*Machine Learning Engineer Roadmap (Step-by-Step)*
(Bookmark this if you’re aiming for ML in 2025)
1. Math & Statistics
↳ Linear Algebra, Probability, Descriptive Stats
↳ Focus on what powers ML under the hood
2. Programming
↳ Python (your #1 language for ML)
↳ Write clean, modular, readable code
3. SQL & Databases
↳ Learn how to query data
↳ Understand relational and non-relational databases
4. Data Science Tools
↳ Anaconda, Jupyter Notebook, Google Colab
5. Data Science Libraries
↳ NumPy, Pandas, Matplotlib, Seaborn
6. Machine Learning Concepts
↳ Supervised vs Unsupervised
↳ Overfitting, Bias-Variance, Model Evaluation
7. ML Libraries
↳ Scikit-learn
↳ NLTK (for NLP)
↳ OpenCV (for computer vision)
8. Deep Learning Concepts
↳ Neural networks, backpropagation, activation functions
9. Deep Learning Frameworks
↳ TensorFlow, PyTorch
10. Real-World Projects
↳ Start with datasets on Kaggle
↳ Build your GitHub portfolio
11. Soft Skills
↳ Communication, problem-solving, teamwork
12. Resume + Job Prep
↳ Build your resume, apply with intent, keep learning
Master the path.
Build projects.
Practice.
Apply.
Repeat.
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