Machine Learning for Beginner (AI) - Data Science

Machine learning is a subset of artificial intelligence that mimics human behavior and uses statistical tools, training and testing data to understand, analyze, and predict new data.

Train a model with thousands of labeled images to recognize dog and cat photos, as traditional programming struggles with edges and angles, and improve accuracy by training and testing.

Clarify how data science underpins artificial intelligence, machine learning, and deep learning, with deep learning as a subset of machine learning, and outline supervised, unsupervised, semi supervised, and transfer learning.

Explore the daily life applications of machine learning, from language processing and spam filtering to self-driving cars, medical robotics, and weather and stock market forecasting.

Explore the three types of machine learning: supervised learning uses past labeled data to train models, unsupervised learning finds patterns without labels, and reinforcement learning improves performance through feedback.

Learn supervised learning with labeled data and training-test cycles, unsupervised learning with clustering and no strict labels, and reinforcement learning driven by feedback to improve performance.

Compare two common model evaluation methods: training and testing on a dataset, and careful cross-validation for limited data, a resampling approach that splits data into groups to estimate performance.

Explore six famous machine learning classifiers, including support vector machine, decision tree, logistic regression, k-nearest neighbors, and random forest, with Python-based explanations across separate sections.

Follow eight steps—from loading and cleaning data in Python to splitting into training and test sets, training, predicting, and evaluating with accuracy, precision, and a confusion matrix.

Learn how principal component analysis reduces dimensionality by transforming large datasets into smaller, easier to explore components for visualization and machine learning.

Explore how principal component analysis reduces dataset dimensions by projecting data onto principal axes, transforming many variables into fewer components for easier visualization and analysis.

Explore the mathematical foundations of principal component analysis, including how the covariance matrix captures relationships between variables, and how principal component analysis reduces the Luz dataset's dimensions.

Derive and interpret a covariance matrix for PCA using five data points for x and y, calculating means, standard deviations, and feature relationships to reveal principal components.

Explore the confusion matrix and how it links predicted and actual values, including true and false positives and negatives. Compute accuracy, error rate, precision, and recall.

Explore how accuracy and error rate measure a machine learning model's performance, and how confusion metrics help compute these metrics.

Explain precision and recall as measures of positive-class prediction, highlighting true positives, predicted positives, and actual positives, and relate them to the confusion matrix and accuracy.

Visualize how too much data and excessive feature specification lead to overfitting in a machine model, while insufficient data causes underfitting, using graphical examples of linear and nonlinear relationships.

Explore how machine learning classifies data using training sets and labels to predict classes, and use regression with continuous values to forecast outcomes like risk assessments.

Explore how regression predicts continuous outputs, unlike classification that yields binary zeros and ones. Examine simple and multiple linear regression, their equations, and how coefficients determine feature importance.

Learn how linear regression uses regression coefficients as weights to measure feature importance, with higher values indicating greater influence, shown by a straight-line relation and a graphical decision boundary.

Explore nonlinear regression and logistic regression, a sigmoid function mapping data to probabilities with a 0.5 threshold and categorical yes/no predictions.

Learn how a support vector machine, a supervised learning classifier, separates data into two classes using a decision boundary based on support vectors and kernel tricks for high dimensions.

The lecture explains how a support vector machine trains on labeled rectangle and triangle data, builds a model, and makes predictions on new data based on past labeled examples.

Explore linear and nonlinear support vector machines and how a maximum-margin hyperplane, touching support vectors, separates data in linear and non-linear forms.

Explore how the optimal hyperplane in a linear support vector machine achieves the maximum margin between support vectors, illustrated by comparing two models where M2 > M1 signals optimality.

See how a linear support vector machine predicts new data and classifies it into rectangle or triangle using a trained hyperplane.

Explore non-linear support vector machines and why linear SVM fails on non-separable data; map data from 2D to higher dimensions using the col technique to achieve separation.

Explore the kernel technique for nonlinear SVMs by mapping data from low to high dimensions and using linear, polynomial, and radial basis kernels to separate nonlinear data.

Apply the kernel trick to transform nonlinear one-dimensional data into two dimensions, enabling a nonlinear support vector machine to separate classes with a clear decision boundary.

Explore how the kernel trick converts nonlinear two-dimensional data into three dimensions, enabling a linear separating plane in a nonlinear support vector machine.

Explore daily life applications of the support vector machine, including text categorization, image classification, face detection, handwritten text detection, and biometrics.

Develop a Python breast cancer data project using a nonlinear support vector machine, loading built-in data, performing train-test split, fitting the model, and evaluating with accuracy, precision, and classification report.

Explore the k nearest neighbors classifier, how it uses nearby data points for classification, and how to choose k for accurate results without misplacing locality, with a Python project.

Explore how the K nearest neighbors (KNN) classifier works using a graphical, two-class dataset to classify a new data point by the closest points and distances.

Learn how k-nearest neighbors classifies new data by using the three closest examples and majority voting, with any object possible as long as labeled classes exist, unlike SVM's decision boundary.

Discover how to choose an accurate k value in k-nearest neighbors, balancing stability and accuracy with nearest-neighbor counts and cross-pollination techniques for reliable classification.

Explore how outliers affect KNN classification, using a two-class example to show how distant points can misclassify new data and destabilize the model.

Explore how to measure distances between data points using Euclidean distance, compare it with Manhattan distance, and apply these distances in a k-nearest neighbors classification.

Explore how to apply the Manhattan distance to measure the distance from data points of a class to a new data point, and compare it with Euclidean distance, illustrated graphically.

Learn to implement a knn classifier in python using pandas, numpy, and scikit-learn. Train-test split, standardization, model training, prediction, and evaluation with confusion matrix and classification report.

Learn how the naive Bayes classifier applies Bayes rule to probabilistic classification, using evidence to compute the probability of outcomes.

Apply the naive bayes rule to a daily life example, showing how distance as evidence informs the probability of an outcome and simple classification.

Apply Bayes rule to classify a person’s age and height using a naive Bayes approach, computing conditional probabilities to predict old, young, or child.

Develop and evaluate a naive Bayes text classifier in Python using 20 newsgroups data. Load data, tokenize, extract features, build a pipeline, train, predict, and assess with a confusion matrix.

Explore logistic regression as a sigmoid-based classifier with a 0–1 range and a 0.5 decision threshold, and implement a Python model for binary classification.

Explore a two-dimensional logistic regression classifier with a sigmoid decision boundary at 0.5. See how it separates class one and class two using probabilities from 0 to 1.

Apply nonlinear logistic regression, also called the sigmoid function, to classify nonlinear two-dimensional data by drawing an s-shaped decision boundary with a 0.5 threshold, separating into two classes.

Explore how logistic regression applies to daily life, from spam detection and disease diagnosis to fraud detection, alert systems, and weather forecasting, using past labeled data and the sigmoid function.

Develop a logistic regression model in Python using the iris dataset, train with feature data, predict and evaluate results, and visualize the sigmoid probability boundary with Matplotlib.

Explore how a decision tree classifier works with the family-tree analogy, where each node has at most two children, and why fewer features and less data keep the model simple.

Explore the black diagram of a decision tree and learn how a binary structure uses true and false branches, with at most two children per node, to model decisions.

This lecture walks through building a decision tree classifier in Python using pandas and scikit-learn, from loading data and preparing features to training, predicting, and evaluating accuracy.