Data Science with Python: Machine Learning

Data Science with Python: Machine Learning

Data Science with Python: Machine Learning

This 20-hour Machine Learning with Python 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 in Python.

Course Overview

This 20-hour Machine Learning with Python 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 in Python.

* Tuition paid for part-time courses can be applied to the Data Science Bootcamps if admitted within 9 months.
January Session
$1990.00
Early bird pricing
$1890.50
January Session
Jan 19 - Feb 16, 2020, 1:00-5:00pm
Want to start right away?
Check out our online option.
March Session
$1990.00
Early bird pricing
$1890.50
March Session
Mar 7 - Apr 4, 2020, 1:00-5:00pm
April Session
$1990.00
Early bird pricing
$1890.50
April Session
Apr 19 - May 17, 2020, 1:00-5:00pm

Date and Time

January Session Early-bird Pricing!

Jan 19 - Feb 16, 2020, 1:00-5:00pm
Day 1: January 19, 2020
Day 2: January 26, 2020
Day 3: February 2, 2020
Day 4: February 9, 2020
Day 5: February 16, 2020
$1990.00$1890.50
Add to Cart

March Session Early-bird Pricing!

Mar 7 - Apr 4, 2020, 1:00-5:00pm
Day 1: March 7, 2020
Day 2: March 14, 2020
Day 3: March 21, 2020
Day 4: March 28, 2020
Day 5: April 4, 2020
$1990.00$1890.50
Add to Cart

April Session Early-bird Pricing!

Apr 19 - May 17, 2020, 1:00-5:00pm
Day 1: April 19, 2020
Day 2: April 26, 2020
Day 3: May 3, 2020
Day 4: May 10, 2020
Day 5: May 17, 2020
$1990.00$1890.50
Add to Cart

June Session Early-bird Pricing!

Jun 13 - Jul 18, 2020, 1:00-5:00pm
Day 1: June 13, 2020
Day 2: June 20, 2020
Day 3: June 27, 2020
Day 4: July 11, 2020
Day 5: July 18, 2020
$1990.00$1890.50
Add to Cart

Instructors

Michael Charles
Michael Charles

I got my master degree in statistics and economics in 2004 from a renowned French school. Since then, I have worked in different industries as a statistician and a data scientist. I started my career in France in the media industry at Mediametrie and after a few years joined a major French banking group, Credit Agricole S.A. After moving to San Francisco, CA, I worked at Bank of the West Risk Management for five years. My last role was in the airline industry in Sydney, Australia where I managed the modeling and insight team within Virgin Australia frequent flyer program.

I love to discover and mine into big and messy data from diverse sources to make sense of them, discover and unlock meaningful insights for the businesses and other stakeholders. When relevant, I have developed and implemented machine learning models that helped drive the business, enhance customer experience and automate decision making. I also enjoy data visualization and always try to deliver and communicate results and tools in the most appropriate way for diverse audiences.

More recently I have been managing teams of analysts and data scientists, and I love supporting and training team members to grow their technical skills along with coaching them to reach their career goals.

Michael Zhao
Michael Zhao
I am getting my Ph.D. at the MIT Sloan School of Management in Spring 2019. As an aspiring computational social scientist, I take advantage of modern computational tools to discover novel insights from big, messy data, with a focus on answering causal questions. My current research looks at estimating the value that social media creates for online content producers. Prior to my starting my Ph.D., I completed my master's degree in Economics at NYU.

Product Description


Overview

 

This 20-hour Machine Learning with Python 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 in Python.

Details

 


Prerequisites

 

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

Certificate

Certificates are awarded at the end of the program at the satisfactory completion of the course.

Students are evaluated on a pass/fail basis for their performance on the required homework and final project (where applicable). Students who complete 80% of the homework and attend a minimum of 85% of all classes are eligible for the certificate of completion.


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

 

  • An Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
  • Applied Predictive Modeling, by Max Kuhn and Kjell Johnson
  • Machine Learning for Hackers, by Drew Conway, John White

Preparation – How to set up Python environment

[IMPORTANT] In the class we will use Python 3. If you are following this video to set up Python environment, please make sure you download the Python 3.X version starting from 1 min 23 s in the video.

Reviews

There are no reviews yet.

