Dear Asmaa Hammam Alfayed

This roadmap is designed to guide you through the field in a balanced way, combining key theoretical concepts with hands-on practice to help you build a solid foundation and grow confidently.

1. Build Foundational Mathematical & Computational Skills

Linear Algebra: Matrix operations, eigenvectors, SVD (critical for ML algorithms like PCA, deep learning).

Probability & Statistics: Distributions, Bayes’ theorem, statistical inference (essential for probabilistic models).

Calculus: Gradients, optimization (needed for training models).

Programming: Python is the lingua franca of ML. Focus on:

NumPy (numerical computing), Pandas (data manipulation), Matplotlib/Seaborn (visualization).

· Start with Python for Data Science(https://www.learnpython.org/).

2. Learn Core Machine Learning Concepts

Begin with supervised learning (regression, classification) and unsupervised learning (clustering, dimensionality reduction). Key topics:

Classical Algorithms: Linear Regression, Decision Trees, SVM, k-NN, Random Forests.

Model Evaluation: Cross-validation, metrics (accuracy, precision-recall, ROC curves).

Resources:

Book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Aurélien Géro): practical focus.

Course: Andrew Ng’s [ML Coursera](https://www.coursera.org/learn/machine-learning) (theoretical grounding).

3. Dive into Deep Learning (Recommended)

Key steps:

Neural Networks Basics: Perceptrons, backpropagation, activation functions.

Frameworks: TensorFlow/PyTorch (PyTorch is more intuitive for researchers).

· Specialized Areas:

· CNNs (for image/video data).

· Transformers (if interested in NLP or symbolic data).

· Resource: Deep Learning (Ian Goodfellow) or [Fast.ai](https://course.fast.ai/) (practical approach).

4. Work on Projects

Apply knowledge to real problems:

· Start with datasets on Kaggle ([Titanic](https://www.kaggle.com/c/titanic), MNIST).

· Reproduce papers or implement ML in your research (e.g., particle classification, signal processing).

· Use GitHub to document code (e.g., Jupyter notebooks).

5. Leverage Physics-Specific Resources

Literature: Review papers on ML in high-energy physics, cosmology, or quantum computing.

· Tools:

o Scikit-learn (https://scikit-learn.org/) (classical ML).

o PyTorch Geometric (https://pytorch-geometric.readthedocs.io/) (for graph-based data).

o Communities: Join ML groups in your field (e.g., ML4Science (https://ml4sci.org/)).

6. Stay Updated & Collaborate

· Follow conferences (NeurIPS, ICML) and arXiv ([cs.LG](https://arxiv.org/)).

· Engage with peers on ResearchGate, GitHub, or ML forums.

Best regards, Ahmed Ali, PIEAS, ISLAMABAD

kaggle and github

In addition to the recommendations already given, there are excellent courses available on Freecodecamp, Coursera, Udacity, EDX, and Kaggle. You can also learn a lot from sklearn, and there are excellent instructional videos on YouTube.