Introduction to AI - Machine Learning and Deep Learning

Refresh statistics basics—mean, median, mode, variance, standard deviation, and outliers—then cover probability distributions, correlation, Bayes’ theorem, calculus for slopes, and matrix algebra.

Explore key function types for machine learning, including linear, quadratic, cubic, and exponential and logarithmic functions, with notes on slope, differentiability, and leaky ReLU lab implementation.

Join the optional video refresher on functions and math to support learners in the introduction to AI, machine learning, and deep learning course.

Learn core data concepts for machine learning, including discrete, continuous, categorical, and time series data, and master data preparation, imputation, normalization, adding or removing features, one-hot encoding, SMOTE, and transformations.

Explore machine learning terminology including observation, attribute, and target attribute, and explain training, validation, and test sets, loss and cost, forward and backward propagation, gradient descent, learning rate, and hyperparameters.

Explore core machine learning and deep learning algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning, with practical examples and intuitive explanations.

Explore regression and its two flavors: linear regression for numeric predictions and logistic regression for classifications, plus multivariate and polynomial extensions, including ordinary least squares and gradient descent in practice.

Explore logistic regression with sklearn, modeling binary outcomes using a four by two matrix and a one by four y vector, fit the model, and interpret coefficients and intercept.

Explore how decision trees solve multi-class classification by splitting on features to maximize information gain. Compare single trees with ensemble approaches like bagging and boosting, including XGBoost.

Train a decision tree on the iris dataset with sklearn, fit the model and predict flower types, shown by a plot that reveals how decisions are made and predicts virginica.

Learn how to apply XGBoost to the iris dataset from Kaggle by importing the XGBoost classifier, splitting the data into train and test, training the model, and predicting iris virginica.

Discover how neural networks work from input to output layers, with weights and activation functions, and explore concepts like CNNs, RNNs, LLMs, GANs, dropout, and softmax.

Train a Keras neural network on the MNIST handwritten digits dataset with 60,000 training and 10,000 test samples, using an input layer and two 512-node dense layers over 10 epochs.

Evaluate model performance after training using RMSE for numeric outputs and confusion matrix metrics: precision, recall, F1, and AUC for classification; apply MAPE for time series.

Explore advanced algorithms from factorization machines and k-nearest neighbors to anomaly detection, word2vec, and sequence2sequence, plus pca, image classification, object detection, and topic modeling with lda.