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Instructor is Shu Yan (Ph.D degree in Physics from University of South Carolina).
Sun, Oct 23, 2016 @ 01:00 PM   $1990   NYC Data Science Academy, 500 8th Ave, Ste 905
 
     
 
 
              

  
 
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LOCATION
EVENT DETAILS
This class will introduce you a wide range of machine learning tools in Python. The main focus is on the concepts, methods, and applications of the general predictive modeling and unsupervised learning and how they are implemented in the Python language environment. The goal is to understand how to use these tools to solve real world problems. After this course you will be able to carry out your experiments with the public available algorithms or develop your own algorithm.

Instructor
Shu Yan , Instructor Shu Yan obtained his Ph.D degree in Physics at the University of South Carolina. As a physicist with proficient analytical skills and strong programming background, he brings coding, data science and critical problem solving skills together to tackle real world problems. His physical intuition and mathematical reasoning always bring more insight when thinking about statistical models and machine learning.

Syllabus
This class will introduce you a wide range of machine learning tools in Python. The main focus is on the concepts, methods, and applications of the general predictive modeling and unsupervised learning and how they are implemented in the Python language environment. The goal is to understand how to use these tools to solve real world problems. After this course you will be able to carry out your experiments with the public available algorithms or develop your own algorithm. Specifically, students will:

Be fluent with popular machine learning techniques with the scikit learn module
Be aware of other available machine learning modules
Explain and adopt the machine learning algorithm

Week 1: Introduction
What is Machine Learning
Mathematics review
Linear Regression, Bayesean Classifiers, K-Nearest Neighbors
Numpy/Scikit-Learn lab

Week 2: Regressions and Classification
Multivariate linear regression
Logistic Regression
Linear Discriminant Analysis
Supervised Learning lab

Week 3: Resampling and Model Selection
Cross-Validation
Bootstrap
Feature selection
Model selection and regularization lab

Week 4: Support Vector Machines and Decision Trees
Support Vector Machines
Decision Trees
Forests
Decision Tree and SVM lab

Week 5: Unsupervised Learning
Principal Component Analysis
Clustering with K-Means
State Estimation
PCA and Clustering lab

Intended Audience and Prerequisite
Practitioners who wish to learn how to execute on predictive analytics by way of the Python language; anyone who wants to turn ideas into software, quickly and faithfully. The students who have taken NYC Data Science Academys Data Science with Python: Data Analytics course or for those who already have a firm understanding of Python and are looking to extend those Python skills to machine learning and advanced statistical methods.The goal of this course is to bring the students to near-expert level in this field. Be sure to read the course syllabus below to ensure your level is appropriate.
 
 
 
 
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