Product Description

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.

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

Our Alumni Feedback

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.

Sam Brand

I was very happy with the theory, the application, the pace of the class and the amount of homework for a 5 week class (Sundays). Instructor Ryan was available to help us to catch up with questions related to Python or graphics before and after class. To anybody who decides to take this class, I would recommend to do the project. If you choose not to do it, I would suggest that you stay longer in the last class to watch the presentations of your classmates. I learned from doing my own project but I specially found very interesting the presentations of my classmates. Also I think that a previous knowledge of Python is necessary.

Kirsten Schulz

Very solid class with an excellent professor Ryan Courtney. We covered all the bases and the professor was very careful to make sure that everyone was being brought along with the course material but still went out of his way to challenge us. Classic socratic method style of pushing the class. Like most courses it still comes down to what you are willing to put in time and effort wise but it was an excellent guided adventure.

Dylan Dempsey

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.

Barbara Wang

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.

Liz Klobusicky

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.

Christopher Bian

Sam Brand

Kirsten Schulz

Dylan Dempsey

Barbara Wang

Liz Klobusicky

Christopher Bian

Campus Location

500 8th Ave Suite 905, New York, NY 10018

Nearby Subways

1 2 3 34th, Penn Station

A C E 34th, Penn Station

N Q R B D F M 34th, Herald Square

Detailed Directions

Instructor

Mark Martinez

Data Science Instructor

Mark Martinez is a data scientist / data engineer at Jackpocket. He graduated from Harvard in 2014 with a bachelor's degree in Applied Math and Biology and from Princeton with a masters degree in Computer Science, with an emphasis on computer vision. At Princeton he did research on self-driving cars, and worked specifically on how to create virtual environments used to test and train algorithms used for lane detection and driving. He worked as a data scientist with Johnson and Johnson from 2014-2016 and as a software developer with Square from 2018-2020.

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Data Science with Python