Intro to Machine Learning

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6/10 stars
based on  3 reviews
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Course Details

Cost

FREE

Upcoming Schedule

  • On demand

Course Provider

Udacity online courses
Udacity gives students the opportunity to create hands-on projects that can be put into their portfolios and used to demonstrate their skills to future employers. You'll have a personal coach who helps provide feedback on your assignments and projects to assist you in reaching your goals and staying on track in your online classes. Throughout your education experience, you'll be able to track your development, complete in-class projects, have access to interactive exercises and videos and ...
Udacity gives students the opportunity to create hands-on projects that can be put into their portfolios and used to demonstrate their skills to future employers. You'll have a personal coach who helps provide feedback on your assignments and projects to assist you in reaching your goals and staying on track in your online classes. Throughout your education experience, you'll be able to track your development, complete in-class projects, have access to interactive exercises and videos and earn a verified certificate at the end of the course as proof of all that you've learned. You'll be learning from knowledgeable professors across various schools and parts of the globe. Learn about computer science from Dave Evans, an instructor at the University of Virginia, or delve into app development with Samantha Ready, a Developer Evangelist at Salesforce.com.

Provider Subject Specialization
Sciences & Technology
104 reviews

Course Description

Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions. Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions. This is a class that will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms. This course is also a part of our Data Analyst Nanodegree.
Intro to Machine Learning course image
Reviews 6/10 stars
3 Reviews for Intro to Machine Learning

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Greg Hamel profile image
Greg Hamel profile image
8/10 starsCompleted
  • 115 reviews
  • 106 completed
5 years, 7 months ago
Udacity's Intro to Machine Learning is an introduction to data analysis using Python and the sklearn package. The course consists of 15 lessons covering a wide range of machine learning topics including classification algorithms (Naive Bayes, decision trees and SVMs), linear regression, clustering, selecting and transforming features and validation. As a self-paced course, you can take however long you wish on each lesson; some take less than an hour, while others can take several hours depending on how long you work on the mini projects. Intro to Machine Learning requires basic programming and math skills. Each lesson consists of a series of video segments and quizzes introducing a new topic followed by a mini-project that gives you a chance to work with code dealing with the topic at hand. Katie does most of the teaching and her enthusiasm helps keep the course engaging. The quizzes can, at times, seem patronizingly simple. Th... Udacity's Intro to Machine Learning is an introduction to data analysis using Python and the sklearn package. The course consists of 15 lessons covering a wide range of machine learning topics including classification algorithms (Naive Bayes, decision trees and SVMs), linear regression, clustering, selecting and transforming features and validation. As a self-paced course, you can take however long you wish on each lesson; some take less than an hour, while others can take several hours depending on how long you work on the mini projects. Intro to Machine Learning requires basic programming and math skills. Each lesson consists of a series of video segments and quizzes introducing a new topic followed by a mini-project that gives you a chance to work with code dealing with the topic at hand. Katie does most of the teaching and her enthusiasm helps keep the course engaging. The quizzes can, at times, seem patronizingly simple. The mini projects are a bit harder and contribute more to learning, although they occasionally lack adequate guidance and feedback to help students arrive at the expected output. The final project and many of the mini-projects leading up to it, involve detecting persons of interest in the Enron scandal using a data set of emails sent by Enron employees. Interesting real-world data sets are always a plus. Intro to Machine Learning is an accessible first course in machine learning that prioritizes breadth, high level understanding and practical tools over depth and theory. You won't be an expert in any of the topics covered in this course by the time you're done, but you'll have a good foundation to build upon. If you are interested taking a similar course with many interesting mini projects that uses the R programming language, try MIT's Analytics Edge on edX. Coursera's Machine Learning with Andrew Ng is a logical next step to dig deeper into machine learning algorithm design and implementation, while Caltech's Learning from Data on edX is a great course if you are interested in machine learning theory.
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Iyobosa Evbayowieru profile image
Iyobosa Evbayowieru profile image

Iyobosa Evbayowieru

2/10 starsDropped
1 month, 2 weeks ago
I couldn't even get past lesson two. Too many things were happening all at once. In the assignments, it isn't even clear what your are asked to do. The codes were just being dropped, no explanation on how the codes came about. Some codes used there were not even sklearn's, there were from Katie's personal module. For God's sake, it's supposed to be a beginner's course.
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Student profile image

Student

6/10 starsTaking Now
4 years, 1 month ago
I had high hopes for this course but in fact there are books around that provide a much better learning experience. Preprocessing isn't taught on the course, the code is given to us in modules and were built not only by using scikit-learn or other Python libraries but partly by using modules created by Katie. Those are buggy. They cause unnecessary delays and struggle while trying to code, trying to work out what is going on and how to fix it. The forums are little help, I do wonder how many people give up on the course as a result. I presume some of the issues have something to do with continuous Python development that the user-made modules have not kept up with, or perhaps they were never tested properly. Running 'starter.py' is supposed to set you up to prevent issues but it really doesn't. It is a good intro course but that's all that is. I am using it as reference only. It's mainly about understanding concepts behind ... I had high hopes for this course but in fact there are books around that provide a much better learning experience. Preprocessing isn't taught on the course, the code is given to us in modules and were built not only by using scikit-learn or other Python libraries but partly by using modules created by Katie. Those are buggy. They cause unnecessary delays and struggle while trying to code, trying to work out what is going on and how to fix it. The forums are little help, I do wonder how many people give up on the course as a result. I presume some of the issues have something to do with continuous Python development that the user-made modules have not kept up with, or perhaps they were never tested properly. Running 'starter.py' is supposed to set you up to prevent issues but it really doesn't. It is a good intro course but that's all that is. I am using it as reference only. It's mainly about understanding concepts behind various algorithms but doesn't prepare you for tackling a machine learning problem from start to finish. Udacity has shockingly bad forums for students, it's a mess. You have to scroll pages and pages to find whether someone has posted about various issues already. Surely keeping it organised would mean that there wouldn't be so many threads unanswered because people would find them in the first place and you wouldn't have the same questions repeated over and over again. Surprising considering the names that are associated with Udacity. The videos are always very good though, they are edited well, cater for any attention span and I find them very useful even if only used as reference. Perhaps too many quizzes on this course though, they slow things down a bit.
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