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Machine Learning: Concepts and Applications
  • Level Professional
  • المدة 38 ساعات hours
  • الطبع بواسطة The University of Chicago
  • Offered by

عن

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. A key feature of this course is that you not only learn how to apply these techniques, you also learn the conceptual basis underlying them so that you understand how they work, why you are doing what you are doing, and what your results mean. The course also features real-world datasets, drawn primarily from the realm of public policy. It is based on an introductory machine learning course offered to graduate students at the University of Chicago and will serve as a strong foundation for deeper and more specialized study.

الوحدات

Course Introduction + Data Manipulation

    • 4 Videos
    • 2 Lab
    • 1 Assignment
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1 Assignment

  • Working with Data

2 Labs

  • Data Basics: Numpy and Pandas
  • Data Exploration with Pandas

4 Videos

  • Course Introduction
  • The Data Science Pipeline
  • Data Ingestion and Exploration
  • Lab Walkthrough: Data Exploration with Pandas

Supervised Learning, Linear Models, and Least Squares

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Introduction to Linear Regression

1 Labs

  • Linear Regression

2 Videos

  • Supervised Learning, Linear Models, and Least Squares
  • Lab Walkthrough: Linear Regression

Least Squares Estimation and Significance

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Linear Regression

1 Labs

  • Linear Regression on the Prostate Cancer Dataset

2 Videos

  • Linear Regression and Least Squares
  • Lab Walkthrough: Linear Regression on the Prostate Cancer Dataset

Maximum Likelihood Estimation

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Maximum Likelihood Estimation

1 Labs

  • Linear Regression and Maximum Likelihood Estimation

2 Videos

  • Maximum Likelihood Estimation
  • Lab Walkthrough: Linear Regression and Maximum Likelihood Estimation

Graded Quiz

    • 1 Programming
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1 Programming

  • Graded Quiz: Manipulating Data & Linear Regressions

Basis Functions

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Polynomial Feature Expansion

1 Labs

  • Features and Basis Functions

2 Videos

  • Basis Functions
  • Lab Walkthrough: Features and Basis Functions

Regularization and Shrinkage

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Regularization

1 Labs

  • Linear Regression: Regularization

2 Videos

  • Regularization and the Bias-Variance Tradeoff
  • Lab Walkthrough: Linear Regression: Regularization

Model Selection and Cross Validation

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Model Tuning and Selection

1 Labs

  • Model Selection and Pipelines

2 Videos

  • Model Selection and Cross Validation
  • Lab Walkthrough: Model Selection and Pipelines

Logistic Regression

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Logistic Regression

1 Labs

  • Logistic Regression

2 Videos

  • Logistic Regression
  • Lab Walkthrough: Logistic Regression

Support Vector Machines

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Classification with SVMs

1 Labs

  • SVMs

2 Videos

  • Support Vector Machines
  • Lab Walkthrough: Support Vector Machines

Naive Bayes Classification

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Naive Bayes Classifiers

1 Labs

  • Naive Bayes Classification Example

2 Videos

  • Naive Bayes Classification
  • Naive Bayes Classification Example

Graded Quiz 2

    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Graded Quiz: Model Evaluation

1 Labs

  • Starter Code for the Quiz

Decision Trees and Ensembles

    • 3 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Trees and Ensembles

1 Labs

  • Trees and Forests

3 Videos

  • Tree-Based Models
  • Ensembles, Bagging, and Boosting
  • Lab Walkthrough: Trees and Forests

Evaluation Metrics

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Evaluating Models

1 Labs

  • Evaluation

2 Videos

  • Evaluation Metrics
  • Lab Walkthrough: Evaluation

Graded Quiz 3

    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Trees and Forests Quiz

1 Labs

  • Starter Code for the Quiz

K-Means and Hierarchical Clustering

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • K-Means and Hierarchical Clustering

1 Labs

  • Clustering

2 Videos

  • Unsupervised Learning (K-Means, Hierarchical)
  • Lab Walkthrough: Clustering

Distribution and Density-Based Clustering

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Clustering II

1 Labs

  • Density and Distribution-Based Clustering

2 Videos

  • Clustering (KDE, Meanshift, DBSCAN)
  • Lab Walkthrough: Density and Distribution-Based Clustering

Dimensionality Reduction

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Principal Component Analysis

1 Labs

  • Principal Component Analysis (PCA)

2 Videos

  • Principal Component Analysis (PCA)
  • Lab Walkthrough: Principal Component Analysis

Hidden Markov Models

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • HMMs

1 Labs

  • Hidden Markov Models on Divvy Bike Trips

2 Videos

  • Temporal Models and Hidden Markov Models
  • Lab Walkthrough: Hidden Markov Models

Feed-Forward Neural Nets

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Neural Networks

1 Labs

  • Feed-forward Neural Nets

2 Videos

  • Feed-Forward Neural Networks
  • Lab Walkthrough: Feed Forward Neural Networks

Convolutional Neural Nets

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Convolutional Neural Nets

1 Labs

  • Convolutional Neural Nets

2 Videos

  • Convolutional Neural Networks
  • Lab Walkthrough: Convolutional Neural Nets

Auto Summary

Dive into "Machine Learning: Concepts and Applications" to master both the theory and practice of machine learning within the Data Science & AI domain. Guided by expert instructors from the University of Chicago, you'll explore Python, Pandas, Scikit-learn, and Tensorflow to handle real-world public policy datasets. Covering various techniques such as linear and logistic regression, SVMs, decision trees, and deep learning, this professional-level course spans 2280 minutes and is available through Coursera's Starter subscription. Ideal for professionals seeking a comprehensive foundation in machine learning.

Dr. Nick Feamster
Instructor

Dr. Nick Feamster