The Ultimate Beginners Guide to Machine Learning

Explore how classification uses historical training data and features like credit history and income to train an algorithm that predicts loan risk, and extend to image classification and product purchases.

Learn how the naive Bayes algorithm builds a probability table from historical bank data, using credit history, debts, properties, and income to classify risk as high, moderate, or low.

Apply naïve bayes in Orange to load data, visualize attributes from the Zoo dataset, train with an 80/20 split using cross-validation, and evaluate with accuracy and a confusion matrix.

Explore the decision tree for predicting loan risk using income, credit history, debts, and properties, and see how entropy and information gain shape classifications into high, moderate, or low risk.

Test a decision tree on the zoo data set in Orange, train with 80% of data, then evaluate with a confusion matrix and 89% accuracy.

Learn how rule-based learning uses customer data - income and credit history - to generate rules that classify risk as high, moderate, or low, and apply them to customers, with a default high risk.

Load the zoo dataset in Orange, apply the rule induction algorithm with 80% train and 20% test, and evaluate 94% accuracy against naive Bayes and the decision tree algorithm.

Explore how support vector machines classify data with the optimal separating line, maximize margins, and leverage support vectors in this introductory overview.

Explore SVM in Orange to classify animals using the zoo dataset, train with 80% of data, test with 20%, and evaluate accuracy at 93% with a confusion matrix.

Learn how regression predicts numerical outcomes from external factors, distinguishing it from classification by forecasting sales, wind speed, and prices based on x variables.

Explore how linear regression predicts health plan cost from age using the line y = B0 + B1 x, and how training finds B0 and B1.

Load a car price dataset in orange and apply linear regression using features like make and wheelbase. Evaluate with mean absolute error and R2 via cross‑validation and save the model.

Explore clustering, a machine learning technique that groups data to reveal patterns across customers, text documents, products, and social networks, with examples from market segmentation and Netflix recommendations.

Learn how the k-means algorithm clusters data by age and salary, defining three centroids, assigning points by distance, and updating centroids until convergence.

Learn to cluster data with the k-means algorithm in Orange, using the iris data set with sepal and petal measurements to identify three clusters and compare them to known classes.

Explore how association rules reveal patterns in data through market basket analysis, using examples like Walmart shelves beer and diapers to boost sales and guide promotions.

Explore the apriori algorithm for mining association rules in market basket analysis, using support and confidence to identify item pairs like coffee and bread.

Implement the Apriori algorithm in orange to mine frequent itemsets and association rules from a market basket dataset, adjusting minimum support and confidence to discover meaningful rules.

Recap the course content, highlighting classification with Naive Bayes, decision trees, rules, and SVMs; regression, K-means clustering, and apriori algorithm for association rules using the orange visual tool.

Discover the AI Expert Academy online platform, offering full access to courses on machine learning, deep learning, computer vision, natural language processing, and algorithms, with certificates and monthly updates.