Programmer to ML Roadmap & Concept Laboratory

2D Decision Boundary Playground Live Vector Plane

Preset:

Visitor Action: Click anywhere on the plane to drop a data point or drag existing points. Drag the Gradient Descent scrubber to update weights $(w_1, w_2, b)$ in real-time.

Class 1: High Intent / Purchase ($y=+1$)

Class 0: Bounce / Low Intent ($y=-1$)

Boundary: $w_1 x_1 + w_2 x_2 + b = 0$

Model Accuracy

94.5%

Loss (BCE)

0.142

Weight Vector (w)

[1.42, 0.88]

Bias (b)

-1.15

GD Iteration Step: Step 45

Learning Rate ($\alpha$): 0.10

Programmer Code (Vectorized / Loops)

# Python / NumPy vectorized dot product
def predict(X, W, b):
    # X shape: (N, 2), W shape: (2, 1)
    z = np.dot(X, W) + b
    return 1 / (1 + np.exp(-z))

Mathematical Formulation

Hypothesis Function: $\hat{y} = \sigma(\mathbf{w}^T \mathbf{x} + b)$ $\sigma(z) = \frac{1}{1 + e^{-z}}$ Inner product computes projection onto separating hyperplane.

12-Week Programmer-to-ML Applied Roadmap 4 Pillars

Progress: 100%

Your Background:

Hours / Week:

Total Effort: 120 hrs

Pillar 1: Linear Algebra & Vector Spaces

Weeks 1–3 (30h)

Vectors, matrix transformations, dot products, projection, eigenvalues/eigenvectors, and singular value decomposition (SVD) intuition for embeddings.

Pillar 2: Calculus & Gradient Descent

Weeks 4–6 (30h)

Derivatives, partial gradients, multivariable chain rule, learning rate schedule, backpropagation formulation, and loss function landscapes.

Pillar 3: Probability & Inferential Statistics

Weeks 7–9 (30h)

Random variables, probability density, Bayes' theorem, maximum likelihood estimation (MLE), hypothesis testing, and confidence intervals.

Pillar 4: Practical Scikit-Learn Pipelines & Deployment

Weeks 10–12 (30h)

Feature engineering, cross-validation, hyperparameter tuning with GridSearchCV, end-to-end classification pipeline, and model serialization (ONNX/joblib).

Weekly Practical Project Deliverables:

W1-3 Project: Build a custom 2D vector matrix multiplication engine from scratch without NumPy.
W4-6 Project: Code mini-batch gradient descent optimizer and visualize loss curve convergence.
W7-9 Project: Spam filter using Naive Bayes classifier & log-likelihood scoring.
W10-12 Project: Full production customer churn / purchase intent predictor deployed via FastAPI.