Python Machine Learning

Introduce mission learning, its relation to data science, and the types of mission learning, plus how to load data, train, evaluate, and improve the model performance for real-time problems.

Explore the fundamentals of machine learning, contrast with traditional programming, and learn data science basics, statistics, data collection and cleaning, model development and evaluation, supervised and reinforcement learning with Python.

Discover data science fundamentals: statistics, machine learning, artificial intelligence, neural networks, and the data science workflow from data collection to deployment.

Explore machine learning fundamentals, including data exploration and visualization, and learn supervised, unsupervised, and reinforcement learning with key algorithms like k-nearest neighbors, SVM, random forest, and linear regression.

Learn how machine learning solves real-time problems by training models on data, evaluating performance, and applying supervised and unsupervised techniques.

Explore the scikit-learn library in Python to understand supervised and unsupervised learning, and learn classification, regression, and clustering with train/test splits on the iris dataset.

Learn how to collect and prepare data, split into training and validation sets, apply k-nearest neighbors classification, and assess performance via stratified cross-validation with accuracy metrics.

Load and inspect data, visualize distributions, and split into input and output. Train a k-nearest neighbors classifier with stratified cross-validation, assess accuracy, and compare with other learning algorithms.

Explore how scikit-learn algorithms like nearest neighbor, decision tree, logistic regression, and support vector machines handle classification and regression, with cross-validation, training, and evaluating accuracy and confusion metrics.

Evaluate model performance in Python machine learning by applying cross-validation, accuracy metrics, and confusion matrices across classifiers such as logistic regression, linear discriminant analysis, k-nearest neighbors, and support vector machines.

Explore classification performance using confusion matrices, evaluating true/false positives and negatives, and metrics like precision, recall, F1, and accuracy across algorithms on cancer datasets.

Learn the end-to-end machine learning workflow from data collection and processing to evaluating model performance and iterating improvements, with examples touching weather and global warming.