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Stanford University

Introduction to Machine Learning Course

Stanford University via Udacity

Overview

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.

Syllabus

  • Welcome to Machine Learning
    • Learn what Machine Learning is and meet Sebastian Thrun!,Find out where Machine Learning is applied in Technology and Science.
  • Naive Bayes
    • 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.
  • Decision Trees
    • 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!
  • Regressions
    • 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.
  • Outliers
    • 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.
  • Clustering
    • 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.
  • Feature Scaling
    • Understand how to preprocess data with feature scaling to improve your algorithms.,Use a min mx scaler in sklearn.

Taught by

Katie Malone and Sebastian Thrun

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Reviews

3.8 rating, based on 21 reviews

Start your review of Introduction to Machine Learning Course

  • 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...
  • Anonymous

    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...
  • Anonymous

    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

    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.
  • Anonymous
    This course is video-based. All lectures are delivered in a good way. However, start this course if you have good listening power.
  • Anonymous

    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
    4

    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.
  • Anonymous
    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

    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.
  • Vinayak Mehta completed this course, spending 10 hours a week on it and found the course difficulty to be medium.

    Nice for a beginner who just wants an intro to machine learning and not delve deeper into the implementation and mathematics behind the algorithms.
  • Hristo Vrigazov completed this course.

    Nice, intuitive introduction for a beginner. It is mostly practical, the math is very shallow so if you are interested in the math behind it, you won't be interested in the course.
  • Anonymous

    Anonymous completed this course.

    The best online course in introductory machine learning. The course is full of interesting quizzes. The instructor is very funny and interesting.
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    2

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    6

    Rafael Prados completed this course.

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