À propos de ce cours
4.6
2,266 ratings
432 reviews
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python....
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Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Calendar

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Intermediate Level

Niveau intermédiaire

Clock

Recommandé : 8 hours/week

Approx. 23 heures pour terminer
Comment Dots

English

Sous-titres : English, Korean

Ce que vous allez apprendre

  • Check
    Build features that meet analysis needs
  • Check
    Create and evaluate data clusters
  • Check
    Describe how machine learning is different than descriptive statistics
  • Check
    Explain different approaches for creating predictive models

Compétences que vous acquerrez

Python ProgrammingMachine Learning (ML) AlgorithmsMachine LearningScikit-Learn
Globe

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Calendar

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Intermediate Level

Niveau intermédiaire

Clock

Recommandé : 8 hours/week

Approx. 23 heures pour terminer
Comment Dots

English

Sous-titres : English, Korean

Programme du cours : ce que vous apprendrez dans ce cours

1

Section
Clock
8 heures pour terminer

Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn

This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library....
Reading
6 vidéos (Total 71 min), 4 lectures, 2 quiz
Video6 vidéos
Key Concepts in Machine Learning13 min
Python Tools for Machine Learning4 min
An Example Machine Learning Problem12 min
Examining the Data9 min
K-Nearest Neighbors Classification20 min
Reading4 lectures
Course Syllabus10 min
Help us learn more about you!10 min
Notice for Auditing Learners: Assignment Submission10 min
Zachary Lipton: The Foundations of Algorithmic Bias (optional)30 min
Quiz1 exercice pour s'entraîner
Module 1 Quiz20 min

2

Section
Clock
9 heures pour terminer

Module 2: Supervised Machine Learning - Part 1

This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees. ...
Reading
12 vidéos (Total 166 min), 2 lectures, 2 quiz
Video12 vidéos
Overfitting and Underfitting12 min
Supervised Learning: Datasets4 min
K-Nearest Neighbors: Classification and Regression13 min
Linear Regression: Least-Squares17 min
Linear Regression: Ridge, Lasso, and Polynomial Regression19 min
Logistic Regression12 min
Linear Classifiers: Support Vector Machines13 min
Multi-Class Classification6 min
Kernelized Support Vector Machines18 min
Cross-Validation9 min
Decision Trees19 min
Reading2 lectures
A Few Useful Things to Know about Machine Learning10 min
Ed Yong: Genetic Test for Autism Refuted (optional)10 min
Quiz1 exercice pour s'entraîner
Module 2 Quiz22 min

3

Section
Clock
7 heures pour terminer

Module 3: Evaluation

This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models. ...
Reading
7 vidéos (Total 81 min), 1 lecture, 2 quiz
Video7 vidéos
Confusion Matrices & Basic Evaluation Metrics12 min
Classifier Decision Functions7 min
Precision-recall and ROC curves6 min
Multi-Class Evaluation13 min
Regression Evaluation6 min
Model Selection: Optimizing Classifiers for Different Evaluation Metrics13 min
Reading1 lecture
Practical Guide to Controlled Experiments on the Web (optional)10 min
Quiz1 exercice pour s'entraîner
Module 3 Quiz28 min

4

Section
Clock
10 heures pour terminer

Module 4: Supervised Machine Learning - Part 2

This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it....
Reading
10 vidéos (Total 94 min), 11 lectures, 2 quiz
Video10 vidéos
Random Forests11 min
Gradient Boosted Decision Trees5 min
Neural Networks19 min
Deep Learning (Optional)7 min
Data Leakage11 min
Introduction4 min
Dimensionality Reduction and Manifold Learning9 min
Clustering14 min
Conclusion2 min
Reading11 lectures
Neural Networks Made Easy (optional)10 min
Play with Neural Networks: TensorFlow Playground (optional)10 min
Deep Learning in a Nutshell: Core Concepts (optional)10 min
Assisting Pathologists in Detecting Cancer with Deep Learning (optional)10 min
The Treachery of Leakage (optional)10 min
Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)10 min
Data Leakage Example: The ICML 2013 Whale Challenge (optional)10 min
Rules of Machine Learning: Best Practices for ML Engineering (optional)10 min
How to Use t-SNE Effectively10 min
How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms10 min
Post-course Survey10 min
Quiz1 exercice pour s'entraîner
Module 4 Quiz20 min
4.6
Direction Signs

55%

a commencé une nouvelle carrière après avoir terminé ces cours
Briefcase

83%

a bénéficié d'un avantage concret dans sa carrière grâce à ce cours

Meilleurs avis

par OASep 9th 2017

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses

par FLOct 14th 2017

Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!

Enseignant

Kevyn Collins-Thompson

Associate Professor
School of Information

À propos de University of Michigan

The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future....

À propos de la Spécialisation Applied Data Science with Python

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate....
Applied Data Science with Python

Foire Aux Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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