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Data Science: Supervised Machine Learning in Python

A-Z Guide to Implementing Classic Machine Learning Algorithms From Scratch and with Sci-Kit Learn
4.6 (122 ratings)
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2,411 students enrolled
Last updated 12/2016
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  • 3 hours on-demand video
  • Full lifetime access
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  • Certificate of Completion
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In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what's going to drive innovation in the coming years. It's embedded into all sorts of different products.

Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail.

It’s important to know both the advantages and disadvantages of each algorithm we look at.

Next we’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.

We’ll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.

Next we’ll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we’ll study, and most courses you’ll look at won’t implement them. We will, since I believe implementation is good practice.

The last algorithm we’ll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.

One we’ve studied these algorithms, we’ll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.

We’ll do a comparison with deep learning so you understand the pros and cons of each approach.

We’ll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.

We’ll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.

All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.


All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples

In the directory: supervised_class

Make sure you always "git pull" so you have the latest version!

TIPS (for getting through the course):

  • Watch it at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
    • Write down the equations. If you don't, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Realize that most exercises will take you days or weeks to complete.


  • (The Numpy Stack in Python)
  • Linear Regression in Python
  • Logistic Regression in Python
  • (Supervised Machine Learning in Python)
  • (Bayesian Machine Learning in Python: A/B Testing)
  • Deep Learning in Python
  • Practical Deep Learning in Theano and TensorFlow
  • (Supervised Machine Learning in Python 2: Ensemble Methods)
  • Convolutional Neural Networks in Python
  • (Easy NLP)
  • (Cluster Analysis and Unsupervised Machine Learning)
  • Unsupervised Deep Learning
  • (Hidden Markov Models)
  • Recurrent Neural Networks in Python
  • Natural Language Processing with Deep Learning in Python
Who is the target audience?
  • Students and professionals who want to apply machine learning techniques to their datasets
  • Students and professionals who want to apply machine learning techniques to real world problems
  • Anyone who wants to learn classic data science and machine learning algorithms
  • Anyone looking for an introduction to artificial intelligence (AI)
Students Who Viewed This Course Also Viewed
What Will I Learn?
Understand and implement K-Nearest Neighbors in Python
Understand the limitations of KNN
User KNN to solve several binary and multiclass classification problems
Understand and implement Naive Bayes and General Bayes Classifiers in Python
Understand the limitations of Bayes Classifiers
Understand and implement a Decision Tree in Python
Understand and implement the Perceptron in Python
Understand the limitations of the Perceptron
Understand hyperparameters and how to apply cross-validation
Understand the concepts of feature extraction and feature selection
Understand the pros and cons between classic machine learning methods and deep learning
Use Sci-Kit Learn
Implement a machine learning web service
View Curriculum
  • Python, Numpy, and Pandas experience
  • Probability and statistics (Gaussian distribution)
Curriculum For This Course
Expand All 35 Lectures Collapse All 35 Lectures 03:02:11
Introduction and Review
3 Lectures 09:44
K-Nearest Neighbor
5 Lectures 21:13
K-Nearest Neighbor Concepts

KNN in Code with MNIST

KNN for the XOR Problem

KNN for the Donut Problem
Naive Bayes and Bayes Classifiers
7 Lectures 33:25
Naive Bayes

Naive Bayes Handwritten Example

Naive Bayes in Code with MNIST

Non-Naive Bayes

Bayes Classifier in Code with MNIST

Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)

Generative vs Discriminative Models
Decision Trees
5 Lectures 34:06
Decision Tree Basics

Information Entropy

Maximizing Information Gain

Choosing the Best Split

Decision Tree in Code
4 Lectures 19:49
Perceptron Concepts

Perceptron in Code

Perceptron for MNIST and XOR

Perceptron Loss Function
Practical Machine Learning
6 Lectures 31:00
Hyperparameters and Cross-Validation

Feature Extraction and Feature Selection

Comparison to Deep Learning

Multiclass Classification

Sci-Kit Learn

Regression with Sci-Kit Learn is Easy
Building a Machine Learning Web Service
2 Lectures 10:22
Building a Machine Learning Web Service Concepts

Building a Machine Learning Web Service Code
1 Lecture 02:50
What’s Next? Support Vector Machines and Ensemble Methods (e.g. Random Forest)
2 Lectures 19:42
How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn

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About the Instructor
4.6 Average rating
4,997 Reviews
29,647 Students
17 Courses
Data scientist and big data engineer

I am a data scientist, big data engineer, and full stack software engineer.

For my masters thesis I worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons communicate with their family and caregivers.

I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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