This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.
제공자:
Machine Learning: Concepts and Applications
시카고대학교이 강좌에 대하여
Basic Python and Linear Algebra
귀하가 습득할 기술
- Artificial Neural Network
- Machine Learning
- Unsupervised Learning
- Statistical Classification
- regression
Basic Python and Linear Algebra
제공자:

시카고대학교
One of the world's premier academic and research institutions, the University of Chicago has driven new ways of thinking since our 1890 founding. Today, UChicago is an intellectual destination that draws inspired scholars to our Hyde Park and international campuses, keeping UChicago at the nexus of ideas that challenge and change the world.
강의 계획표 - 이 강좌에서 배울 내용
Machine Learning and the Machine Learning Pipeline
In this module you will be introduced to the machine-learning pipeline and learn about the initial work on your data that you need to do prior to modeling. You will learn about how to ingest data using Pandas, a standard Python library for data exploration and preparation. Next, we turn to the first approach to modeling that we explore in this class, linear regression with ordinary least squares.
Least Squares and Maximum Likelihood Estimation
In this module, you continue the work that we began in the last with linear regressions. You will learn more about how to evaluate such models and how to select the important features and exclude the ones that are not statistically significant. You will also learn about maximum likelihood estimation, a probabilistic approach to estimating your models.
Basis Functions and Regularization
This module introduces you to basis functions and polynomial expansions in particular, which will allow you to use the same linear regression techniques that we have been studying so far to model non-linear relationships. Then, we learn about the bias-variance tradeoff, a key relationship in machine learning. Methods like polynomial expansion may help you train models that capture the relationship in your training data quite well, but those same models may perform badly on new data. You learn about different regularization methods that can help balance this tradeoff and create models that avoid overfitting.
Model Selection and Logistic Regression
In this module, you first learn more about evaluating and tuning your models. We look at cross validation techniques that will help you get more accurate measurements of your model's performance, and then you see how to use them along with pipelines and GridSearch to tune your models. Finally, we look a the theory and practice of our first technique for classification, logistic regression.
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