AI and data science courses

Machine learning is easier to understand when you can see every number. These AI and data science courses build the core algorithms cell by cell on small datasets, so you see exactly what a library such as scikit-learn or PyTorch computes when you call fit and predict. Nothing is hidden behind code, and every step is checked as you go.

Machine Learning in Excel splits and scales data, fits a linear regression with the normal equations, runs gradient descent by hand, scores a logistic regression, evaluates a classifier with a confusion matrix, precision, recall and F1, and covers k-nearest neighbours, k-means clustering and a decision-tree split with Gini impurity. Neural Networks by Hand builds a neuron, activation functions, a forward pass, loss functions, one backpropagation step, softmax and early stopping. Recommendation Systems & Text Analytics covers cosine similarity, user- and item-based collaborative filtering, evaluation with MAE, bag of words, TF-IDF and lexicon sentiment. The courses suit analysts and students who want to understand machine learning before they code it, and managers who need to judge model results. For the statistics underneath, Business Statistics & Regression and Probability & Combinatorics are good companions, and Forecasting & Time-Series Analysis covers prediction over time.