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Data Science: Supervised Machine Learning in Python

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Sci-Kit Learn
Instructor:
Lazy Programmer Inc.
15,665 students enrolled
English [Auto] More
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

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.

 

 

Suggested Prerequisites:

  • calculus (for some parts)
  • probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy, Scipy, Matplotlib

 

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.
  • Write code yourself, don’t just sit there and look at my code.

 

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)

 

Introduction and Review

1
Introduction and Outline
2
Review of Important Concepts
3
Where to get the Code and Data
4
How to Succeed in this Course

K-Nearest Neighbor

1
K-Nearest Neighbor Intuition
2
K-Nearest Neighbor Concepts
3
KNN in Code with MNIST
4
When KNN Can Fail
5
KNN for the XOR Problem
6
KNN for the Donut Problem
7
Effect of K
8
KNN Exercise
9
Suggestion Box

Naive Bayes and Bayes Classifiers

1
Bayes Classifier Intuition (Continuous)
2
Bayes Classifier Intuition (Discrete)
3
Naive Bayes
4
Naive Bayes Handwritten Example
5
Naive Bayes in Code with MNIST
6
Non-Naive Bayes
7
Bayes Classifier in Code with MNIST
8
Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)
9
Generative vs Discriminative Models

Decision Trees

1
Decision Tree Intuition
2
Decision Tree Basics
3
Information Entropy
4
Maximizing Information Gain
5
Choosing the Best Split
6
Decision Tree in Code

Perceptrons

1
Perceptron Concepts
2
Perceptron in Code
3
Perceptron for MNIST and XOR
4
Perceptron Loss Function

Practical Machine Learning

1
Hyperparameters and Cross-Validation
2
Feature Extraction and Feature Selection
3
Comparison to Deep Learning
4
Multiclass Classification
5
Sci-Kit Learn
6
Regression with Sci-Kit Learn is Easy

Building a Machine Learning Web Service

1
Building a Machine Learning Web Service Concepts
2
Building a Machine Learning Web Service Code

Conclusion

1
What’s Next? Support Vector Machines and Ensemble Methods (e.g. Random Forest)

Appendix / FAQ

1
What is the Appendix?
2
BONUS: Where to get Udemy coupons and FREE deep learning material
3
Windows-Focused Environment Setup 2018
4
How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn
5
How to Code by Yourself (part 1)
6
How to Code by Yourself (part 2)
7
How to Succeed in this Course (Long Version)
8
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
9
Proof that using Jupyter Notebook is the same as not using it
10
Python 2 vs Python 3
11
What order should I take your courses in? (part 1)
12
What order should I take your courses in? (part 2)
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