Machine Learning for Everybody – Full Course

Kylie Ying introduces machine learning for beginners, covering supervised and unsupervised models. The lesson starts with setting up a Google Colab notebook for a 'magic gamma telescope dataset' (UCI Machine Learning Repository). This dataset contains features of high-energy particles to classify them as gamma or hadron. The process involves downloading the data, loading it into a Pandas DataFrame, and converting categorical labels (G/H) into numerical (0/1) for machine processing. Initial data exploration and plotting histograms of features are performed to understand data distribution related to the class.

A crash course covering fundamental ML concepts. It defines AI, ML, and Data Science, differentiating their scopes. Three main types of machine learning are introduced: Supervised Learning (using labeled data for predictions, e.g., image classification), Unsupervised Learning (finding patterns in unlabeled data, e.g., clustering), and Reinforcement Learning (learning through rewards and penalties). The discussion then shifts to supervised learning model architecture, outlining inputs (feature vectors), types of features (qualitative/categorical, quantitative/numerical), and prediction types (classification/regression).

This section focuses on how to evaluate machine learning models. Using a diabetes dataset example, it explains features, labels, and the importance of splitting data into training, validation, and test sets to assess a model's generalization capability. Key concepts like 'loss' (the difference between prediction and true value) and 'training' (adjusting the model to minimize loss) are introduced. Various loss functions (L1, L2, Binary Cross-Entropy) and performance metrics (accuracy, precision, recall, F1-score) are explained using visual aids and definitions.

K-Nearest Neighbors (KNN), a supervised learning algorithm, is introduced. The concept is explained with a 2D plot (kids vs. income) to predict car ownership, highlighting Euclidean distance as the core mechanism. The 'K' in KNN represents the number of nearest neighbors considered for classification. The practical implementation of KNN using scikit-learn's `KNeighborsClassifier` is demonstrated, along with evaluating its performance using a classification report, accuracy, precision, recall, and F1-score for the telescope dataset.

This part covers Naive Bayes, a classification algorithm based on Bayes' Theorem. The explanation begins with conditional probability using a COVID test example, then expands to Bayes' rule for classification. Key terminology like 'posterior,' 'likelihood,' 'prior,' and 'evidence' are defined. The 'naive' assumption of feature independence is crucial. The implementation of Gaussian Naive Bayes from scikit-learn is shown, and its performance is evaluated and compared to KNN, typically showing lower accuracy for this particular dataset.

Logistic Regression, despite its name, is a classification algorithm. The concept starts by illustrating why linear regression is unsuitable for binary classification due to probability constraints (0-1 range). The solution involves using the 'odds ratio' and its logarithm (the log-odds), which maps to the linear model. This leads to the sigmoid function, which squashes the output between 0 and 1, representing probabilities. Simple and multiple logistic regression are discussed. Its implementation using scikit-learn's `LogisticRegression` is demonstrated and its performance is evaluated, showing it often performs better than Naive Bayes but not as well as KNN or SVM for the telescope dataset.

Support Vector Machines (SVMs) are introduced as a powerful classification model. The core idea is to find a 'hyperplane' that best separates different classes in a feature space, maximizing the 'margin' between the closest data points (support vectors) of each class. The concept of the 'kernel trick' is explained for handling non-linearly separable data by transforming it into higher dimensions. Practical implementation of `SVC` (Support Vector Classifier) from scikit-learn is shown, demonstrating its superior performance (highest accuracy so far) on the telescope dataset compared to previous models.

The section moves to Neural Networks (NNs). The basic structure of a neuron (inputs, weights, bias, activation function) and a neural network (input, hidden, output layers) is illustrated. The importance of non-linear activation functions (sigmoid, tanh, ReLU) is emphasized to prevent the network from collapsing into a linear model. Backpropagation and gradient descent are briefly explained as the mechanism for training the network and adjusting weights. TensorFlow is introduced as a library for building and training NNs. A multi-layer perceptron with dense layers and ReLU activation, and a sigmoid output for classification, is implemented. Overfitting prevention with dropout layers and hyperparameter tuning through grid search are also covered. The NN's performance is compared to previous models.

Transitioning to regression, the second type of supervised learning, this part explains how to predict continuous numerical values. Linear regression is introduced as a method to fit a 'line of best fit' to data. The concept of 'residuals' (errors) and how linear regression minimizes the sum of squared residuals is explained. Assumptions for linear regression (linearity, independence, normality, homoscedasticity) are covered, along with visual interpretations. Evaluation metrics for regression are detailed: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (coefficient of determination), explaining their calculations and practical interpretations.

This segment focuses on practical regression using a bike-sharing dataset from Seoul. Data setup involves downloading, loading into Pandas, cleaning, and selecting relevant features (e.g., temperature). Initial plotting shows potential linear relationships. Simple linear regression (using only temperature to predict bike count) and multiple linear regression (using multiple features) are implemented with scikit-learn. The R-squared score is used to evaluate performance. The use of a simple neural network (one dense layer with linear activation) with TensorFlow for linear regression is also demonstrated, and its loss is plotted over epochs. This highlights how both traditional linear models and neural networks can approach regression problems.

The discussion shifts to unsupervised learning, where data lacks labels. K-Means Clustering is introduced as an algorithm to find 'k' clusters in unlabeled data. The step-by-step process of K-Means is thoroughly explained: random centroid initialization, assigning points to the closest centroid, and recalculating centroids, iteratively, until convergence (expectation-maximization). The implementation of K-Means from scikit-learn is demonstrated using a 'seeds' dataset (wheat kernel features), and the results are visualized and compared to actual (hidden) class labels to assess clustering effectiveness.

Principal Component Analysis (PCA) is presented as a dimensionality reduction technique. Its main goal is to reduce the number of features while retaining the most important information, finding directions of largest variance in the data. The concept is illustrated by projecting multi-dimensional data onto a single principal component. PCA aims to either maximize the variance among points along a new axis or minimize the 'projection residuals' (the distance from the original point to its projection). PCA implementation using scikit-learn for the seeds dataset is shown, reducing its dimensions from seven to two, and the new 2D representation is used for visualization and further clustering, demonstrating how PCA can improve cluster separation.