Big Data Analytics

In the first part of this Big Data Analytics course, students get started in machine learning by implementing powerful supervised learning algorithms in Python using its allied packages, providing limited theoretical concepts and practical awareness of important learning algorithms.

Students will get hands-on experience with the rich functionality provided by Python and other packages for implementing linear and logistic regressions, decision and regression trees, Naïve Bayes classifiers, dimensionality reduction, support vector machines, and model evaluation and cross-validation.

In the second part of the course, students will develop their advanced modeling techniques through unsupervised learning techniques (e.g., clustering), time-series forecasting, anomaly detection, training with artificial neural networks and deep belief networks, and feature engineering.

Particularly, students will learn clustering to detect patterns and structure within data sets, know how to transform data to make machine learning feasible, know how to enhance data, etc.

The learning outcome of this course is to make students understand data sets from diverse domains and perform all sorts of data analysis, such as descriptive, exploratory, predictive, and inferential, using Python.

Prerequisites:

• Data Mining

• Theoretical knowledge and understanding of the three types of machine learning: supervised, unsupervised, and reinforcement

• Basic knowledge of uni-variate calculus and basic linear algebra concepts

Topics of This Course:

• Introduction to Machine Learning and its role in Data Science

• Math: Overview of Linear Algebra, Euclidean Distance, KNN and K-means

• Explore Classification through KNN and Clustering through K-means

• Linear Classifier: Perceptron, Optimization and Understanding Gradient descent

• Logistic Regression, Regularization, and Feature Normalization: Bias and Variance

• Multi-class classification, SVM, Kernel Methods

• Non-linear classification, Introduction to Decision Trees and Random Forests, Neural Networks and Ensemble Learning

• Natural Language Processing (1)

• Natural Language Processing (2)