Data Science
Machine Learning and Predictions
Heads up! This course may be archived and/or unavailable.
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
Get a Reminder
Rating | Not enough ratings |
---|---|
Length | 6 weeks |
Effort | 4 - 6 hours per week |
Starts | On Demand (Start anytime) |
Cost | $199 |
From | University of California, Berkeley, BerkeleyX via edX |
Instructors | Ani Adhikari, John DeNero, David Wagner |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming Data Science |
Tags | Computer Science Data Analysis & Statistics |
Get a Reminder
Similar Courses
Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
Research Scientist-Machine Learning $55k
Cloud Architect - Azure / Machine Learning $75k
Watson Machine Learning Engineer $81k
Machine Learning Software Developer $103k
Software Engineer (Machine Learning) $116k
Applied Scientist, Machine Learning $130k
Autonomy and Machine Learning Solutions Architect $131k
Applied Scientist - Machine Learning -... $136k
RESEARCH SCIENTIST (MACHINE LEARNING) $147k
Machine Learning Engineer 2 $161k
Machine Learning Scientist Manager $170k
Machine Learning Scientist, Personalization $213k
Write a review
Your opinion matters. Tell us what you think.
Please login to leave a review
Rating | Not enough ratings |
---|---|
Length | 6 weeks |
Effort | 4 - 6 hours per week |
Starts | On Demand (Start anytime) |
Cost | $199 |
From | University of California, Berkeley, BerkeleyX via edX |
Instructors | Ani Adhikari, John DeNero, David Wagner |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming Data Science |
Tags | Computer Science Data Analysis & Statistics |
Similar Courses
Sorted by relevance
Like this course?
Here's what to do next:
- Save this course for later
- Get more details from the course provider
- Enroll in this course