Skip to content

Latest commit

 

History

History
58 lines (48 loc) · 2.33 KB

README.md

File metadata and controls

58 lines (48 loc) · 2.33 KB
Raw

I taught Machine Learning to 250+ students through an online platform. These are the course materials I built for my students (Mostly graduate-level students from the Non-CS background).

Video Lectures: https://www.youtube.com/playlist?list=PLGjf1T0akmhN1B4YHB5PT42XmJwr9BpS6

Quiz: https://kawsar34.medium.com/list/machine-learning-interview-quiz-057db29497a4

Lecture 1: Difference between Supervised Learning & Unsupervised Learning

  • Defining Machine Learning: What can ML do?
  • How does ML work?
  • Machine Learning terminologies
  • Supervised Learning, Unsupervised Learning, Deep Learning

Lecture 2: Supervised Learning: Linear Regression

  • Supervised Learning: Linear Regression
  • train data, test data
  • Understanding the equation of a straight line
  • feature coefficient (slope, gradient, m)
  • bias coefficient (y-intercept, c)
  • domain: x-axis, independent variable
  • range: y-axis, dependent variable
  • loss function, cost function, objective function, error function
  • bias-variance tradeoff, overfitting, underfitting
  • ordinary least square method
  • gradient descent method
  • residual, error, squared error, RMSE - Root Mean Squared Error

Lecture 3: Supervised Learning: Linear Regression and Regression accuracy metrics

  • Supervised Learning: Linear Regression
  • Accuracy metric in Regression problem
  • Mean Absolute Error (MAE)
  • Mean Absolute Percentage Error (MAPE)
  • Mean Squared Error (MSE)
  • Root Mean Squared Error (RMSE)
  • R-squared or coefficient of determination
  • Prediction result evaluation

Lecture 4: Supervised Learning - Classification: Logistic Regression

  • Supervised Learning - Classification: Logistic Regression

Lecture 5: Supervised Learning - Classification: Accuracy Metrics

  • Supervised Learning - Classification: Logistic Regression
  • Confusion Matrix
  • Accuracy, Precision, Recall/Sensitivity/True Positive Rate, F1 score, False Positive Rate
  • ROC: Receiver Operating Characteristics and AUC: Area Under the Curve
  • Classification report

Lecture 6: Supervised Learning - Decision Tree

  • Decision Tree Classification and Regression

Lecture 7: Supervised Learning - Decision Tree, Cross-validation and Grid Search

  • Decision Tree Classification
  • Cross-Validation
  • Grid Search

Lecture 8: Unsupervised Learning - K-means Clustering

  • Unsupervised Learning
  • K-means Clustering
  • Elbow method