Artificial Intelligence and Machine Learning: Complete Guide

Learn intelligent search algorithms by defining problems and representations, using a city map to find the best routes with greedy search and A star search, and explore related data structures.

Explore implementing a sorted array in Python with a class named Ordered Vector, managing capacity and inserting elements in order by shifting values, demonstrated in a Google Colab notebook.

Build a city map for intelligent search by modeling cities as vertices with adjacency and heuristic distances, then use an ordered array to explore with greedy search.

Explore the intuition of greedy search, using straight-line distance as a heuristic to find the shortest path from Arad to Bucharest by expanding the closest city first.

Implement a star search in a Google Colab notebook, using a star distance (heuristic plus road cost) and an ordered adjacency structure to navigate from Arad to Bucharest.

Represent the problem for optimization by converting flight data into a Python dictionary that captures origin, destination, depart, arrival, and price.

Introduces hill climb algorithm, starting from a random solution and moving to the best neighbor to minimize cost, and demonstrates local vs global optima, applicable to minimization and maximization.

Learn the basics of genetic algorithms, including initial population, individuals, chromosomes, and genes, and how evaluation and stopping criteria guide selection, crossover, and mutation to improve solutions.

Discover machine learning fundamentals, including classification, regression, clustering, and reinforcement learning, with intuition for algorithms like naive Bayes, decision trees, SVM, and linear regression, using Orange 2.

Explore how machine learning and data science fit inside artificial intelligence, covering classification, regression, clustering, and real-world applications from face detection to self-driving cars and streaming recommendations.

Implement a Naïve Bayes classifier in Orange by loading the Zoo dataset, training with an 80/20 split, evaluating with cross-validation, and interpreting the confusion matrix and accuracy.

Load the dataset, configure the kNN algorithm in Orange, and predict animal types from features using a 80/20 train/test split and euclidean distance; compare 92% to 94% accuracy.

Learn how k-fold cross-validation evaluates algorithms by partitioning data into k folds, training on k-1 folds, testing on the remaining fold, and averaging results for robust accuracy.

Investigate wine quality prediction with the wine dataset using multiple algorithms such as SVM, naive Bayes, and decision trees. Apply 80/20 training testing and cross-validation to compare accuracies.

Build a regression model to predict house prices from 21 features (21,613 instances) using a train-test split, removing id and date, and evaluating mean absolute error.

Cluster images in orange by converting pixels to numeric embeddings, measuring euclidean distances, and applying hierarchical clustering to visualize cat and dog image groups.

Explore association rules to uncover data patterns, enabling market basket analysis, promotions, and shelf placement strategies, with examples like beer and diapers and dropout risk.

Apply principal component analysis to reduce the adult dataset to two components, then cluster with K-means and visualize on a scatterplot of the first two components for income classification.

Detect outliers in datasets using scatter plots and unsupervised methods like one-class SVM and local outlier factor, with examples from credit data and fraud detection.

Explore basic charts in orange to visualize the iris dataset, a flower classification task, using distributions, scatter plots, three V's, mosaic displays, and circle-based visualizations.