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Foundations of Data Science: Prediction and Machine Learning

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University of California, Berkeley

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English

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Overview

Learn how to use machine learning, with a focus on regression and classification, to automatically identify patterns in your data and make better predictions.

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free

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Type: Online

This course includes

  • 6 Weeks
  • Self-paced on your time
  • Add a Verified Certificate for $199
  • 4–6 hours per week

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Foundations of Data Science: Prediction and Machine Learning

Created by -

University of California, Berkeley

0.00

(0 ratings)

English

Wishlist

Overview

Learn how to use machine learning, with a focus on regression and classification, to automatically identify patterns in your data and make better predictions.

course image

Skillqore is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

free

provider image

Type: Online

This course includes

  • 6 Weeks
  • Self-paced on your time
  • Add a Verified Certificate for $199
  • 4–6 hours per week

Taken this course?

Share your experience with other students

Share

Add Review

Foundations of Data Science: Prediction and Machine Learning

Created by -

University of California, Berkeley

0.00

(0 ratings)

Beginner

Start Date: January 28th 2019

Course Description

One of the principal responsibilities of a data scientist is to make reliable predictions based on data. When the amount of data available is enormous, it helps if some of the analysis can be automated. Machine learning is a way of identifying patterns in data and using them to automatically make predictions or decisions. In this data science course, you will learn basic concepts and elements of machine learning.

The two main methods of machine learning you will focus on are regression and classification. Regression is used when you seek to predict a numerical quantity. Classification is used when you try to predict a category (e.g., given information about a financial transaction, predict whether it is fraudulent or legitimate).

For regression, you will learn how to measure the correlation between two variables and compute a best-fit line for making predictions when the underlying relationship is linear. The course will also teach you how to quantify the uncertainty in your prediction using the bootstrap method. These techniques will be motivated by a wide range of examples.

For classification, you will learn the k-nearest neighbor classification algorithm, learn how to measure the effectiveness of your classifier, and apply it to real-world tasks including medical diagnoses and predicting genres of movies.

The course will highlight the assumptions underlying the techniques, and will provide ways to assess whether those assumptions are good. It will also point out pitfalls that lead to overly optimistic or inaccurate predictions.

What you'll learn

  • Fundamental concepts of machine learning
  • Linear regression, correlation, and the phenomenon of regression to the mean
  • Classification using the k-nearest neighbors algorithm
  • How to compare and evaluate the accuracy of machine learning models
  • Basic probability and Bayes’ theorem

Course Structure

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About the Instructor

Ani Adhikari,John DeNero,David Wagner,University of California, Berkeley

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