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Machine Learning Foundations

Understand and implement core ML algorithms with intuition, math, and hands-on code.

4.8(1,320 ratings)
12,330 students
38h 20m total video hours
2 video lessons
Kavya Nair

Created by

Kavya Nair

AI Research Scientist

Machine Learning Foundations
Free$149.00
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38h 20m on-demand video
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What You Will Learn

Understand loss functions, cost surfaces, and gradient descent optimization
Implement classification and regression algorithms from mathematical principles
Master bias-variance tradeoff, regularization (L1 Lasso / L2 Ridge), and cross-validation
Construct neural network architectures with backpropagation
Deploy ML inference pipelines to web APIs

Course Curriculum

1 modules • 2 lessons • 38h 20m total duration

Module 1: Supervised Learning & Optimization2h 15m

Lesson 1: Gradient Descent & Loss Landscapes

Visualize how parameters update along negative gradients to minimize cost functions such as Mean Squared Error and Cross-Entropy.

29:10

Lesson 2: Classification with Logistic Regression & Sigmoid

Learn how the sigmoid activation function maps continuous real numbers into probability estimates between 0 and 1 for binary classification.

24:45

Course Final Assessment Quiz

5 questions • Instant correct/incorrect answer verification • Score 80%+ for Certificate

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About Your Instructor

Kavya Nair

Kavya Nair

AI Research Scientist

Published researcher in deep learning architectures, former lab researcher at Stanford AI, passion for intuitive math.

Requirements

  • Comfort with basic linear algebra, calculus intuition, and Python
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