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Machine learning with Scikit-learn (Udemy.com)

Learn the most important machine learning techniques using the best machine learning library available

3.6
94 reviews

Created by: Francisco Juretig

Last updated March 2017

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Course Description

This course will explain how to use scikit-learn to do advanced machine learning. If you are aiming to work as a professional data scientist, you need to master scikit-learn! It is expected that you have some familiarity with statistics, and python programming. It's not necessary to be an expert, but you should be able to understand what is a Gaussian distribution, code loops and functions in Python, and know the basics of a maximum likelihood estimator. The course will be entirely focused on the python implementation, and the math behind it will be omitted as much as possible. The objective of this course is to provide you with a good understanding of scikit-learn (being able to identify which technique you can use for a particular problem). If you follow this course, you should be able to handle quite well a machine learning interview. Even though in that case you will need to study the math with more detail. We'll start by explaining what is the machine learning problem, methodology and terminology. We'll explain what are the differences between AI, machine learning (ML), statistics, and data mining. Scikit-learn (being a Python library) benefits from Python's spectacular simplicity and power. We'll start by explaining how to install scikit-learn and its dependencies. And then show how can we can use Pandas data in scikit-learn, and also benefit from SciPy and Numpy. We'll then show how to create synthetic data-sets using scikit-learn. We will be able to create data-sets specifically tailored for regression, classification and clustering. In essence, machine learning can be divided into two big groups: supervised and unsupervised learning. In supervised learning we will have an objective variable (which can be continuous or categorical) and we want to use certain features to predict it. Scikit-learn will provide estimators for both classification and regression problems. We will start by discussing the simplest classifier which is "Naive Bayes". We will then see some powerful regression techniques that via a special trick called regularization, will help get much better linear estimators. We will then analyze Support Vector Machines, a powerful technique for both regression and classification. We will then use classification and regression trees to estimate very complex models. We will see how we can combine many of the existing estimators into simpler structures, but more robust for out of sample performance, called "ensemble" methods. In particular random forests, random trees, and boosting methods. These methods are the ones winning most data science competitions nowadays. We will see how we can use all these techniques for online data, image classification, sales data, and more. We also use real datasets from Kaggle such as spam SMS data, house prices in the United States, etc. to teach the student what to expect when working with real data. On the other hand, in unsupervised learning we will have a set of features (but with no outcome or target variable) and we will attempt to learn from that data. Whether it has outliers, whether it can be grouped into groups, whether we can remove some of those features, etcetera. For example we will see k-means which is the simplest algorithm for classifying observations into groups. We will see that sometimes there are better techniques such as DBSCAN. We will then explain how we can use principal components to reduce the dimensionality of a data-set. And we will
use some very powerful scikit-learn functions that learn the density of the data, and are able to classify outliers. I try to keep this course as updated as possible, specially since scikit-learn is constantly being updated. For example, neural networks was added in the latest release. I tried to keep the examples as simple as possible, keeping the amount of observations (samples) and features (variables) as small as possible. In real situations, we will use hundreds of features and thousands of samples, and most of the methods presented here scale really well into those scenarios. I don't want this course to be focused on very realistic examples, because I think it obscures what we are trying to achieve in each example. Nevertheless, some more complex examples will be added as additional exercises. Who this course is for:
  • Students with some analytics/data-science knowledge aiming at being able to comfortable model in scikit-learn
  • Experienced data scientists working in R/SAS/MATLAB, wanting to transition into ML with Python
Read More Beginner Beginner Level product length iconTime: 6.5h

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Instructor Details

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I worked for 7+ years exp as statistical programmer in the industry. Expert in programming, statistics, data science, statistical algorithms. I have wide experience in many programming languages. Regular contributor to the R community, with 3 published packages. I also am expert SAS programmer. Contributor to scientific statistical journals. Latest publication on the Journal of Statistical Software.

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Reviews

3.6

94 ratings on Udemy

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By Nupur Singh

Initutive explanation with real work example

By Pablo

Very poor instructional design. Great for watching copy/paste from right side of screen to left side, and watching a yellow dot move around the screen. The author needs to determine learning objectives, present the objectives, show how to achieve the objectives, and summarize. Author may know Scikit-Learn and Python, but lacks basic instructional design know-how. Suggest author get some help to organize and present his courses. And review other Udacity courses.

By Young Kim

this course lacks depth and explanations. There are many alternative courses on Udemy that would be much better to learn from.

By Sean ODonnell

I am taking a Data Mining course for my MS in computer Science, is this is already a tremendous help to me. It is full of great 'hands on' practice, with great explanations to go along with it.

By Vibhor Sood

Excellent! Very well taught.

I highly recommend. One of the best teachers to learn Machine Learning at Udemy.

The most generous teacher I have ever come across on Udemy!

By Tuuber Tuub

Great course! Would benefit from using Jupyter notebooks for organization, but on the other hand you're forced to pay more attention when no notebooks present so probably better learning outcome.

Recommend this to everyone who wishes to jump-start their machine learning with scikit-learn!

By Vinay Desai

He takes ages to find the right words, which isn't his fault because English isn't his first language, but it's incredibly slow and frustrating for a student. The main problem though is the course itself. This is basically going through the documentation, which you can do yourself. Loads of copy and pasting of basic snippets that you can find on the scikit learn website. I'm massively disappointed because I was really looking forward to this course. I was hoping to have something worth showing to others but it's there's nothing to it. You're just watching a guy copy and paste and chatting without explaining clearly. It's not good enough in my opinion.

By Kepa J. Rodriguez

The videos and exercises give a good introduction to some relevant algorithms and their implementation in SciKit. Although the course is quite basic and further material is needed, it helps to speed up the learning process.

By Paola Barra

he chews while speaking and doesn't go in the deep of the topic. the topics are interesting but dealt with superficially.

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Overall Score : 72 / 100

Machine learning with Scikit-learn
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  • Video Duration: icon 6.5h
  • Price: $54.99
  • Provider: icon Udemy
  • Updated: icon March 2017
  • Level: Beginner Beginner
  • Language: icon English
  • Certificate: icon Yes
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