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.
Overview
Syllabus
- 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.
- 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.
- 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.
- 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.
Taught by
Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero and V. G. Vinod Vydiswaran
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Reviews
4.0 rating, based on 6 Class Central reviews
4.6 rating at Coursera based on 8514 ratings
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David ChenI took this course when it just came out. In my opinion, it was a wonderful overview of the basic machine learning concepts, including classification, regression, and clustering. The course made use of python Jupyter Notebook, and back then it was a… -
Slow prof. Make you go sleep.
Could hardly tell where he is going. There is not continuity to his explanations.
Almost makes me think and question if he know the stuff he is talking about. -
Raivis JokstsThis is an interesting course, but is somewhat lacking practical example to illustrate theoretical concepts. Topics like model evaluation and tuning parameters would have benefited if those are explained using real or semi-real life problem examples. Especially the quizzes needed more context as to why a particular situation might occur, and why that particular variable of interest is necessary. The feature selection / engineering aspect was just barely touched upon. -
Interesting course, similar to Andrew Ng's machine learning course, but covers a slightly different spectrum of topics, and skips things like inner workings of gradient descent in order to have more of a focus on practical aspects of sklearn and python.
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this course is well and cover a lot and very practice assignment but for each week cover too much of detail that make us hard to focus with it would better if this course split into something like 6-8 week, and a bit slow if you already know ml
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It's very good course for the student's.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 machi…
