In the first part of this machine learning 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.
In the first part of this machine learning 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)
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