English Version
1. Learn the Core Math
Start with linear algebra, probability, and basic calculus. You need these topics to understand models. Keep the focus on vectors, matrices, derivatives, and distributions.
2. Learn Python
Python is the main language for machine learning. Learn variables, loops, functions, and modules. Then learn NumPy and Pandas. These libraries handle data and arrays.
3. Understand Data Handling
Learn how to load data, clean data, and prepare data. Learn missing values, encoding, normalization, and splitting. Clean data improves model results.
4. Start With Supervised Learning
Begin with simple models. Learn linear regression, logistic regression, decision trees, and k nearest neighbors. Build small projects and test them.
5. Learn Model Evaluation
Learn accuracy, precision, recall, F1, RMSE, and confusion matrices. Evaluation helps you understand model performance.
6. Move to Advanced Models
After the basics, study random forests, gradient boosting, SVMs, and neural networks. These models give stronger results for many tasks.
7. Practice With Real Projects
Pick small datasets. Build simple pipelines. Train, test, and evaluate. Improve the workflow step by step.
8. Learn ML Tools
Learn scikit learn first. Then explore TensorFlow and PyTorch. These tools help you build deep learning systems.
9. Read Research and Documentation
Read official docs for libraries. Follow updates and study tutorials. This keeps your skills fresh.
10. Stay Consistent
Set a clear schedule. Build often. Test ideas. Small steps push you forward in ML.
Moroccan Darija Version
1. Bda b Math l Asasiya
Bda b linear algebra, probability, w calculus. Hadi l core bach tfham models. Sir l vectors, matrices, derivatives, w distributions.
2. T3llam Python
Python hiya l language l msta3mla f ML. T3llam basics. B3d t3llam NumPy w Pandas bach t7km f data.
3. Fham Data Handling
T3llam kifash tloadi data, tnqqiha, w tjiheziha. Fham missing values, encoding, normalization, w splitting.
4. Bda b Supervised Learning
Bda b linear regression, logistic regression, decision trees, w k nearest neighbors. Diri small projects.
5. T3llam Evaluation
Fham accuracy, precision, recall, F1, RMSE, w confusion matrix. Hadi katwerik performance.
6. Zid l Models Mtwrin
B3d basics, qra random forests, gradient boosting, SVM, w neural networks.
7. Diri Projects 3lihom Data Bssita
Khddm 3la datasets sgharin. Sna3 pipeline. Train, test, w evaluate.
8. T3llam Tools dyal ML
Bda b scikit learn. B3d sir l TensorFlow w PyTorch.
9. Qra Docs w Tutorials
Tb3a docs dyal libraries. Qra examples w updates.
10. Dwam 3la tta3allom
Dir schedule. Khddm b niyma. Qder t3awd. L consistency katbni skills.