Course Description

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

Course Objective

• Introduction and Overview of Machine Learning
• Introduction to Python, Pandas, Numpy, Matplotlib, Seaborn and GitHub
• Regression, Logistic Regression: in sklearn and statsmodels
• Feature Extraction and Selection
• Classification, kNN, Cross Validation, Dimensionality Reduction, PCA, t-SNE, GridSearch
• SVM & Kernels, Evaluation, Decision Trees and Random Forests
• Text and Clustering
• Neural Networks
• Participating in Kaggle Competition
• Natural Language Processing
• Hands-On Deep Learning with Keras
• Social Network Analysis
• Final Project