Machine Learning Engineer Roadmap

This roadmap is designed to guide you from a complete beginner to a job-ready Machine Learning Engineer. It focuses on practical engineering skills, avoiding unnecessary theoretical depth where it's not needed for daily work.

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Stage 1: Foundations (1 Week Only)

Goal: Learn just enough math to understand ML (not to become a mathematician)

Topic	Focus Areas	Best Resource
Linear Algebra	• Vectors • Matrices • Matrix multiplication • Dot products • Norms • Linear transformations • Eigenvalues (intuition)	Essence of Linear Algebra (3Blue1Brown)
Probability & Statistics	• Random variables • Expectation, variance • Distributions (Bernoulli, Gaussian, Categorical) • Mean, median, quantiles • Correlation • Bayes rule • Overfitting vs Underfitting • Confidence intervals	StatQuest with Josh Starmer (YouTube)
Calculus	• Derivatives as rate of change • Partial derivatives • Gradients • Chain rule	Essence of Calculus (3Blue1Brown)

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Stage 2: Programming (2–3 Weeks)

You cannot do machine learning without being a solid programmer.

Skill	Focus Areas	Resources
Python Basics	• Data types • Loops & conditionals • Functions • Classes (basic usage) • File I/O • Virtual environments	• Programming with Mosh (YouTube) • freeCodeCamp Python Course (YouTube)

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Stage 3: Data & Scientific Stack

Library	Key Concepts
NumPy	• Arrays • Broadcasting • Indexing
Pandas	• DataFrames • Filtering • GroupBy • Joins • Missing values
Matplotlib	• Basic plotting
PyTorch	• Tensors (basic understanding)

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Stage 4: Core Machine Learning (≈ 1 Month)

This is where most people mess up by jumping too fast into deep learning.

1. Core Concepts & Metrics

Category	Concepts to Master
Foundations	• Train / Test split • Cross-validation • Overfitting vs Underfitting • Bias–variance tradeoff
Regression Metrics	• MSE (Mean Squared Error) • MAE (Mean Absolute Error) • R² Score
Classification Metrics	• Accuracy • Precision • Recall • F1 Score • ROC-AUC

2. Algorithms (Supervised Learning)

Practice Rule: Use scikit-learn on 2–3 datasets per algorithm.

Algorithm	Focus
Linear Regression	Simple baseline for continuous values.
Ridge & Lasso	Regularization techinques.
Logistic Regression	Baseline for classification.
K-Nearest Neighbors	Instance-based learning.
Decision Trees	Interpretability and splitting logic.
Random Forest	Bagging ensemble method.
Gradient Boosted Trees	XGBoost / LightGBM (State of the art for tabular data).
SVM	Support Vector Machines (Conceptual understanding).

3. Unsupervised Learning (Basics)

Topic	Focus
Clustering	• K-Means clustering
Dimensionality Reduction	• PCA (for visualization & preprocessing) • t-SNE / UMAP (optional, visualization only)

Best Course: Machine Learning Specialization — Andrew Ng (Coursera)

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Stage 5: Structured Mini Projects

Use Kaggle datasets.

Project Workflow Table

Step	Action	Goal
1	Define Problem	clearly state the problem and the evaluation metric.
2	EDA	Perform Exploratory Data Analysis to understand data distribution.
3	Baseline Model	Train a simple model (Logistic/Linear Regression) to set a benchmark.
4	Strong Model	Train a complex model (Random Forest / XGBoost).
5	Compare	Evaluate results against the baseline.
6	Reflect	Ask specific questions: • Why did performance change? • Why is one model better suited? • How did hyperparameters affect results?

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Stage 6: Advanced ML & Deep Learning (2–3 Months)

Only start after you are comfortable with everything above.

Topic	Focus Concepts
Neural Network Foundations	• Perceptron & MLP • Activation functions (ReLU, etc.) • Loss functions (Cross-Entropy, MSE) • Backpropagation (conceptual) • Optimization (SGD, Adam) • Regularization (Dropout, Early stopping) • Train / Validation / Test splits
Frameworks	• PyTorch (Recommended) • Avoid over-optimizing framework choice.

Best Course: Deep Learning Specialization (Coursera)

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Stage 7: Specialization Samplers (To Stand Out)

Pick ONE track to do a mini-project in. These are resume differentiators.

Track	Key Concepts	Sample Tasks
Computer Vision	• CNNs • Pretrained ResNet	• Cats vs Dogs classifier • CIFAR-10 subset
NLP	• Tokenization & Embeddings • RNN / LSTM (high-level) • Transformers & LLMs • Hugging Face pretrained models	• Sentiment analysis • Text classification
Time Series	• Lag features • Rolling statistics • Time-aware splits • Regression-based forecasting	• Forecasting sales or stock trends

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Stage 8: Projects & Portfolio (Most Important)

If starting over, I would spend most of my time here. Build 3–5 serious projects.

Project Type	Description	Examples
Business-Style Problem	Solves a real-world business need.	• Churn prediction • Dynamic pricing model • Fraud detection
Interpretability Focus	Focuses on explaining why the model predicts what it does.	• Feature importance analysis • SHAP value explanations
End-to-End System	Full stack ML engineering.	• Train & save model • Serve via Flask/FastAPI • Frontend or script interface

Best Advice: Work on something you genuinely care about (Music, Sports, Finance, Climate, Game AI). Recruiters notice the passion.

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Stage 9: Show Your Work (Non-Negotiable)

Recruiters often reach out only after seeing public work.

Action	Details
Push to GitHub	Organize repos properly. Don't just upload random scripts.
Write Blogs/Notebooks	Documentation should cover: 1. What problem you solved 2. What worked 3. What didn’t 4. What you learned
LinkedIn Sharing	Share small write-ups and results.

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What to Avoid (Common Traps)

• ❌ Binging courses without building anything.
• ❌ Waiting to “master math” before starting ML.
• ❌ Jumping into GANs, RL, or LLM fine-tuning too early.
• ❌ Obsessing over tools, frameworks, or MLOps stacks.

Bottom Line: Fundamentals + Projects > Tools.