Machine Learning Roadmap
Introduction
This roadmap gives simple steps to learn Machine Learning. It guides beginners and students through clear stages from basics to projects. Next, you move step by step with practical actions.
بهاد ال roadmap غادي تبدا ف طريق machine learning ب خطوات واضحين. خطوة ب خطوة حتى توصل لمستوى زوين.
1. Learn the Basics
Start with the core ideas. Build a strong base.
- Understand Machine Learning
- Know supervised, unsupervised, reinforcement learning
- Know terms like model, dataset, training, inference
بدا ب الأساسيات. فهم شنو هو machine learning وشنو كيعني تدريب موديل و inference. فهم supervised o unsupervised o reinforcement.
2. Build Math Skills
Math supports every ML model. Learn the parts you need.
- Linear algebra: vectors, matrices, operations
- Calculus: derivatives, gradients
- Probability: distributions and random variables
- Statistics: mean, variance, correlation
الرياضيات ضرورية. تعلم matrices o vectors o gradients o الاحصائيات باش تبني موديلات قوية.
3. Learn Python
Python drives Machine Learning. Focus on simple and clean code.
تعلم Python. استعمل NumPy o Pandas ودر تمارين بزاف باش تولف data manipulation.
4. Learn Data Handling
Good data improves model performance.
- Clean datasets
- Fix missing values
- Normalize values
- Split data for training and testing
ال data خاصها تكون نقية. صلح القيم ناقصين. نورماليز القيم. قسم data ل train و test.
5. Learn Core Algorithms
Study classic Machine Learning models.
- Linear regression
- Logistic regression
- Decision trees
- Random forests
- K nearest neighbors
- Support vector machines
- K means clustering
تعلم algorithms بحال regression o trees o SVM o K-means.
6. Learn Model Evaluation
Measure how your model performs.
- Accuracy
- Precision
- Recall
- F1 score
- Confusion matrix
- Cross validation
قيم الموديل ب accuracy o precision o recall o confusion matrix.
7. Learn Deep Learning
Move to neural networks once your basics are ready.
- Understand neurons and layers
- Learn activation functions
- Study backpropagation
- Use TensorFlow and PyTorch
- Train simple neural networks
ملي تفهم ML دخل ل deep learning. تعلم layers o activations o backpropagation واستعمل PyTorch ولا TensorFlow.
8. Build Projects
Projects build skill and confidence.
- Image classification
- Sentiment analysis
- Recommendation systems
- Time series forecasting
دير مشاريع عملية بحال image classification ولا sentiment analysis باش تقوى فعلاً.
9. Learn MLOps Basics
Deploy and manage your models.
- APIs
- Model versioning
- Monitoring
باش تخدم ف الواقع خصك تدير deployment و monitoring.
10. Stay Updated
ML changes fast. Keep learning new tools.
- Read new research papers
- Follow developer blogs
- Test new tools
machine learning كيتطور بزاف. تبع papers o blogs o tools جداد.
Syntax or Model Structure Example
Below is a simple Python example for training a model.
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
data = pd.read_csv("data.csv")
X = data[["feature1", "feature2"]]
y = data["target"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = LinearRegression()
model.fit(X_train, y_train)
score = model.score(X_test, y_test)
print("Model score:", score)
هادا مثال بسيط باش تربّي model ب LinearRegression.
Exercises
- Define supervised learning in one short sentence.
- Explain the role of gradients.
- Write a small Python script that loads a CSV file.
- Create a NumPy array and compute its mean.
- Train a decision tree on any small dataset.
- List three activation functions.
- Plot a confusion matrix for any model.
- Do a train test split with different ratios.
- Train a simple neural network with PyTorch.
- Deploy a small model with a local API.
دابا حاول تجاوب على هاد التمارين. غادي تعاونك تبني أساس قوي.