MACHINE LEARNING AND AI - Beginners

Learn AI & Machine Learning from scratch! Master Linear Regression, SVM, Decision Trees, etc.

What you'll learn
- Understand what AI, ML, and Deep Learning are — and how they differ
- Build strong foundational knowledge in Linear Regression, Cost Functions, and Prediction Models
- Learn and explain key ML algorithms like Decision Trees, Random Forest, SVM, Clustering, and more
- Understand R-Square, Least Squares, and how models evaluate performance
- Perform hands-on formula-based calculations used in machine learning manually
- Explore career opportunities and real-world applications of AI/ML in business, healthcare, marketing, etc.
- Gain the ability to understand ML models conceptually before jumping into code
- Be ready to take on more advanced AI/ML courses, certifications, and job interviews

Requirements
- Basic understanding of mathematics (algebra, simple statistics)
- Curiosity to learn data-driven problem-solving
- A working laptop or mobile to view lectures and take notes
- No prior coding or ML experience is required (this course starts from scratch!)

Description
Unlock the world of Artificial Intelligence and Machine Learning with this beginner-friendly course! Whether you're a student, aspiring data scientist, or tech enthusiast, this course gives you a solid foundation in AI, ML, and Data Science—with zero fluff and full clarity.

You'll start with thebasics of AI, including what AI is, its varioussubsets (Machine Learning, Deep Learning, Computer Vision, Reinforcement Learning), and how they relate to each other. From there, we dive deep into the role ofData Sciencein modern AI applications.

This course simplifies complex topics like:

Linear and Multiple Linear Regression

Cost Functions & Gradient Descent

Polynomial Regression

Support Vector Machines (SVM)

Decision Tree Regression

Random Forest Algorithm

K-Means Clustering

All withreal-world examples, visual explanations, and formula breakdownsto ensure practical understanding.

No prior coding experience or math-heavy background is needed. This course is designed tomake concepts intuitiveand actionable—so you not only understand the theory but know how to use it.

What You’ll Learn:

What is AI and its key subsets (ML, DL, CV, RL)

Supervised vs Unsupervised Learning

Real-world applications of AI/ML

How data science powers machine learning

Build and understand linear & multiple linear regression models

How cost functions and gradient descent work

Decision Trees, Random Forests & SVMs explained simply

Basics of clustering with K-Means

Who this course is for:
- This course is perfect for:
- Beginners curious about AI and Machine
- Learning Students from any background (technical or non-technical)
- Business analysts, marketers, or entrepreneurs wanting to use ML
- Job seekers preparing for data sd a strong foundcience roles Anyone looking to builation in AI/ML — without needing prior coding experience
More Info