Data Science with Python: Machine Learning

Data Science with Python: Machine Learning
Course Overview

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.

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Dates & Time Venue Tuition  
January 22, 2017 - February 26, 2017 1:00-5:00pm Weekends
Day 1: January 22, 2017
Day 2: January 29, 2017
Day 3: February 5, 2017
Day 4: February 12, 2017
Day 5: February 26, 2017
New York
500 8th Ave., Suite 905
New York, NY 10018.0
$1990.00 Add to Cart
March 12, 2017 - April 9, 2017 1:00-5:00pm Weekends
Day 1: March 12, 2017
Day 2: March 19, 2017
Day 3: March 26, 2017
Day 4: April 2, 2017
Day 5: April 9, 2017
New York
500 8th Ave., Suite 905
New York, NY 10018.0
$1990.00 Add to Cart
June 11, 2017 - July 23, 2017 1:00-5:00pm Weekends
Early-Bird Pricing!
Day 1: June 11, 2017
Day 2: June 25, 2017
Day 3: July 9, 2017
Day 4: July 16, 2017
Day 5: July 23, 2017
New York
500 8th Ave., Suite 905
New York, NY 10018.0
$1990.00
$1890.50
Early-Bird Pricing!
Add to Cart
September 17, 2017 - October 22, 2017 1:00-5:00pm Weekends
Early-Bird Pricing!
Day 1: September 17, 2017
Day 2: September 24, 2017
Day 3: October 1, 2017
Day 4: October 15, 2017
Day 5: October 22, 2017
New York
500 8th Ave., Suite 905
New York, NY 10018.0
$1990.00
$1890.50
Early-Bird Pricing!
Add to Cart
Questions? Read our FAQs & Refund Policy
For corporate training or small group training inquiry:
Instructors
Reece Heineke
Reece Heineke
Reece Heineke is Director of Big Data at a fintech startup. His wanderlust has led him near and far, with a stop in the UK where he completed a PhD in Astrophysics at the University of Cambridge before embarking on a career in quantitative finance in Chicago, London and now New York City. He is excited to join the Academy’s team to be able to teach once again. In addition to data and computer science, Reece is passionate about snowboarding, long distance running and bread baking.
Gordon Fleetwood
Gordon Fleetwood
Gordon has a B.A in Pure Mathematics and a M.A. in Applied Mathematics from CUNY Queens College. He worked for two startups, but most of his experience is in academia—the latest being as an Adjunct Mathematics Lecturer. He is currently a Data Analyst at New Classrooms where the goal is to insight into how to improve Personalized Learning. Gordon is equally comfortable with both the Python and R Data Science toolboxes.

Product Description



Details


Overview

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.


Recommended Readings


Preparation – How to set up Python environment

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Testimonials

Barbara Wang
I have been taking classes at NYC data science academy, there is a reason I came back. I learned so much from both of the instructors I had. They really really do care about you and give you a lot of individual attention. You almost can't slack because they will be right there and push you to finish your problem sets. This is something you can't get just taking an on line class. I highly recommend anyone to take this class in person instead of on line.
Sam Brand
I took both Data Analysis and Machine Learning with Python with Vivian. I highly recommend these classes to anyone who wants to take their analytics skills beyond Excel, pivot tables, and averages and into more advanced predictive modeling methods. Luckily, a lot of the work has already been done for us by the developers who created pandas, matplotlib, statsmodels, and scikit-learn. I didn't know anything about these tools prior to taking this class. Vivian makes machine learning easy. At work I can now stand on the shoulders of Python's giants. Pretty cool. Extremely useful.
Yu Ma

I took Machine Learning with Python and Data Analysis with Python in the Spring. I found both course useful and informative. The courses have given me a comprehensive and yet in-depth introduction into Machine Learning and Python. And these skills turn out to be invaluable at work. Most importantly, Vivian is an excellent instructor. She is immensely helpful and supportive which makes the learning process quite enjoyable. Definitely recommend NYC Data Science Academy!

Liz Klobusicky

I took the Data Science with Python: Machine Learning course and I learned a lot. This course helped me to improve my data analysis and general Python skills. It introduced me to several new libraries and algorithms, most of which I plan to use at work. Overall, I had a very positive experience.

Bret Fontecchio

I took Vivian’s Data Science course and had a fantastic experience. I networked with Data professionals from the NBA, the Federal Reserve Bank, NYC startups, and more. I learned a lot very quickly and had a lot of fun. It’s a nice part of the city and the building has a great startup feel to it. …While I was still enrolled I implemented a hierarchical clustering algorithm and put it into production. I wouldn’t have been able to do that if I hadn’t learned Data Science at NYC Data Science Academy.

Christopher Bian

The intermediate python machine learning course was a fascinating time. It gave me a much better feel for the variety of practical techniques that can be used in the field, and I’m frankly really excited to apply what I’ve learned in the near future. Make no mistake, the course and topics are challenging, but your perseverance will be rewarded.

John Maiden

I found Vivian’s Intermediate Python class to be very refreshing, given the formulaic approach that most books I’ve read on Data Science tend to be. She definitely knows her subject, clearly communicates that to her students, and fosters lively debate during class. Can’t wait to see what my fellow students present for their final projects!