Source
https://www.udemy.com/course/ml-foundations/
Category
File Size
5.4 GB
Publisher
Swapnil Daga
Updated
January 28, 2026
Description
This course builds strong ML foundations by combining clear intuition, solid math, and hands-on implementation. You won’t just use ML libraries — you’ll understand how models work internally, why they work, and when they fail.
After completing this course, you will:
- Think beyond black-box ML
- Confidently explain ML concepts in interviews
- Build and debug models on your own
- Choose the right model for the right problem
In short: from following tutorials → to real ML understanding.
This course is ideal for :
- Students & freshers aiming for ML/Data roles
- Software professionals transitioning into ML
- Anyone who knows “some ML” but lacks confidence
This course helps you upgrade your career by building real ML depth, not just surface knowledge.
What is covered:
- Math foundations for ML (basic → advanced)
- Core models: Linear & Logistic Regression, Decision Trees, Neural Networks
- Ensemble methods: Bagging, Boosting, Random Forest
- Optimizers, regularization, overfitting & bias-variance tradeoff
- Hands-On Learning
- Movie rating classification (Kaggle + GPUs)
- Neural Network implementation from scratch
- Music genre classification using MFCC + Neural Networks
Preview
1 image