Instructors

Michael Charles
Michael Charles

I got my master degree in statistics and economics in 2004 from a renowned French school. Since then, I have worked in different industries as a statistician and a data scientist. I started my career in France in the media industry at Mediametrie and after a few years joined a major French banking group, Credit Agricole S.A. After moving to San Francisco, CA, I worked at Bank of the West Risk Management for five years. My last role was in the airline industry in Sydney, Australia where I managed the modeling and insight team within Virgin Australia frequent flyer program.

I love to discover and mine into big and messy data from diverse sources to make sense of them, discover and unlock meaningful insights for the businesses and other stakeholders. When relevant, I have developed and implemented machine learning models that helped drive the business, enhance customer experience and automate decision making. I also enjoy data visualization and always try to deliver and communicate results and tools in the most appropriate way for diverse audiences.

More recently I have been managing teams of analysts and data scientists, and I love supporting and training team members to grow their technical skills along with coaching them to reach their career goals.

Michael Zhao
Michael Zhao
I am getting my Ph.D. at the MIT Sloan School of Management in Spring 2019. As an aspiring computational social scientist, I take advantage of modern computational tools to discover novel insights from big, messy data, with a focus on answering causal questions. My current research looks at estimating the value that social media creates for online content producers. Prior to my starting my Ph.D., I completed my master's degree in Economics at NYU.

Product Description


Overview

 

This 20-hour Machine Learning with Python 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 in Python.

Details

 


Prerequisites

 

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

Certificate

Certificates are awarded at the end of the program at the satisfactory completion of the course.

Students are evaluated on a pass/fail basis for their performance on the required homework and final project (where applicable). Students who complete 80% of the homework and attend a minimum of 85% of all classes are eligible for the certificate of completion.


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

 

  • An Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
  • Applied Predictive Modeling, by Max Kuhn and Kjell Johnson
  • Machine Learning for Hackers, by Drew Conway, John White

Preparation – How to set up Python environment

[IMPORTANT] In the class we will use Python 3. If you are following this video to set up Python environment, please make sure you download the Python 3.X version starting from 1 min 23 s in the video.

Reviews

There are no reviews yet.

Testimonials View All Student Testimonials

Barbara Wang
Barbara Wang
Business Intelligence Analyst at
SPS Commerce
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
Sam Brand
Product Growth Manager at
Stack Overflow
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
Yu Ma
Risk Analyst at
Upwork

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
Liz Klobusicky
Senior Manager, Management Science & Integration at
NBCUniversal Media, LLC

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
Bret Fontecchio
Python Developer at
Akamai Technologies

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
Christopher Bian
Cofounder & CTO at
Unlockable

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
John Maiden
Data Scientist, Digital Intelligence at
JP Morgan

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!

Date and Time

January Session Early-bird Pricing!

Jan 19 - Feb 16, 2020, 1:00-5:00pm
Day 1: January 19, 2020
Day 2: January 26, 2020
Day 3: February 2, 2020
Day 4: February 9, 2020
Day 5: February 16, 2020
$1990.00$1890.50
Register before Dec 20th to take advantage of this price!
Add to Cart

March Session Early-bird Pricing!

Mar 7 - Apr 4, 2020, 1:00-5:00pm
Day 1: March 7, 2020
Day 2: March 14, 2020
Day 3: March 21, 2020
Day 4: March 28, 2020
Day 5: April 4, 2020
$1990.00$1890.50
Register before Feb 6th to take advantage of this price!
Add to Cart

April Session Early-bird Pricing!

Apr 19 - May 17, 2020, 1:00-5:00pm
Day 1: April 19, 2020
Day 2: April 26, 2020
Day 3: May 3, 2020
Day 4: May 10, 2020
Day 5: May 17, 2020
$1990.00$1890.50
Register before Mar 20th to take advantage of this price!
Add to Cart

June Session Early-bird Pricing!

Jun 13 - Jul 18, 2020, 1:00-5:00pm
Day 1: June 13, 2020
Day 2: June 20, 2020
Day 3: June 27, 2020
Day 4: July 11, 2020
Day 5: July 18, 2020
$1990.00$1890.50
Register before May 14th to take advantage of this price!
Add to Cart

Online Session

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