ML RoadMap

ML_roadmap.md

ML RoadMap

Stage 1: Foundations (1 Week)

1. Mathematics for Machine Learning (Applied Level)

1.1 Linear Algebra (for ML, not proofs)

Topics to read

• Scalars, Vectors, Matrices
• Matrix addition and multiplication
• Dot product
• Norms (L1, L2)
• Linear transformations
• Shapes of matrices
• Dense (Fully Connected) layer equation

Correct Reference

• Essence of Linear Algebra — 3Blue1Brown (not “shlu one brown”)

Checkpoint

• Understand this equation and shapes

y = W x + b

# Example:
# x : input vector of shape (3, 1)
# W : weight matrix of shape (1, 3)
# b : bias of shape (1,)

# Output y is a single number

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1.2 Probability & Statistics (Core Concepts)

Topics to read

• Random variables

• Mean, Median, Mode

• Variance, Standard Deviation

• Probability distributions

  • Bernoulli
  • Gaussian (Normal)
  • Categorical

• Bayes Theorem

• Correlation

• Histogram

• Confidence Interval

• Overfitting vs Underfitting

Corrections

• “barnoli” → Bernoulli
• “gshian” → Gaussian
• “base rule” → Bayes Rule
• “quantiz” → Quantiles

Checkpoint

# Example:
Marks = [40, 50, 60, 70, 80]

Mean = 60
Histogram shows how many students fall in each range

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1.3 Calculus (Very Lightweight)

Topics to read

• Derivative as rate of change
• Partial derivatives
• Gradient
• Chain rule
• Gradient Descent (intuition only)

Correct Reference

• Essence of Calculus — 3Blue1Brown

Checkpoint

# Example:
Loss = (prediction - actual)^2

Gradient descent:
Move weights in direction where loss reduces

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Stage 2: Programming (2–3 Weeks)

2. Python Programming

Topics to read

• Variables, data types
• Lists, tuples, dictionaries, sets
• Loops and conditions
• Functions
• Classes (basic OOP)
• File input/output
• Virtual environments

References

• Programming with Mosh
• freeCodeCamp (YouTube)

Example

data = [1, 2, 3, 4]
total = sum(data)

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3. Data & Scientific Python Stack

3.1 NumPy

• Arrays
• Indexing & slicing
• Broadcasting

3.2 Pandas

• DataFrame
• Filtering
• GroupBy
• Joins
• Missing values

3.3 Visualization

• Matplotlib (basic plots)

3.4 Deep Learning Tensor Library

• PyTorch tensors

Correction

• “Mattplot lily” → Matplotlib

Example

import pandas as pd

df = pd.DataFrame({
  "age": [20, 25, 30],
  "salary": [20000, 30000, 40000]
})

df[df["age"] > 22]

---

Stage 3: Core Machine Learning (1 Month)

4. Supervised Learning

4.1 Core Concepts

• Train / Test split
• Cross-validation
• Bias–Variance trade-off
• Overfitting vs Underfitting

4.2 Evaluation Metrics

Regression

• MSE
• MAE
• R²

Classification

• Accuracy
• Precision
• Recall
• F1-score
• ROC-AUC

---

4.3 Algorithms (Exact Order)

• Linear Regression
• Ridge Regression
• Lasso Regression
• Logistic Regression
• K-Nearest Neighbors
• Decision Trees
• Random Forest
• Gradient Boosted Trees
• XGBoost / LightGBM
• Support Vector Machines (conceptual)

Practice Rule

• Use scikit-learn
• Apply each on 2–3 datasets
• Tune hyperparameters
• Plot learning curves

Reference

• Machine Learning Specialization — Andrew Ng (Coursera)

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5. Unsupervised Learning

Topics

• K-Means Clustering
• PCA (Dimensionality Reduction)
• t-SNE (visualization only)
• UMAP (visualization only)

Focus

• When to use clustering vs classification
• PCA for preprocessing, not solving problems

Example

# Example:
Customer data without labels
Use K-Means to group similar customers

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6. Structured Mini Projects

Example Projects

• Loan default prediction
• Fraud detection

Steps

1. Define problem
2. Choose metric
3. EDA
4. Baseline model (Logistic / Linear)
5. Strong model (Random Forest / XGBoost)
6. Compare results
7. Reflect on why performance changed

Why

• Interviewers ask why model improved, not just accuracy

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Stage 4: Advanced ML & Deep Learning (2–3 Months)

7. Neural Networks & Deep Learning

Core Topics

• Perceptron
• Multi-Layer Perceptron (MLP)
• Activation functions (ReLU, Sigmoid)
• Loss functions (MSE, Cross Entropy)
• Backpropagation (conceptual)
• Gradient Descent, SGD, Adam
• Train / Validation / Test
• Regularization
• Dropout
• Early stopping

Framework

• PyTorch (recommended)

Correction

• “relig functions” → ReLU functions
• “atom” → Adam optimizer

Reference

• Deep Learning Specialization — Coursera

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8. Specialization Samplers (To Stand Out)

8.1 Computer Vision

• CNN
• Pretrained ResNet
• Image classification (Cats vs Dogs, CIFAR-10)

8.2 Natural Language Processing (NLP)

Topics

• Tokenization
• Embeddings
• RNN, LSTM (high level)
• Transformers
• Pretrained models (Hugging Face)

Tasks

• Sentiment analysis
• Text classification

Reference

• NLP course by IIT Professors (free)

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8.3 Time Series

Topics

• Lag features
• Rolling mean
• Time-aware train/test split

Example

Sales today depends on last 7 days sales

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Stage 5: Projects, Deployment & Visibility

9. Serious Project Portfolio (Most Important)

Rules

• 3 to 5 projects
• Clear problem
• Clear metric
• Baseline vs improved model
• Interpretability

Topics

• Feature importance
• SHAP values

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10. End-to-End ML System

Steps

• Train model
• Save model
• Load in API
• Deploy using Flask or FastAPI

Example

User sends input → API → Model → Prediction

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11. Show Your Work

Actions

• GitHub (organized repos)
• Blog posts / notebooks
• LinkedIn write-ups

Why

• Recruiters notice explanation, not certificates

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12. What to Avoid (Very Important)

• Watching too many courses without projects
• Waiting to “master math” before ML
• Jumping to GANs, RL, LLM fine-tuning too early
• Over-focusing on tools and frameworks

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Final Message from Transcript (Cleaned)

Fundamentals + Projects matter more than tools. Build, reflect, explain, and show your work.

If you want, next I can:

• Convert this into week-by-week plan
• Create project ideas with datasets
• Make interview preparation checklist