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

Prerequisites

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


Syllabus

Unit 1: Introduction and 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 and Model Selection

Cross-Validation
Bootstrap
Feature Selection
Model Selection and Regularization lab

Unit 4: Classification II

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

Unit 5: Unsupervised Learning

Principal Component Analysis
Kmeans and Hierarchical Clustering
PCA and 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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