Learning vs Deep Learning08:00
-
Where AI Shows Up07:00
-
The ML Workflow08:00
Module 2: Data and Learning Types
-
Datasets and Features09:00
-
Supervised Learning09:00
-
Unsupervised Learning07:00
Module 3: Models You Can Explain
-
Linear Models Intuition09:00
-
Trees and Neighbors (Idea)08:00
-
Evaluating Honestly08:00
Module 4: Python Path & Responsible AI
-
Python Stack Overview09:00
-
A Toy Model in Words10:00
-
Responsible AI06:00
Video testimonials
Student Success Stories
Is course ke graduates ki zubani — real experience.
Course Reviews
4.5"Good balance of theory and practice. Some later lessons assume you already know Python well, so brush up first."
"This course made neural nets feel approachable. Paid track is worth it if you want a career shift into AI."
"Strong intro to ML workflows. I would like more notebook walkthroughs, but the concepts are solid."
"Math is explained without scare tactics. Supervised learning modules are excellent and the examples are relevant."
Meet Your Instructor
-
Hina Zaidi
AI Instructor
Machine learning fundamentals and responsible AI practice.
Frequently Asked Questions
Kya math PhD chahiye?
Nahi. School-level graphs aur percentages se start. Calculus baad mein specialized tracks mein aati hai.
GPU / ChatGPT API zaroori hai?
Is foundation track ke liye nahi. Concepts pehle, heavy models baad mein.
Python pehle karni chahiye?
Helpful hai ÔÇö PYTHON PROGRAMMING course parallel ya pehle kar sakte ho. Kuch lessons spreadsheet se bhi samajh aate hain.
Kya main job-ready ML engineer ban jaunga?
Yeh foundation hai, job-complete path nahi. Next: projects, stats, aur supervised practice.