Machine Learning for Everyone by Kylie Ying

Introduction

• Kylie Ying: Physicist, engineer, worked at MIT, CERN, and Free Code Camp
• Aim: Teach machine learning for absolute beginners
• Topics covered: Supervised and unsupervised learning models, logic/math behind them, and programming in Google Colab

Dataset Overview

• Source: UCI machine learning repository
• Example Dataset: Magic Gamma Telescope Dataset
  • Contains high-energy particle data (gamma particles, hadrons)
  • Attributes: Length, width, size, asymmetry, etc.
• Preparation Steps:
  • Download dataset and load it into Google Colab
  • Draw and drop the file into the Colab environment
  • Read CSV using pandas and assign column labels

Concepts Explained

Machine Learning Overview

• Sub-domain of computer science focusing on algorithms that enable computers to learn from data without explicit programming
• AI vs ML vs Data Science:
  • AI: Enable machines to perform human-like tasks
  • ML: Subset of AI, make predictions using data
  • Data Science: Find patterns and insights from data, can involve ML

Types of Machine Learning

1. Supervised Learning

• Uses labeled inputs to train models
• Example: Image classification (cat, dog, lizard)

2. Unsupervised Learning

• Uses unlabeled data to find patterns
• Example: Clustering similar images together

3. Reinforcement Learning

• Agent learns in an interactive environment based on rewards and penalties
• Example: Training a dog using rewards

Supervised Learning

Basics

• Model: Input features -> Model -> Output prediction
• Features: Variables that help in prediction
  • Qualitative (Categorical): Gender, nationality
  • Quantitative: Length, temperature (Discrete or Continuous)
• Predictions:
  • Classification: Predict discrete classes (binary or multi-class)
  • Regression: Predict continuous values (e.g., house prices)

Evaluation Metrics

• Training, Validation, and Testing datasets
• Loss functions: Measure error between prediction and actual value
  • Types: L1 Loss, L2 Loss, Binary Cross Entropy Loss
• Accuracy and other performance metrics

Example - Magic Gamma Telescope Dataset

• Loading and preparing data with pandas
• Visualizing and analyzing data
• Splitting data into training, validation, and test sets
• Scaling and oversampling datasets to balance class distribution
• Implementing various models: KNN, Naive Bayes, Logistic Regression, SVM, Neural Networks
  • Evaluation using metrics like accuracy, precision, recall, F1 score

Models Implementations

K-Nearest Neighbors (KNN)

• Organize data points into clusters based on distance
• Implemented using KNeighborsClassifier from sklearn

Naive Bayes

• Based on Bayes' theorem
• Assumes independence between features
• Implemented using GaussianNB from sklearn

Logistic Regression

• Predicts probability of a class
• Uses the sigmoid function
• Implemented using LogisticRegression from sklearn

Support Vector Machines (SVM)

• Finds the hyperplane that best separates classes
• Uses SVC from sklearn

Neural Networks

• Incorporate multiple layers to learn from data
• Non-linear transformation of features
• Implemented using TensorFlow's Keras API

Regression

• Linear Regression: Predicts continuous values
• Assumptions: Linearity, Independence, Normality, Homoscedasticity
• Evaluation Metrics: MAE, MSE, RMSE, R²
• Example: Bike sharing dataset
• Implemented Linear Regression and Neural Networks in TensorFlow

Unsupervised Learning

K-Means Clustering

• Clusters data points into K groups based on similarity
• Implemented using KMeans from sklearn

Principal Component Analysis (PCA)

• Reduces dimensions while preserving variance
• Example: Wheat kernels dataset
• Implemented using PCA from sklearn

Conclusion

• Successful coverage of machine learning models in supervised and unsupervised learning
• Practical implementation with datasets in Google Colab
• Invitation to the community for feedback and collaborative learning