From Intermediate To Advanced

Data Science with Python: Machine Learning

A class for computer-literate people with some programming background who wish to become a master of python programing language.

Python- machine learning
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Certificate Awarded
Data Science with Python - Machine Learning
Best Data Science Bootcamp Switchup
4.89 / 5
(317 Reviews)
Best Data Science Bootcamp
5 Years Running
Course Report Rating
4.84 / 5
( 322 Reviews)
Best Data Science Bootcamp
5 Years Running

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.

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

* Tuition paid for part-time courses can be applied to the Data Science Bootcamps if admitted within 9 months



Customer Reviews

I chose NYC Data Science Academy because of its focus on data science applications in both R and Python and because of the quality of the faculty. The program lived up to every expectation. I came from a quantitative background, but the program did an excellent job of providing material so that it was accessible to people with a less quant heavy background, while also allowing people with a quant background to take a deeper dive. I was looking to switch from finance to a data science role, and this program helped me bridge the gap. The program is intense but deeply gratifying.

Tomas Nivon

 I had a Master in Business Analytics before joining NYC Data Science Academy, with a knowledge of programming and data science/machine learning. Though I knew how to make graphs and build models with R and Python, and knew some concepts learned from the online course on EDX and Coursera, this bootcamp was still truly helpful for me. My goal was to explore more deeply the big data techniques including Hadoop and Spark and get a chance to review data science and machine learning stuff in a systemic way. This bootcamp gave me almost everything I desired, with so many unexpected benefits. It was seriously life-changing for me. I achieved something that would otherwise never be possible had I just stuck with online courses. Read on for more detail. All the courses were well-designed. They covered everything I needed in my data science journey. Some might wonder why I chose to spend money on this bootcamp to learn something that seems available online. The reason for me was that I feel my time is quite valuable. 

Shuheng-Li, NYC Data Science Academy
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Reasons to Enroll


Our instructors are consistently highly rated by their students.  They not only know their subject cold, they are experts at teaching you.


Our curriculum is continuously updated to reflect the latest technology trends.


Learn on the latest technology.  When you complete this course, you will have a solid foundation in python and the use of the tools.

Sam Audino

Sam obtained their Bachelor’s Degree in Mathematics from Bard College, while dabbling in some computer science classes along the way. After school, they decided to make a break from traditional academic life, and worked for several years doing carpentry for places like West Elm, and events like Shakespeare in the Park. Eventually they would turn to the field of Data Science, wherein a passionate blend of creative and analytical thinking can lead to some robust outcomes. They’ve worked in industry building models for Fortune 500 companies that ease the hiring process, and allow for interviewees to be more than just their resume. Their interests include reading, sewing, carpentry, painting, understanding systems, and communicating information effectively to others.

Sam Audino, Data Science Bootcamp Instructor

Your Certificate of Completion


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


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.

Certificate of Completion

Data Science with Python - Machine Learning


Simple Linear Regression

Introduction and Regression
Ryan Courtney
NYC Data Science Academy's Instructor, Ryan Courtney, walks through a lecture on simple linear regression.