Python practicals that cover the core areas of Data Science (eg. models for regression and classification) from several perspectives: conceptual formulation and properties, solution algorithms and their implementation, data visualization for exploratory data analysis and the effective presentation of modelling outputs.
Practical 1: Linear Regression (Scikit-Learn) Defining a model, fitting a model, least squares regression, linear regression, gradient descent, scikit-learnPractical 2: Classification I (Scikit-Learn) Classification, logistic regression, perceptron, multi-class classification, classification performance measuresPractical 3: Cassification II (Scikit-Learn) An overview of other classification techniques (e.g., decision trees, SVMs) and more advanced techniques including ensemble-based models (boosting, bagging, exemplified with AdaBoost and Random Forests)Practical 4: Deep Learning I (TensorFlow) Neural networks, applications in the world, optimization, stochastic gradient descent, backpropagation, learning rates. Introduction to TensorFlow, minimal TensorFlow example, symbolic graphs, training a network, practical tips for deep learningPractical 5: Deep Learning II (TensorFlow) Convolutional networks, RNNs, LSTMs, autoencoders, regularizationPractical 6: Visualization Scales and coordinates, depicting comparisons. Common plotting patterns, including dimension reduction