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

It will force you to install and start R (at the very least).
It will given you a bird’s eye view of how to step through a small project.
It will give you confidence, maybe to go on to your own small projects.

Data Science and Machine Learning
with Python Course Outline

● An Introduction to Python
● Beginning Python Basics
● Python Program Flow
● Functions & Modules
● Exceptions
● File Handling
● Classes In Python
● Regular Expressions
● Data Structures
● Regression analysis
● K-Means Clustering
● Principal Component Analysis
● Train/Test and cross validation
● Bayesian Methods
● Decision Trees and Random Forests
● Multivariate Regression
● Multi-Level Models
● Support Vector Machines
● Reinforcement Learning
● Collaborative Filtering
● K-Nearest Neighbor
● Bias/Variance Tradeoff
● Ensemble Learning
● Term Frequency / Inverse Document Frequency
● Experimental Design and A/B Tests

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