Linear Regression How Machines Learn From Data || Hindi || Lesson 11 || Machine Learning ||

In this class We discuss linear regression how machines learn from data with visualization hindi 0:00 - continuation class of linear regression loss function 0:28 - graphical visualization of gradient descent in three dimensional coordinate axes 0:58 - random initialization in gradient descent (the first step) and visual of how weights move 1:44 - refreshing formulae for gradient descent in two dimensional space 2:36 - deriving gradient descent formulae for loss function in 3D 3:56 – subscribe 4:20 real example to show weights update 5:05 – first iteration in gradient descent 6:09 - graphical changes in weights and loss surface after first iteration 6:37 - After 15000 iterations how weights shifted 7:33 - final prediction line 8:11 - comment Gradient Descent is the engine of AI. Imagine a 3D "Loss Surface" shaped like a bowl. Our goal is the global minimum, where error is lowest. We start with random parameters, such as w 1 = 3 w 1 ​ =3 and w 0 = − 2 w 0 ​ =−2 . At this stage, our prediction line fits poorly, representing high initial loss. The math relies on partial derivatives: w n e w = w o l d − α ( ∂ L / ∂ w ) w new ​ =w old ​ −α(∂L/∂w) . By calculating the gradient, we determine the direction of steepest descent. In the first iteration (with learning rate α = 0.0004 α=0.0004 ), w 1 w 1 ​ drops from 3 to 0.57, and w 0 w 0 ​ adjusts to -2.06. Visually, the line "tilts" and "shifts" toward the data points. After 15,000 iterations, the model converges to w 1 ≈ 0.187 w 1 ​ ≈0.187 and w 0 ≈ − 0.057 w 0 ​ ≈−0.057 , resulting in the final model: y ^ = 0.187 x − 0.057 y ^ ​ =0.187x−0.057 . The core takeaway is "Physical Alignment": abstract math updates become a visible best-fit line. These twin pillars—optimization and fitting—form the foundational bedrock of all Artificial Intelligence. Master these, and you master the soul of the machine. visit our learning agent for quizzes and projects www.wisdomers.in