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ML Engineer Roadmap

A practical roadmap focused on the hands-on skills ML engineers use daily — data preparation, classical ML, deep learning, and model evaluation.

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Junior
01
Python
0/12
- Basic Syntax
- Variables & Data Types
- Conditionals
- Loops
- Functions
- Data Structures
- Exceptions
- OOP
- Essential libraries
  - NumPy
  - Pandas
  - Matplotlib
  - Seaborn
02
Mathematical Foundations
0/15
- Linear Algebra
  - Scalars/Vectors/Tensors
  - Matrix Operations
  - Determinants/Inverse
  - Eigenvalues/Diagonalization
  - SVD
- Calculus
  - Derivatives/Partial Derivatives
  - Chain Rule
  - Gradient/Jacobian/Hessian
- Statistics & Probability
  - Descriptive
  - Inferential
  - Probability Basics
  - Distributions
  - Random Variables/PDFs
  - Bayes Theorem
  - Graphs & Charts
03
Data Sources & Collection
0/2
- Databases (SQL/NoSQL)
- Internet APIs
04
Data Formats
0/4
- CSV
- JSON
- Parquet
- Excel
05
Data Cleaning & Preprocessing
0/5
- Data Cleaning
- Feature Engineering
- Feature Selection
- Feature Scaling & Normalization
- Dimensionality Reduction
Mid-Level
06
Types of Machine Learning
0/13
- What is ML?
- Supervised
  - Regression
    - Linear
    - Polynomial
  - Classification
    - Logistic Regression
    - Decision Trees/Random Forest
    - SVM
    - KNN
    - Gradient Boosting
- Unsupervised
  - Clustering
    - Exclusive
    - Hierarchical
    - Probabilistic
  - Dimensionality Reduction
    - PCA
    - Autoencoders
07
Scikit-learn
0/3
- Lasso
- Ridge
- ElasticNet Regularization
08
Model Evaluation
0/8
- Accuracy
- Precision
- Recall
- F1-Score
- ROC-AUC
- Log Loss
- Confusion Matrix
- K-Fold Cross Validation
09
Neural Network Basics
0/5
- Perceptron/MLP
- Forward Propagation
- Back propagation
- Activation Functions
- Loss Functions
Senior
10
Deep Learning Libraries
0/7
- PyTorch
- TensorFlow/Keras
- Workflow
  - Data Loading
  - Preparation
  - Model Selection
  - Tuning
  - Prediction
11
Deep Learning Architectures
0/14
- CNNs
  - Convolution
  - Padding
  - Strides
  - Pooling
  - Applications
    - Image Classification
    - Segmentation
    - Recognition
    - Recommendation Systems
- RNNs
  - RNN
  - LSTM
  - GRU
  - Attention Mechanisms
- Transformers
  - Self-Attention
  - Multi-head Attention
12
Advanced Concepts
0/5
- NLP
  - Tokenization
  - Stemming
  - Lemmatization
  - Embeddings
  - Attention Models

Project Checklist

A reference to follow when implementing a real network project.

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Problem Definition0/4

Problem type identified (classification, regression, or clustering)
Success metric agreed and tied to a business objective
Baseline performance established (rule-based or random predictor)
Data availability and labeling cost confirmed before starting

Data Preparation0/6

Train / validation / test split done before any transformation (no leakage)
Missing values handled with a documented strategy (drop, impute, or flag)
Categorical features encoded (label, one-hot, or target encoding)
Numerical features scaled where the algorithm requires it
Class imbalance assessed and addressed (oversampling, class weights, etc.)
All preprocessing steps wrapped in a reproducible sklearn Pipeline

Modeling0/4

Multiple algorithms compared on the same validation set
Hyperparameters tuned with cross-validation (grid, random, or Bayesian search)
Overfitting diagnosed by comparing train vs. validation metric
Feature importances reviewed and low-signal features removed

Evaluation0/4

Final metrics measured on held-out test set — not the validation set
Confusion matrix and ROC-AUC reviewed for classification models
Error analysis done on the worst-performing samples
Model behavior on edge cases and out-of-distribution inputs checked

Packaging0/4

Preprocessing pipeline and model serialized together (joblib or pickle)
Model version and training data snapshot recorded in a registry
Inference script tested end-to-end on a clean environment
Dependencies pinned in requirements.txt or pyproject.toml

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