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Beginning Machine Learning with scikit-learn

Understanding Fundamental Concepts and Exploring the API for Supervised Learning

Topic: Data
David Mertz, Ph.D.

‘Machine learning’ is simply what we call the algorithmic extraction of knowledge from data. The ability to perform complex analysis of data, moving beyond the basic tools of statistics, has been refined and developed increasingly over the last two decades. Over a similar period, Python has grown to be the premier language for data science, and scikit-learn has grown to be the main toolkit used within Python for general purpose machine learning.

This course introduces a range of fundamental concepts and techniques used throughout machine learning, using scikit-learn as the concrete library and API in which these are illustrated. This first course focuses most heavily on what is called “supervised machine learning” but also introduces a number of concepts required to understand unsupervised learning.

This course is the first class in the Machine Learning with scikit-learn Series by David Mertz, followed by Diving Deeper into Machine Learning with scikit-learn then Advanced Machine Learning with scikit-learn. (Note that at any given time, only a subset of these classes will be scheduled and open for registration.)

What you'll learn-and how you can apply it

  • Data cleanup and examining data's “shape”
  • Classification vs. Regression vs. Clustering
  • The scikit-learn models APIs
  • Evaluation and scoring of models
  • Hyperparameters

This training course is for you because...

  • You are an aspiring or beginning data scientist.
  • You have a comfortable intermediate-level knowledge of Python and a very basic familiarity with statistics and linear algebra.
  • You are a working programmer or student who is motivated to expand your skills to include machine learning with Python.


  • A first course in Python and/or working experience as a programmer
  • College-level basic mathematics

Course Set-up

Students should have a system with Jupyter notebooks installed, a recent version of scikit-learn, along with Pandas, NumPy, and matplotlib, and the general scientific Python tool stack.

Before class:

Before attending this course, please configure the environments you will need. Within the repository, find the file requirements.txt to install software using pip, or the file environment.yml to install software using conda.

This training material is available under a CC BY-NC-SA 4.0 license. You can find it at: https://github.com/DavidMertz/ML-Live-Beginner

Recommended Preparation

Recommended Follow-up

About your instructor

  • David Mertz is a data scientist, trainer, and erstwhile startup CTO, who is currently writing the Addison Wesley title Cleaning Data for Successful Data Science: Doing the other 80% of the work. He created the training program for Anaconda, Inc. He was a Director of the Python Software Foundation for six years and remains chair of a few PSF committees. For nine years, David helped with creating the world's fastest—highly-specialized—supercomputer for performing molecular dynamics.


The timeframes are only estimates and may vary according to how the class is progressing

Lesson 1: What is Machine Learning? (1 hour)

1.1 Difference between "Deep Learning" and other ML techniques

1.2 Overview of techniques used in Machine Learning - 1.2.1 Classification - 1.2.2 Regression - 1.2.3 Clustering - 1.2.4 Dimensionality Reduction - 1.2.5 Feature Engineering - 1.2.6 Feature Selection - 1.2.7 Categorical vs. Ordinal vs. Continuous variables - 1.2.8 One-hot encoding - 1.2.9 Hyperparameters - 1.2.10 Grid Search


Lesson 2: Exploring a data set (30 minutes)

2.1 Looking for anomalies and data integrity problems

2.2 Cleaning data

2.3 Massaging data format to be model-ready

2.4 Choosing features and a target

2.5 Train/test split


Lesson 3: Classification (30 minutes)

3.1 Choosing a model

3.2 Feature importances

3.3 Cut points in a decision tree

3.4 Comparing multiple classifiers


Lesson 4: Regression (30 minutes)

4.1 Sample data sets in scikit-learn

4.2 Linear regressors

4.3 Probabilistic regressors

4.4 Other regressors


Lesson 5: Hyperparameters (30 minutes)

5.1 Understanding hyperparameters

5.2 Manual search of parameter space

5.3 GridsearchCV

5.4 Attributes of grid search and wrapped model