Machine_Learning_intro

:) سلام دوستان

This is the material used in my free Persian course: Machine Learning with Python (available on YouTube).

This 2 hours long course offers a practical introduction into Machine Learning with Python. this course is for you if you are familiar with data analytics libraries in Python (Pandas, NumPy, Matplotlib) and you are looking for a hands-on introduction to Machine Learning. After finishing this course, you will grasp the basic concepts in Machine Learning and be able to use its techniques on any data with Scikit-Learn, the most commonly used library for Machine Learning in Python.

Note

Oddly, the notebook cells are horizontally aligned when rendered on GitHub. I haven't found the solution to this problem unfortunately. However, they are correctly aligned when rendered on Jupyter, so I recommend downloading the notebook files and opening them with Jupyter or Colab or similar IDEs.

Topics covered:

Intro_to_ML_1:

• 1:
  • What is Machine Learning?
  • Types of Machine Learning
  • Types of Supervised Learning
• 2.1:
  • Types of Regression
  • Simple Linear Regression
• 2.2:
  • Model Evaluation in Regression
  • Overfitting
  • Train/test split
  • Cross-Validation
  • Accuracy Metrics for Regression
  • Simple Linear Regression with Python
• 2.3:
  • Multiple Linear Regression with Python
  • Polynomial Regression with Python
• 2.4:
  • Regularization
  • Ridge Regression with Python
  • Lasso Regression with Python

Intro_to_ML_2:

• 3.1:
  • Types of Classification
  • K-nearest neighbors (KNN)
• 3.2:
  • Evaluation metrics in Classification
  • Confusion Matrix
  • KNN with Python
• 3.3:
  • Decision Trees with Python
  • Logistic Regression with Python
  • Support Vector Machines (SVM) with Python
• 3.4:
  • Neural Networks
  • Perceptron with Python
  • Multi-Layer Perceptron (MLP) with Python

Intro_to_ML_3:

• 4:
  • Why reduce dimensionality?
  • Feature Selection with Python
  • Feature Extraction with Python

Contact

Feel free to email me your questions here: [email protected]

Material gathered, created, and taught by Yara Mohamadi.