Applied Machine Learning in Python

4.6
1,550 ratings
323 reviews

Course 3 of 5 in the Applied Data Science with Python Specialization

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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100% 在线课程

立即开始,按照自己的计划学习。
SvgLevelIntermediate

中级

Clock

完成时间大约为20 小时

建议:7 hours/week
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English

字幕:English

您将学到的内容有

  • 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

您将获得的技能

Machine LearningPython ProgrammingPandasData Science
Globe

100% 在线课程

立即开始,按照自己的计划学习。
SvgLevelIntermediate

中级

Clock

完成时间大约为20 小时

建议:7 hours/week
CommentDots

English

字幕:English

Syllabus - What you will learn from this course

1

Section
Clock
8 hours to complete

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....
SvgReading
6 videos (Total 71 min), 4 readings, 2 quizzes
Video6 videos
Key Concepts in Machine Learning13m
Python Tools for Machine Learning4m
An Example Machine Learning Problem12m
Examining the Data9m
K-Nearest Neighbors Classification20m
SvgReading4 readings
Course Syllabus10m
Help us learn more about you!10m
Notice for Auditing Learners: Assignment Submission10m
Zachary Lipton: The Foundations of Algorithmic Bias (optional)30m
Quiz1 practice exercises
Module 1 Quiz20m

2

Section
Clock
9 hours to complete

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. ...
SvgReading
12 videos (Total 166 min), 2 readings, 2 quizzes
Video12 videos
Overfitting and Underfitting12m
Supervised Learning: Datasets4m
K-Nearest Neighbors: Classification and Regression13m
Linear Regression: Least-Squares17m
Linear Regression: Ridge, Lasso, and Polynomial Regression19m
Logistic Regression12m
Linear Classifiers: Support Vector Machines13m
Multi-Class Classification6m
Kernelized Support Vector Machines18m
Cross-Validation9m
Decision Trees19m
SvgReading2 readings
A Few Useful Things to Know about Machine Learning10m
Ed Yong: Genetic Test for Autism Refuted (optional)10m
Quiz1 practice exercises
Module 2 Quiz22m

3

Section
Clock
7 hours to complete

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. ...
SvgReading
7 videos (Total 81 min), 1 reading, 2 quizzes
Video7 videos
Confusion Matrices & Basic Evaluation Metrics12m
Classifier Decision Functions7m
Precision-recall and ROC curves6m
Multi-Class Evaluation13m
Regression Evaluation6m
Model Selection: Optimizing Classifiers for Different Evaluation Metrics13m
SvgReading1 readings
Practical Guide to Controlled Experiments on the Web (optional)10m
Quiz1 practice exercises
Module 3 Quiz28m

4

Section
Clock
10 hours to complete

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....
SvgReading
10 videos (Total 94 min), 11 readings, 2 quizzes
Video10 videos
Random Forests11m
Gradient Boosted Decision Trees5m
Neural Networks19m
Deep Learning (Optional)7m
Data Leakage11m
Introduction4m
Dimensionality Reduction and Manifold Learning9m
Clustering14m
Conclusion2m
SvgReading11 readings
Neural Networks Made Easy (optional)10m
Play with Neural Networks: TensorFlow Playground (optional)10m
Deep Learning in a Nutshell: Core Concepts (optional)10m
Assisting Pathologists in Detecting Cancer with Deep Learning (optional)10m
The Treachery of Leakage (optional)10m
Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)10m
Data Leakage Example: The ICML 2013 Whale Challenge (optional)10m
Rules of Machine Learning: Best Practices for ML Engineering (optional)10m
How to Use t-SNE Effectively10m
How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms10m
Post-course Survey10m
Quiz1 practice exercises
Module 4 Quiz20m
4.6
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55%

started a new career
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83%

got a tangible career benefit from this course

Top Reviews

By 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!!

By 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

Instructor

Avatar

Kevyn Collins-Thompson

Associate Professor

About 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....

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