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Python Machine Learning

The class will introduce you to a wide range of machine learning tools in Python.

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Video Hours

Course Introduction

Course Overview

The class will introduce you to 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.

Course Goal

Students will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions in Python


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


Luke Lin

Luke holds a PhD in Mathematics at Stony Brook University, specialized in partial differential equations. As a lifelong learner of mathematics, he is extremely efficient in quantitative analysis and also skilled at communicating abstract concepts. With proficiency in R and Python, Luke is primed to be a major asset to any analytic force. Being extremely passionate to share the insight of the data from variety of industries, Luke looks forward to meeting talented students from all kinds of background here in NYC Data Science Academy.

Aiko Liu

Aiko grew up in Taiwan where he studied Mathematics and Physics in college. He then moved to the United States to obtain his PhD in Mathematics at Harvard. After finishing his degree, Aiko conducted research and taught at M.I.T and U.C Berkeley for nine years before moving into the world of finance. He worked in the hedge fund industry on quantitative trading for a decade before diving into Data Science full time. Aiko enjoys programming and using machine learning algorithms for industrial research. When at home he enjoys reading books on a really, really wide variety of topics.


Setting up a Python Environment
Python Machine Learning - Linear Regression
Python Machine Learning - Classification 1
Python Machine Learning - Model Selection
Python Machine Learning - Classification 2
Python Machine Learning - Unsupervised Learning
Kaggle Jump-start: Sberbank Russian Housing Market

Ensemble and Stacking (Advanced Content)
Nonlinear Time Series with XGBoost (Advanced Content)

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Python Machine Learning


The class will introduce you to a wide range of machine learning tools in Python.

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