COURSE 1

Supervised Machine Learning : Regression and Classification

Project Details

  • Date: Fall 2023
  • Subject: Machine Learning
  • Lecturer: Théo Gachet, MSc student
  • GitHub: Course Repository

This course provides a comprehensive introduction to machine learning, starting with its basic concepts and applications, moving through different types of regression, and culminating with classification and handling overfitting. Participants will engage in hands-on labs using Python, Jupyter Notebooks, and scikit-learn, ensuring a balance of theoretical understanding and practical skills. Through quizzes and interactive labs, the course ensures a robust understanding of machine learning fundamentals, essential for anyone looking to delve into this field.


WEEK 1

Introduction to Machine Learning




Overview of Machine Learning

  • Welcome to Machine Learning
  • Applications of Machine Learning

    • Supervised vs. Unsupervised Machine Learning

  • What is Machine Learning ?
  • Supervised Learning
  • Unsupervised Learning
  • Jupyter Notebooks
  • Lab : Python and Jupyter Notebooks
  • Quizz

    • Regression Model

  • Linear regression model
  • Lab : Model representation
  • Cost function formula
  • Cost function intuition
  • Visualizing the cost function
  • Visualization examples
  • Lab : Cost function
  • Quizz

    • Train the model with gradient descent

  • Gradient descent
  • Implementing gradient descent
  • Gradient descent intuition
  • Learning rate
  • Gradient descent for linear regression
  • Running gradient descent
  • Lab : Gradient descent
  • Quizz


    • WEEK 2

    Regression with multiple input variables




    Multiple linear regression

  • Multiple features
  • Vectorization
  • Lab : Python, NumPy and vectorization
  • Gradient descent for multiple linear regression
  • Lab : Multiple linear regression
  • Quizz

    • Gradient descent in practice

  • Frature scaling
  • Checking gradient descent for convergence
  • Choosing the learning rate
  • Lab : feature scaling and learning rate
  • Feature engineering
  • Polynomial regression
  • Lab : Feature engineering and polynomial regression
  • Lab : Linear regression with scikit-learn
  • Lab : Linear regression
  • Quizz


    • WEEK 3

    Classification




    Classification with logistic regression

  • Motivations
  • Lab : Classification
  • Logistic regression
  • Lab : Sigmoid function and logistics
  • Decision boundary
  • Lab : Decision boundary
  • Quizz

    • Cost function for logistic regression

  • Cost function for logistic regression
  • Lab : Logistic loss
  • Simplified cost function for logistic regression
  • Lab : cost function for logistic regression
  • Quizz

    • Gradient descen for logistic regression

  • Gradient Descent implementation
  • Lab : Gradient descent for logistic regression
  • Lab : Logistic regression with scikit-learn
  • Quizz

    • The problem of overfitting

  • The problem of overfitting
  • Addressing overfitting
  • Lab : Overfitting
  • Cost function with regularization
  • Regularized linear regression
  • Regularized logistic regression
  • Lab : Regularization
  • Lab : Logistic regression
  • Quizz


    • © 2023 Théo Gachet