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20-hr course covering all the basic machine learning methods Python modules (esp. Scikit-Learn) for implementing them.
Sunday, June 11, 2017 at 01:00 PM    Cost: $1990
NYC Data Science Academy, 500 8th Ave, Ste 905
 
     
 
 
              

              
 
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LOCATION
 
DESCRIPTION
This 20-hour course covers all the basic machine learning methods & Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple & multiple Linear regressions; classification methods including logistic regression, discriminant analysis & naive bayes, support vector machines (SVMs) & tree based methods; cross-validation & feature selection; regularization; principal component analysis (PCA) & clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms & implement these methods to analyze complex datasets & make predictions.

Prerequisites

Knowledge of Python programming
Able to munge, analyze, & visualize data in Python


Syllabus

Unit 1: Introduction & Regression

What is Machine Learning
Simple Linear Regression
Multiple Linear Regression
Numpy/Scikit-Learn Lab

Unit 2: Classification I

Logistic Regression
Discriminant Analysis
Naive Bayes
Supervised Learning Lab

Unit 3: Resampling & Model Selection

Cross-Validation
Bootstrap
Feature Selection
Model Selection & Regularization lab

Unit 4: Classification II

Support Vector Machines
Decision Trees
Bagging & Random Forests
Decision Tree & SVM Lab

Unit 5: Unsupervised Learning

Principal Component Analysis
Kmeans & Hierarchical Clustering
PCA & Clustering Lab

Final Project
After 20 hours of structured lectures, students are encouraged to work on an exploratory data analysis project based on their own interests. A project presentation demo will be arranged afterwards.
 
 
 
 
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