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.
Welcome to Machine Learning
Learn what Machine Learning is and meet Sebastian Thrun!,Find out where Machine Learning is applied in Technology and Science.
Use Naive Bayes with scikit learn in python.,Splitting data between training sets and testing sets with scikit learn.,Calculate the posterior probability and the prior probability of simple distributions.
Support Vector Machines
Learn the simple intuition behind Support Vector Machines.,Implement an SVM classifier in SKLearn/scikit-learn.,Identify how to choose the right kernel for your SVM and learn about RBF and Linear Kernels.
Code your own decision tree in python.,Learn the formulas for entropy and information gain and how to calculate them.,Implement a mini project where you identify the authors in a body of emails using a decision tree in Python.
Choose your own Algorithm
Decide how to pick the right Machine Learning Algorithm among K-Means, Adaboost, and Decision Trees.
Datasets and Questions
Apply your Machine Learning knowledge by looking for patterns in the Enron Email Dataset.,You'll be investigating one of the biggest frauds in American history!
Understand how continuous supervised learning is different from discrete learning.,Code a Linear Regression in Python with scikit-learn.,Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions.
Remove outliers to improve the quality of your linear regression predictions.,Apply your learning in a mini project where you remove the residuals on a real dataset and reimplement your regressor.,Apply your same understanding of outliers and residuals on the Enron Email Corpus.
Identify the difference between Unsupervised Learning and Supervised Learning.,Implement K-Means in Python and Scikit Learn to find the center of clusters.,Apply your knowledge on the Enron Finance Data to find clusters in a real dataset.
Understand how to preprocess data with feature scaling to improve your algorithms.,Use a min mx scaler in sklearn.
Gregory J Hamel ( Life Is Study) completed this course and found the course difficulty to be easy.
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...
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 implementing the topics you learned in Python using scikit-learn. The course instructors Katie and Sebastian (the guy who runs Udacity) do a good job explaining the material keeping the course engaging, but they keep things simple. The quizzes, at times, are almost patronizingly easy. 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 will be exposed to several major topics in machine learning and have a basic understanding of how they work. 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. Just be aware that both of these courses (particularly the Caltech course) require a stronger math background.
I give this course 4 out of 5 stars: Very Good.
Anonymous completed this course.
I started this course after having taken the Coursera course of AndrewNg. My goal was to apply the algorithms in Python and to become familiar with Scikit learn. I have completed about 70% of Udacities intro to ML and I have to say I am very disappointed...
I started this course after having taken the Coursera course of AndrewNg. My goal was to apply the algorithms in Python and to become familiar with Scikit learn. I have completed about 70% of Udacities intro to ML and I have to say I am very disappointed about the quality of the course, especially about the quality of the videos and the quizzes. The mathematical level is broken down to high school level, which is good for the intuitive understanding, but in my opinion the level is far too low to learn anything serious, especially when comparing with AndrewNgs course. The same applies for the quizzes. Let me illustrate this with an example. Assume they want you to calculate a*b/(c*d+e*f). Then there would be a quiz to calculate a*b, another quiz to calculate c*d, another quiz to calculate e*f, another quiz to calculate c*d+e*f, and then finally the whole thing. One has to go through 6 videos and 5 quizzes to calculate a simple fraction. The programming assignments are similar in quality. I have to say I didnt finish the course and therefore I can not comment on the final project, which may be more serious. In conclucion, I can not recommend this course to anyone who has a serious interest in learning something about ML. Invest your time better!!
Anonymous is taking this course right now.
It's so cringe-worthy, I couldn't get past the first couple of sections. This is supposed to be a foundation for people wanting to pay to take the data science nanodegree. It's as of they're just not tskkmg it seriously at all. Painful to watch. Having completed and enjoyed the data analyst nanodegree, this has put me off further study with Udacity.
Anonymous completed this course.
The math is sloppy and confusing. It often seems like he can't quite decide what he's asking for the probability of. Even worse, the expressions will suddenly change between slides with no explanation of why. In an attempt to simplify the math, they just muddle it up.
I'm not sure who the intended audience is for this course. It's conceptually too slow for anyone with sufficient background to do the math. Yet the math is almost unrecognizable to anyone who already knows it
Unfortunately, this is a lot of like other Udacity courses, that try too hard to be fun, and fail to be sufficiently substantive.
On a positive note, the Python examples are good.
This course is video-based. All lectures are delivered in a good way. However, start this course if you have good listening power.
Anonymous completed this course.
This is practical course, instructors are nice. If you like python you would love this course. Mathematics is not strong here but this an Intro to Machine learning and they are doing the best they can to expose us not only to machine learning algorithm but sci-kit learn api which keeps you hooked on this course. Once you get the idea of any algorithm you can go deeper into mathematical aspects of it. One of the issue I faced was the problem with quizzes few often they are a little opaque.
Sergej Novik completed this course and found the course difficulty to be easy.
The course will teach you the very basics of sklearn but not much of machine learning. Some core concepts are explained in an easy way. The quizzes are however sometime next to idiotic. It would be better to drop half of them altogether.
I gave it 4 because I did not know neither python nor sklearn and it was useful for me. If you know python then go somewhere else.
hello world this is foobar here - where are you ? i have been waiting only for an year now. Your review helps other learners like you discover great courses. Only review the course if you have taken or started taking this course.
Anonymous completed this course.
I hated how the quiz questions weren't clearly written out (some missing information was said instead of shown visually). This stops you from skimming through the quizzes if you are already familiar with the concepts.